Detecting Cervical Dysplasia with Quantitative Spectroscopic Imaging
by
Condon Lau
S.M., Mechanical Engineering ARCHNES Massachusetts Institute of Technology, 2006
SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MECHANICAL ENGINEERING MASSACHUSETTS INSTITUJTE OF TECHNOLOGY AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUN 16 2009
June, 2009 LIBRARIES
2009 Condon Lau. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or her fter created.
Signature of Author: Department of Mechanical Engineering June, 2009
Certified by: Michael Feld Professor of Physics Thesis Supervisor
Accepted by: reter So Professor of Mechanical and Biological Engineering Chairman, Thesis Committee
Accepted by: 55vid E. Hardt Chairman, Committee on Graduate Students Detecting Cervical Dysplasia with Quantitative Spectroscopic Imaging
by
Condon Lau
Submitted to the Department of Mechanical Engineering on May 6, 2009, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering
Abstract
This thesis extends quantitative spectroscopy, a form of model-based reflectance and fluorescence spectroscopy, from a small area, contact-probe implementation to wide-area quantitative spectroscopic imaging (QSI) for complete coverage of at-risk tissue. QSI uses the scanning virtual probe concept that is critical for model-based spectroscopy and offers spatial resolution advantages over conventional wide-field illumination. We develop a QSI system capable of imaging cervical dysplasia in vivo. Using the QSI system, we conduct a clinical study to train and prospectively evaluate QSI's ability to distinguish high-grade squamous intraepithelial lesions (HSIL) from non-HSILs (less severe conditions) in cervical transformation zone. This is a clinically important distinction because HSIL requires treatment. The results show measuring the per-patient normalized reduced scattering coefficient alone accurately performs the distinction. This is in good agreement with our previous contact-probe study of HSIL. Due to improved accuracy, QSI used as an adjunct to colposcopy can potentially reduce the number of unnecessary biopsies over colposcopy alone. The results also suggest a simplified optical instrument can be used to detect HSIL and this may advance cervical dysplasia detection in developing countries, where cervical cancer mortality is highest.
Thesis Supervisor: Michael Feld
Title: Professor of Physics and Director of the George R. Harrison Spectroscopy Laboratory Acknowledgements
This Ph.D. thesis represents my most significant professional accomplishment to date. It has required a significant amount of time and effort, but I feel the scientific and medical advancements far outweigh the costs. However, none of this would have been possible without the support of my family and colleagues.
I would like to begin by thanking my parents, John and Vivian Lau, my brother, Cornwall
Lau, and my girlfriend, Josephine Yuen, for their patience and support. You have provided me with important physical and emotional support throughout this challenging, but rewarding process.
I would also like to thank my numerous colleagues who have worked hard on this project and provided valuable advice. To Geoffrey O'Donoghue and Dr. Chung-Chieh Yu, thank you very much for your mentorship and help in developing the quantitative spectroscopic imaging system. To Dr. Jelena Mirkovic, Dr. Antonio de las Morenas, and Dr. Elizabeth Stier, thank you very much for your efforts and guidance with the clinical studies. To Dr.
Ramachandra Dasari and Dr. Kamran Badizadegan, thank you very much for your valuable advice and support throughout my research at MIT.
To conclude, I would like to thank my advisor and mentor, Dr. Michael Feld, for his excellent mentorship and support through all phases of this project and my research at MIT.
Your guidance has made me a better scientist and engineer and permitted the important contributions of this thesis. Table of Contents
1 . In tro d u ctio n ...... 1
2. Cervix and cervical cancer ...... 7
3. Im aging technology ...... 35
4. Quantitative spectroscopy ...... 52
5. Probe to im aging ...... 70
6 . C lin ica l stu d y ...... 98
7. Application - sim ple instrum ent ...... 120
8. Conclusion ...... 126
A p p e n d ix A ...... 132
A p p e n d ix B ...... 138
List of figures ...... 142
List o f ta b le s ...... 146 1. Introduction
Cervical cancer is a cancer of the uterine cervix, which is located at the anterior portion of the uterus and is connected to the vagina. In almost all cases, cervical cancer is caused by exposure to the Human Papillomavirus (HPV), which is often transmitted to women during sexual intercourse. Cervical cancer in the United States, a country with an established screening program, results in approximately 4000 deaths annually, which is low compared to other cancers such as breast and lung cancer[1]. With the recent introduction of vaccines targeting
HPV, the mortality rate will likely continue to drop. However, if we consider the world as a whole, cervical cancer is the second most common cancer amongst women with approximately
300 thousand deaths annually. Looking specifically at developing countries, cervical cancer is often the most common cancer amongst women[2]. This disparity occurs because women in developed countries have access to established annual screening programs based on the
Papanicolaou test (Pap smear), which has proven very effective for identifying patients at risk of cervical cancer. Women with a positive Pap smear are referred for further tests with colposcopy, a visual examination of the cervix under magnification to guide biopsy[3]. Biopsy specimens are submitted for histopathological analysis which serves as the final diagnosis.
As with most cancers, the key to successful treatment of cervical cancer is the ability to detect the cancer at its earliest stages, such as dysplasia, prior to invasion. This is where the screening programs established in developing countries have made the most impact. Treatment procedures for cervical dysplasia, such as Loop Electrosurgical Excision Procedure (LEEP), are considerably simpler and more cost effective than treatments for invasive cancer because the
1 disease is isolated in the most superficial tissue layers. Unfortunately, early cancerous changes often involve only a small number of cells and thus, are difficult to detect with current imaging technology, such as a colposcope[3]. As a result, Pap smear and colposcopy are somewhat inaccurate and this often leads to many unnecessary biopsies and possibly undetected regions with dysplasia or invasive cancer[4, 5]. Excessive biopsies and false positive results cause the patient to endure unnecessary physical and mental discomforts and increase the costs of healthcare. False negative results may lead to invasive cancer, significantly more complicated and expensive treatment procedures, and possible death. To compound matters, Pap smear, colposcopy, and histopathological evaluation of biopsy specimens are all subjective procedures that suffer from interobserver variability[6-11]. Therefore, the diagnosis of one expert may differ from that of another and less experienced clinicians may make relatively inaccurate decisions. A technology that can maintain or improve upon the accuracy of Pap smear and colposcopy while providing a more objective, possibly quantitative, diagnosis can significantly improve detection of cervical dysplasia. If this technology is also easy to use and can be manufactured at an affordable price (for developing countries), it can be deployed in developing countries as a screening and diagnosis tool to significantly reduce mortality due to cervical cancer[12].
Vaccines targeting HPV, such as Gardasil (Merck and Co., Inc.), may reduce the future need for screening and diagnostic procedures such as Pap smear and colposcopy. However, there are a number of reasons to believe that vaccination will not reduce this need in the near future and there may still be significant need for screening and diagnostic technology in the long-term. First, Gardasil is only effective against strains of HPV (types 6, 11, 16, 18) responsible
2 for approximately 70% of cervical cancers[13]. Therefore, there will always be a need for screening technology and as cervical dysplasia/cancer becomes less common, young doctors will receive less of the training and exposure necessary to make an accurate diagnosis. This increases the need for accurate and objective dysplasia/cancer detection technology. Second, vaccination is ineffective for immune compromised patients, such as those with acquired immune deficiency syndrome (AIDS). Third, vaccination is most effective if administered to young women prior to sexual activity, less effective for older women, and is not administered to all eligible recipients[14. Since vaccination is also only effective if administered prior to viral infection, many young women today will need Pap smears and colposcopic examinations in the near future. Fourth, the establishment of a primary prevention program to vaccinate all at-risk members of the population requires significant resources and organization. These may not be available in developing countries where cervical cancer is the most prevalent and dangerous.
Quantitative spectroscopic imaging (QSI), the topic of this thesis, is an optical modality that measures tissue structure and biochemistry over a wide area in vivo and can quantitatively diagnose dysplasia and other early stages of cancer in the cervix and other organs of the body[15]. As a result, it has the ability to address many of the challenges facing cervical cancer screening and diagnosis in the developed world and possibly the developing world. Its development and testing will be described in detail in this thesis, which is divided into eight chapters. Chapter 2 will describe the anatomy of the normal cervix and cancerous changes. This chapter will also describe the current clinically used screening and treatment procedures in more detail. Chapter 3 will present existing imaging technology that is used, at research or clinical stages, to detect cervical dysplasia, such as a colposcope and other optical modalities.
3 Chapter 4 will describe the principles of optical and Quantitative spectroscopy (QS) and corresponding studies in the cervix that have proven their effectiveness for detecting high- grade squamous intraepithelial lesions (HSIL) in vivo, an advance stage of cervical dysplasia with approximately 5% risk of progressing to cancer[16]. The findings of these studies establish the framework for clinical studies conducted with QSL. Chapter 5 presents the scientific advances and instrumentation necessary to extend QS from a contact-probe modality to wide-area QSL.
Chapter 6 presents the experimental procedures and findings of a clinical study to evaluate the accuracy of QSI for detecting HSIL in patients referred for colposcopy and LEEP and compares it to that of expert colposcopy. Chapter 7 presents an exciting application that follows from the findings in chapter 6. Chapter 8 reviews the findings of this thesis and presents future research directions that follow from them.
4 References
1. N. C. Institute, (2008), http://www.cancer.gov/cancertopics/types/cervical. 2. W. H. Organization, "Cervical Cancer Screening in Developing Countries," (World Health Organization, Geneva, 2002). 3. D. G. Ferris, J. T. Cox, D. M. O'Connor, V. C. Wright, and J. Foerster, Modern Colposcopy: Textbook and Atlas (Kendall Hunt, 2004). 4. M. Arbyn, R. Sankaranarayanan, R. Muwonge, N. Keita, A. Dolo, C. G. Mbalawa, H. Nouhou, B. Sakande, R. Wesley, T. Somanathan, A. Sharma, S. Shastri, and P. Basu, "Pooled analysis of the accuracy of five cervical cancer screening tests assessed in eleven studies in Africa and India," Int J Cancer 123, 153-160 (2008). 5. S. B. Cantor, M. Cardenas-Turanzas, D. D. Cox, E. N. Atkinson, G. M. Nogueras-Gonzalez, J. R. Beck, M. Follen, and J. L. Benedet, "Accuracy of colposcopy in the diagnostic setting compared with the screening setting," Obstet Gynecol 111, 7-14 (2008). 6. J. Jeronimo, L. S. Massad, P. E. Castle, S. Wacholder, and M. Schiffman, "Interobserver agreement in the evaluation of digitized cervical images," Obstet Gynecol 110, 833-840 (2007). 7. K. M. Ceballos, W. Chapman, D. Daya, J. A. Julian, A. Lytwyn, C. M. McLachlin, and L. Elit, "Reproducibility of the histological diagnosis of cervical dysplasia among pathologists from 4 continents," Int J Gynecol Pathol 27, 101-107 (2008). 8. L. S. Massad, J. Jeronimo, and M. Schiffman, "Interobserver agreement in the assessment of components of colposcopic grading," Obstet Gynecol 111, 1279-1284 (2008). 9. M. Sideri, N. Spolti, L. Spinaci, F. Sanvito, R. Ribaldone, N. Surico, and L. Bucchi, "Interobserver variability of colposcopic interpretations and consistency with final histologic results," J Low Genit Tract Dis 8, 212-216 (2004). 10. A. Simsir, S. Hwang, J. Cangiarella, P. Elgert, P. Levine, M. V. Sheffield, J. Roberson, L. Talley, and D. C. Chhieng, "Glandular cell atypia on Papanicolaou smears: interobserver variability in the diagnosis and prediction of cell of origin," Cancer 99, 323-330 (2003). 11. M. Confortini, F. Carozzi, P. Dalla Palma, B. Ghiringhello, F. Parisio, S. Prandi, G. Ronco, S. Ciatto, and G. Montanari, "Interlaboratory reproducibility of atypical squamous cells of undetermined significance report: a national survey," Cytopathology 14, 263-268 (2003). 12. N. Thekkek, J. Martinez, I. Adewole, C. MacAuley, and M. Follen., "Digital Imaging Aid for Early Detection of Cervical Cancer in Low Resource Settings," in Biomedical Engineering Society 2007 Annual Fall Meeting(Los Angeles, CA, 2007). 13. K. A. Ault, "Human papillomavirus vaccines: an update for gynecologists," Clin Obstet Gynecol 51, 527-532 (2008). 14. P. Begue, R. Henrion, B. Blanc, M. Girard, and H. Sancho-Garnier, "[Vaccination against human papillomavirus. Implementation and efficacy against cervical cancer control]," Bull Acad Natl Med 191, 1805-1816; discussion 1816-1807 (2007). 15. C. C. Yu, C. Lau, G. O'Donoghue, J. Mirkovic, S. McGee, L. Galindo, A. Elackattu, E. Stier, G. Grillone, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Quantitative spectroscopic imaging for non-invasive early cancer detection," Opt Express 16, 16227-16239 (2008). 16. L. V. Xuan, L. T. Giang, P. D. Man, N. Q. Truc, N. M. Quoc, N. S. Trung, V. V. Vu, N. V. Thai, N. Q. Dung, N. V. Thanh, L. P. Thinh, H. T. Diem, B. D. Hien, P. T. B. Van, T. C. Khuong, S. S. Raab,
5 E. J. Suba, and N. C. Hung, "The Cost Effectiveness of Pap smear Screening Services in a Developing Country," (The Viet/American Cervical Cancer Prevention Project, 2000).
6 2. Cervix and cervical cancer
The uterine cervix is part of the female lower genital tract, which also includes the
the uterus to vagina and vulva. It is located at the anterior end of the birth canal and connects the vagina. Figure 2.1 shows the location of the cervix relative to other organs in the female
into the pelvis. The opening of the cervix leading to the endocervical canal and ultimately is uterus is known as the cervical os. In order to visualize or manipulate the cervix, the vagina to usually expanded by a speculum such that there is a straight path for light and surgical tools
traverse.
rectouteri ne pouch uterine (Faltopian) tube,, uterus
ovary,,,_
round tqarnt uteru
vesicouterine pouch
bladder
urethra coccyxy
pubic syrnptysi IN recturn
chtorisl
abia minora labia majora vagna
Figure 2.1: A diagram of the female pelvis showing the location of the cervix relative to other organs. The cervix is approximately 2cm in diameter, but it can expand considerably, along with the vagina and endocervical canal, during child birth. This figure is replicated from Ferris et aL [1]
In this chapter, we will first describe the macroscopic and microscopic anatomy of the
normal cervix. Afterwards, we present the anatomical alterations caused by different degrees
7 of dysplasia and invasive cancer. To conclude, we will detail the current clinical procedures for
screening, diagnosis, and treatment of cervical dysplasia and cancer. Understanding cervical
anatomy and the current clinical practice will help us to design improved imaging technology in
the subsequent chapters.
2.1. Anatomy of the normal cervix
2.1.1. Macroscopic anatomy
As shown in Fig. 2.1, the cervix connects the endocervical canal with the vagina and as a
result, has a mixture of the anatomical features of both organs. Figure 2.2 is a white-light
photograph of the adult cervix acquired with a commercial digital colposcope (Coopersurgical,
Inc.) under magnification. This cervix is representative of that of patients at risk of cervical
dysplasia and cancer. The slit-shaped opening at the center of the cervix is the cervical os. In
women who have not given birth, the os is circular. Immediately surrounding the os is the red
color endocervix, which consists of columnar tissue similar to that found in the endocervical
canal. In older women, the endocervix may be sufficiently receded within the cervical os such
that it is not visible during a colposcopic examination without manual manipulation of the
cervix[2]. At the perimeter of the cervix is the light pink color ectocervix, which consists of
squamous tissue. The dark color tissue approximately orthogonal to the ectocervix is the
vagina, which is also made of squamous tissue. Between the ectocervix and endocervix is a
region of progressively changing tissue type known as the transformation zone. Here, columnar tissue is gradually transformed into squamous tissue by the process of squamous metaplasia.
The boundary between the endocervix and the transformation zone is called the
8 squamocolumnar junction and is the origin of the vast majority of cervical dysplasia[1]. It is important to note that the boundaries between different parts of the cervix are usually not perfect circles, but rather are often jagged contours with islands of squamous metaplasia.
Figure 2.2: White-light photograph of the cervix acquired by a digital colposcope (CooperSurgical, Inc.) after the application of 5% acetic acid. A speculum, the shadow of which can be seen at the top of the figure, has been put in place to open the vagina and permit visualization.
The three regions of the cervix have different innervations. Sensory nerves approach the cervical epithelium from the endocervical canal and stroma. As a result, the endocervix is well innervated[1]. However, the transformation zone and ectocervix are poorly innervated in comparison and consequently, these parts of the cervix are less sensitive to pain. This distribution of sensory nerves has significant impact on clinical practice as procedures such as biopsy of ectocervix and transformation zone tissue are relatively painless. In contrast,
9 endocervical curettage and loop electrosurgical excision procedure (LEEP), two procedures that affect the endocervix, can be painful and often require local anesthesia.
2.1.2. Microscopic anatomy
In the previous section we showed the normal cervix consists of 3 tissue types: squamous, columnar, and a transitional tissue type resulting from squamous metaplasia.
Squamous and columnar epithelia have significantly different microscopic architectures that affect the appearance and function of the cervix. In this section we will describe the histology of the squamous and columnar epithelia present in the cervix along with that of the hybrid epithelium of the transformation zone.
The endocervical canal and the endocervix are lined by deep red columnar epithelium.
Columnar epithelium consists of a single layer of columnar cells on top of the cervical stroma.
Figure 2.3(a) shows the monolayer of elongated columnar cells at the surface of the tissue.
Notice the dark staining nuclei are positioned at the bottom of the cells just above the stroma.
Many of the columnar cells secrete mucous and are responsible for the secretions often observed during colposcopy[1]. Mucous helps to lubricate the cervix and vagina. The endocervical stroma consists largely of fibrous connective tissue with columnar cells extending several millimeters deep from the tissue surface. These cells in the stroma align to form crypts, which are commonly referred to as endocervical glands. Figure 2.3(b) shows endocervical stroma with glands lined by columnar cells. In the endocervical stroma, small capillaries can be found very close to the epithelium. The single layer epithelium and proximity of capillaries are
10 responsible for the red color of endocervix observed during colposcopy because light traverses a relatively thin layer of tissue to and from blood vessels[3, 4].
(a) (b)
Figure 2.3: Hematoxylin and eosin (H&E) stained sections of the endocervix at (a) high and (b) low magnification. The black arrow in (b) points to an endocervical gland. Cervical stroma, which is rich in collagen, surrounds the glands. This figure is replicated from Ferris et ai. [1]
The ectocervix and vagina are lined by stratified squamous epithelium, which has a light pink color on the cervix. Squamous epithelium, unlike its columnar counterpart, consists of many layers of cells on top of the cervical stroma. This extra epithelial thickness protects the cervix from the acidic environment of the vagina. Figure 2.4 shows the epithelium and underlying stroma. The navicular cell, or intermediate, layer, comprises most of the epithelium.
These cells have glycogen rich cytoplasm that appear clear in a stained section[1]. The bottom of the epithelium is the basal cell layer and this layer is only several cells thick. Basal cells have large nuclei and are responsible for differentiating into superficial epithelial cells. Directly beneath the basal cells and separating them from the ectocervical stroma is the basement membrane. The ectocervical stroma, like its endocervical counterpart, consists primarily of
11 fibrous connective tissue made of collagen. However, ectocervical stroma has far fewer, and
smaller, gland structures and capillaries are located deeper down. This structure of squamous
tissue is responsible for its light pink color observed during colposcopy. Light must traverse
more tissue to reach the blood vessels and as a result, more blue and green light return to the
surface[3, 4].
Navicular te IayI'
Stroina
Basal cell layer
Figure 2.4 H&E stained section of stratified squamous epithelium obtained under magnification showing clear distinction between the epithelium and the underlying stroma. A basal cell layer is clearly visible.
The transformation zone is the region of the cervix between the endlocervix and
ectocervix that is undergoing cervical squamous metaplasia. As a result, its microscopic architecture has features of both squamous and columnar tissues. In the process of squamous metaplasia, cube-shaped reserve cells appear beneath the monolayer of columnar epithelial
12 cells. These can be seen in Fig. 2.5(a). Over time, the reserve cells differentiate into layers of immature squamous cells and these lift the original columnar cells away from their blood supply in the stroma. As a result, the columnar cells disappear and the immature squamous cells slowly mature into the stratified squamous epithelial cells in Fig. 2.4. This is visually characterized by an increase in the cytoplasm to nucleus volume ratio. Figure 2.5(b) shows a region of squamous metaplasia with epithelial cells that begin to resemble stratified squamous epithelium. Notice the beginnings of a basal cell layer. Glandular structures in the endocervical stroma undergo a similar process and eventually merge with the newly forming squamous epithelium. In general, transformation zone has diverse appearances. For example, in about
40% of cervices, there is an abrupt transition from columnar to squamous tissue with no region undergoing squamous metaplasia[1].
(a) (b)
Figure 2.5: (a) H&E stained section at the squamocolumnar junction showing the appearance of reserve cells (arrows) capable of differentiating into columnar and squamous cells. (b) Fully developed squamous metaplasia shown by the presence of mature squamous mucosa overlying endocervical glands. This figure is replicated from Ferris et al. [1]
2.2. Anatomy of the dysplastic/cancerous cervix
13 We have just reviewed the macroscopic and microscopic anatomy of the normal uterine cervix. In this section, we will describe the common anatomical alterations present in the abnormal cervix, particularly those with dysplasia or invasive cancer. Prior to the discussion on anatomy, we briefly describe the crucial role of HPV infection in cervical cancer.
2.2.1. The role of the Human Papillomavirus in cervical cancer
Human Papillomavirus (HPV) infection is responsible for almost all high-grade dysplasia and cervical cancers[5]. There are approximately 40 types of HPV that infect the genital tract.
Fifteen of these present a high-risk of causing cervical cancer and twelve have a low-risk of causing cervical cancer, but can also cause other cervical abnormalities[6]. HPV 16 and 18, two of the high-risk types targeted by the Gardasil (Merck and Co., Inc.) and Cervarix
(GlaxoSmithKline plc.) vaccines, are responsible for approximately 70% of cervical cancers[7].
High-risk HPVs have the ability to immortalize cells, which allows for the uninhibited growth necessary for cancer. Multiple infections by high-risk HPV increases the risk of developing cervical dysplasia[8]. The current U.S. Food and Drug Administration (FDA) approved clinical test for high-risk types of HPV is the digene HPV test (Qiagen)[9]. It is important to note that
HPV infection, even with a high-risk type, does not ensure cervical cancer. For example, HPV 16 is detected in 30% of women with low-grade dysplasias and 10% of women with no detected abnormalities[1].
2.2.2. Macroscopic anatomy
We previously described the macroscopic appearance of the normal cervix and a photograph is presented in Fig. 2.2. In this section we describe the typical anatomy of a
14 dysplastic/cancerous cervix, particularly the features clinicians observe during colposcopy. The procedures involved in a colposcopic examination will be described later in this chapter.
The abnormal cervix is characterized by changes in color, vasculature, and topography.
These changes can be benign or they can indicate various degrees of dysplasia and invasive cancer. Some of the changes can only be observed after the application of acetic acid. In this thesis, we will denote various stages of squamous cell dysplasia in the transformation zone using the Bethesda system[10]. Under the Bethesda system, dysplastic lesions are either low- grade squamous intraepithelial lesions (LSIL) or high-grade squamous intraepithelial lesions
(HSIL).
Color: There are several types of color changes in the abnormal cervix, some of which can only
be observed after the application of acetic acid. Leukoplakia, a whitening and thickening
of the cervical epithelium, is occasionally observed in patients undergoing colposcopy.
Leukoplakia is a clinical description that is usually associated with hyperkeratosis, which
is a benign increase in keratin, and can be observed as long as the cervix is clear of blood
and mucous. However, leukoplakia can mask dysplastic and cancerous changes in the
underlying epithelium. After the application of low concentration acetic acid, typically
3% - 5%, some parts of the cervix may turn a transient white. This phenomenon is
acetowhitening and can occur in normal squamous metaplasia, inflammation, and
dysplastic/cancerous epithelium[11]. Acetic acid is a vasoconstrictor and the exact
etiology of acetowhitening is under investigation. The reflectivity, or "whiteness", of the
epithelium has been correlated with the severity of dysplasia (LSIL or HSIL)[12].
Acetowhitening is the most common abnormality observed during colposcopy.
15 However, it is important to note that acetowhitening, like leukoplakia, is not a 100%
accurate indicator of dysplasia/cancer and determining the "whiteness" of tissue is a
subjective judgment that can vary from clinician to clinician. Also, whitening varies with
age, time from application of acetic acid, and concentration of acid.
Vasculature: Dysplastic and cancerous regions of the cervix can be characterized by
macroscopically visible changes in vasculature. There are three general vascular
abnormalities observed during colposcopy: mosaicism, punctation, and atypical vessels.
A mosaic pattern develops when stromal capillaries branch parallel to the tissue surface.
The thin red color capillaries form a tile pattern on the epithelium. In general, the
severity of dysplasia is correlated with the diameter of the vessels, the consistency of
vessel diameter, and the size of the tiles. However, mosaicism is not a 100% accurate
indicator of dysplasia or cancer. Punctations are caused by stromal capillaries that align
orthogonal to the epithelium. This results in red dots on the tissue surface that can be
observed during colposcopy. Similar to mosaicism, punctuations tend to indicate more
severe dysplasia if the capillaries are larger and further spaced apart, but are also not a
100% accurate indicator of dysplasia/cancer. Atypical vessels are enlarged capillaries
that grow and branch randomly in all directions, unlike normal vessels, which branch in
patterns and tend to taper in diameter. During colposcopy, they appear as large red
vessels aligned parallel to the tissue surface. Atypical vessels are often associated with
invasive cancer. They grow in response to endothelial growth factors secreted by
cancerous cells.
16 The picture of the cervix in Fig. 2.6 was taken with a digital colposcope after the application of 5% acetic acid. Several regions of the cervical epithelium in the transformation zone show acetowhitening. In the lower right corner of the photograph is a Mosaic pattern caused by abnormal vasculature. All of these regions were determined to be HSIL by subsequent histopathological analysis. Acetowhitening and Mosaicism are the two most common macroscopic cervical abnormalities associated with dysplasia that can be observed during colposcopy.
Figure 2.6: White-light photograph of a cervix with extensive acetowhite regions. 5% acetic acid had been applied to the cervix. Histopathology obtained after the colposcopic examination showed the patient had HSIL. topography: The surface of the normal cervix is smooth, as in the cervix of Fig. 2.2. However, in
an abnormal cervix, there may be troughs (ulcerations) and ridges (elevations).
17 Ulcerations can be caused by a number of factors, including trauma induced by foreign
objects inserted into the vagina, viral infection, and cancer. During colposcopy, they
appear as slightly depressed red areas often due to erosion of the epithelium. In
cancers, ulcerations may arise at the surface of tumors where rapid growth results in an
inadequate blood flow and tissue necrosis. Similar to ulcerations, elevations can be due
to a number of factors, including trauma, infection, and cancer. However, elevations
observed during colposcopy almost always indicate some condition, cancer or non-
cancer, which requires treatment. In the case of cancer, uninhibited cell growth results
in tumors that rise above the normally flat cervical epithelium.
2.2.3. Microscopic anatomy
We previously described the microscopic appearance of the normal cervix. In this section we describe the histology of different degrees of cervical dysplasia and cancer as observed during the histopathological evaluation of biopsy and cytology specimens.
Cervical dysplasia/cancer can occur in the glands of the transformation zone and endocervix and the squamous epithelium of the transformation zone. We will refer to those of glandular orgin as glandular dysplasia/cancer and those of squamous origin as squamous cell dysplasia/cancer. Our clinical studies in chapters 6 and 7 will evaluate QSI's ability to detect high-grade squamous cell dysplasia, which is the same as HSIL.
Glandular dysplasia/cancer: Glandular abnormalities can arise throughout the endocervical
canal, although the majority of dysplasia/cancers occur in the transformation zone.
Adenocarcinoma in situ (dysplasia) and invasive adenocarcinoma (cancer) can also be
18 multifocal, which means they occur at multiple sites in the cervix. This contrasts with squamous cell dysplasia/cancers, which occur in one site[l], although the size of that site may be large. In adenocarcinoma in situ, the stromal glands have normal size and shape, but the columnar cells lining the glands have unusually high nuclei:cytoplasm volume ratio and their nuclei vary considerably in size and shape. Nucleoli are difficult to observe after Hematoxylin and Eosin (H&E) staining. Also, the epithelial cells form layers
(columnar epithelium is normally a monolayer) and signs of mitosis can be readily observed. They key feature of adenocarcinoma in situ is the basement membrane remains intact. Figure 2.7 shows an H&E stained section of abnormal glandular cells typical of adenocarcinoma in situ. In invasive adenocarcinoma, the abnormal epithelial cells penetrate the basement membrane and infiltrate the surrounding stroma. These cells also have enlarged and non-uniform nuclei and signs of mitosis can be observed.
However, nucleoli are now more readily seen after H&E staining. Invasive adenocarcinoma is dangerous because the abnormal cells are considerably closer to the lymphatic and blood vessels that serve as roadways for metastasis. The likelihood of adenocarcinoma in situ developing into invasive adenocarcinoma and metastatic cancer is not as well known as compared to that of squamous cell dysplasia[l].
19 44.4444.4~444~4~44...44444444. . '4, 44~44. ,**~ 444~4444<.44~4.4~.4...*444>~.44444. 4444~4~*.~444~, 444*4... 44 ~ 4~ 44..*.4.4~4~.*,4444~Y444444*..4 j .A~#44~ 444~ 44 .4444. .4** - 4 444 ~44 ~j4*.f 444~444 444,4~ .44.4 ~44~;44 ~ . 44~4 ! 44 p V
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Figure 2.7: H&E stained section of adenocarcinoma in situ observed under magnification. The black arrows indicate mitotic cells. This figure is replicated from Ferris et al. [1]
Squamous cell dysplasia/cancer: Squamous cell dysplasia/cancer arises in the squamous
epithelium of the cervical transformation zone. According to the Bethesda System,
squamous cell dysplasia can be divided into two classes, low-grade squamous
intraepithelial lesions (LSIL) and high-grade squamous intraepithelial lesions (HSIL)[10].
LSIL is equivalent to cervical intraepithelial neoplasia grade 1 (CIN 1) while HSIL covers
CIN 2 and 3. The CIN convention is used by the World Health Organization[13]. In LSIL,
basal cells normally restricted to several layers above the basement membrane
proliferate to occupy up to 1/3 the thickness of the epithelium. The remaining 2/3 is
filled with koilocytes, which are altered squamous cells with HPV infested nuclei. These
cells have nuclei up to 3 times as large as normal squamous cells and nucleoli are
difficult to observe after H&E staining. The nuclei also have non-circular boundaries and
20 cells may have multiple nuclei. The cytoplasm of koilocytes accumulate near the cell membrane, causing the cell to appear hollow (except for the nuclei) under microscope examination [14]. Figure 2.8(a) shows a H&E stained section of LSIL with visible koilocytes. In HSIL, basal cells proliferate to more than 1/3 of the epithelium and koilocytes can be found in the remaining depth. The proliferating basal cells (dysplastic cells) have enlarged and irregularly shaped nuclei. Their nucleoli are difficult to observe under H&E staining and mitotic cells can be seen amongst them. A key feature that distinguishes squamous intraepithelial lesions (SIL) from cancer is that the basement membrane is not penetrated by dysplastic cells. Figure 2.8(b) shows a H&E stained section of HSIL where dysplastic cells occupy most of the epithelium. The basement membrane is still intact. It is important to note that HSIL has a greater likelihood than
LSIL of progressing to invasive carcinoma (cancer) and metastasis.
21 (a) (b)
Figure 2.8: H&E stained sections of (a) LSIL and (b) HSIL observed under magnification. (a) The triangles point to koilocytes and the arrow points to a mitotic cell. (b) The arrows indicate the intact basement membrane.
Invasive carcinoma involves abnormal squamous cells penetrating the basement
membrane and infiltrating the underlying stroma. Squamous cancer cells are larger than
dysplastic cells and have larger nuclei and visible nucleoli (after H&E staining). Similar to
invasive adenocarcinoma, invasive carcinoma is dangerous because cancerous cells are
closer to lymphatic and blood vessels that can enable metastasis to other body organs.
2.3. Cervical cancer screening, diagnosis, and treatment
In this chapter we have reviewed the macroscopic and microscopic anatomy of the normal and abnormal cervix. This upcoming section will describe the screening, diagnosis, and treatment procedures used to manage cervical cancer in the United States and most developed countries. We will also overview established patient management guidelines which direct the use of screening, diagnosis, and treatment procedures.
2.3.1. Cytologic screening
22 Screening refers to the examination of patients for the presence of a disease when they
have no symptoms of that disease. For examples, women are routinely screened for cervical
and breast cancer. In the cervix, the primary screening tool is the Papanicolaou smear test (Pap
smear) and its liquid-based variant. The Pap smear is named after its inventor, George
Papanicolaou[15], and involves removing epithelial cells from the cervix using a spatula and
"smearing" them onto a microscope slide. The slide is then examined by a cytopathologist with
a light microscope to find abnormal cells characteristic of dysplasia and cancer. These cell
characteristics were discussed earlier in this chapter. According to the Bethesda System, the
cytopathologist will provide a result of either negative or positive for abnormal cells. If positive,
the cytopathologist can further specify the abnormality[16]. The positive categories are listed in
Table 2.1. The most commonly returned categories are ASCUS and ASCH.
Table 2.1: Bethesda System categories for positive cytology results.
Squamous cell abnormalities 0 Atypical squamous cells of undetermined significance (ASCUS) * Atypical squamous cells cannot exclude HSIL (ASCH) [SIL L * HSIL * Invasive carcinoma Glandular cell abnormalities 0 Atypical endocervical cells * Atypical endometrial cells * Atypical glandular cells * Atypical endocervical cells, favor neoplastic * Atypical glandular cells, favor neoplastic * Endocervical adenocarcinoma in situ (AIS) Invasive adenocarcinoma
Liquid-based cytology, such as ThinPrep (Cytic Corp.) and SurePath (Becton, Dickinson
and Co.), follows the same concept except centrifugation is used to remove non-cell material,
23 which results in more consistent slides. Studies have shown the Pap smear has a relatively low sensitivity, approximately 51%, and high specificity for detecting cervical dysplasia[17]. These findings show the Pap smear is a suitable screening tool because it refers a portion of the population for further testing. It is important to note that cytology is a subjective decision of the cytopathologist (recently, automated slide reading technologies have emerged[18]), so results are subject to interobserver variability.
2.3.2. Human Papillomavirus test
Since it is now accepted that HPV infection is a necessary event for cervical cancer development[5], HPV testing is becoming an integral part of cervical cancer screening. The current FDA approved clinical test for HPV is a solution hybridization test called Hybrid Capture
2 (HC2), made by Digene Corporation. HC2 is a test that uses cells from the cervix, which can be obtained from the Pap smear, and places them in a solution of RNA probes complementary to the DNA of high-risk HPV types. If any high-risk HPV is present in the cells, the relevant RNA probe will hybridize with the DNA of the HPV type. The amounts of resulting molecules are detected by luminescence measurements[1]. If the luminescence exceeds a certain intensity threshold, the test returns positive for high-risk HPV.
HPV testing has been evaluated in three clinical scenarios: (1) Adjunct to Pap smear during screening, (2) Secondary triage after cytology, and (3) Follow-up after treatment.
(1) In the screening scenario, HPV testing has demonstrated a very high sensitivity, approaching
100%[19, 20], for detecting HSIL and cancer. In the future, HPV testing may be the primary
screening tool for cervical cancer and Pap smear becomes a secondary triage tool to reduce
the number of false positives.
24 (2) Currently, Pap smear alone refers a large number of patients for further procedures due to
its low sensitivity (51%)[17]. Since HPV testing has high sensitivity, it can be used after a
positive Pap smear result (secondary triage) to reduce the false positive rate[21]. If the HPV
test is negative, a patient does not require further procedures.
(3) The majority of women who receive successful treatment for SIL become HPV negative[1].
Since HPV testing has a high sensitivity, it can be used to identify women who did not
receive successful treatment[22]. HPV testing is now a standard follow-up procedure after
treatment for cervical dysplasia[23].
2.3.3. Colposcopy
Colposcopy is a visual examination of the cervix using a magnification instrument
(colposcope) and contrast agents such as acetic acid. It is usually employed as a diagnostic test
(for patients with a positive screening test) to determine the presence/absence and severity of disease. A colposcope contains magnification optics and light sources that illuminate the cervix with white light and permit observation of tissue color and texture. Some colposcopes contain color filters that allow the user to view the cervix at one color, usually green, to highlight blood vessels. Many colposcopes today also have digital image capture capabilities, similar to a digital camera, that permit visualization of the cervix on a computer monitor and storing images in computer memory. Figure 2.9 shows the appearance of a typical colposcope. The QSI system, to be discussed later, has all the basic functions of a digital colposcope.
25 Eyepieces
Objective
Magnification Illumination adjustment
Figure 2.9: A typical colposcope with key components indicated. This figure is adapted from www.leisegang.de.
During colposcopy, the patient is seated and a speculum is inserted into the vagina to expose the cervix. The colposcope is next positioned at the correct working distance (around
30cm) from the patient and 3 - 5% acetic acid is applied evenly on the cervix. The colposcopist, who is often a gynecologist or healthcare provider, looks for macroscopic changes in anatomy, such as acetowhitening or mosaicism, that suggest cervical dysplasia or cancer. Refer to the previous section for more detail. Some of the changes are best observed before acid is applied and others are time dependent, so colposcopists observe the cervix before acetic acid application and throughout the entire procedure. Also, colposcopists must be able to observe the entire squamo-columnar junction as that is where the vast majority of cervical dysplasia and cancer originate[1], otherwise the colposcopy is deemed unsatisfactory. Unfortunately, the junction can be hidden within the endocervical canal, particularly in older patients who have given birth. If abnormal changes are identified, the colposcopist will biopsy those sites and submit the specimens for histopathological analysis. In most colposcopy procedures, the
26 colposcopist will also examine the vagina and vulva, but for the purposes of this thesis, we will not discuss these other parts of the genital tract.
Specimens submitted to pathology are sectioned into thin (approximately 10 micron) pieces, and stained, typically with Hematoxylin and Eosin, and placed in a microscope slide.
H&E staining causes nuclei to appear dark and cytoplasm to appear pink[24]. A cervical pathologist then examines the section for microscopic anatomical features that indicate dysplasia or cancer. The pathologist will report the severity of dysplasia (eg. LSIL, HSIL, adenocarcinoma in situ) and cancer (eg. invasive carcinoma, invasive adenocarcinoma) if any is detected. These results, along with the patient's prior medical history, will determine her subsequent management.
The accuracy of colposcopy depends on the colposcopist's judgement, therefore, diagnoses are subject to interobserver variability[25, 26]. Typical values of sensitivity and specificity depend on the stage of dysplasia chosen as the cutoff and the method used to confirm the accuracy of a negative colposcopy. If HSIL is used as the diagnostic cutoff (LSIL and no dysplasia are considered normal while HSIL and worse are abnormal), the sensitivity of colposcopy is 85% and the specificity is 69%[27]. However, small dysplasias and cancers may be more difficult to detect, resulting in a reduction in sensitivity[28].
A conventional Pap smear and colposcopy often have difficulties examining the endocervical canal where adenocarcinoma can occur. For this purpose, a cytologic test called endocervical curettage (ECC) is occasionally conducted during colposcopy. The difference between ECC and Pap smear is the tool used to obtain epithelial cells. ECC uses a thin curette
27 that can fit in the narrow (~ 1mm) endocervical canal to remove endocervical cells while Pap smear typically uses a larger spatula.
2.3.4. Treatments
Once the presence of cervical dysplasia or cancer is confirmed by pathology, there are a number of treatment options available. These options include cryotherapy, laser ablation, excision, hysterectomy, radiation therapy, and chemotherapy. The treatment procedure ultimately used will depend on a number of factors, such as the patient's age, the surgeon's expertise, the availability of equipment, and the severity of disease[29]. cryotherapy: Cryotherapy uses cryoprobes to cover the abnormal tissue areas. Liquid nitrogen is
subsequently passed through the probes such that the diseased tissue freezes.
Cryotherapy is effective for dysplasia and localized cancers and usually can be done in
the clinic. laser ablation: Laser ablation uses a focused laser beam, typically at infrared wavelengths, to
destroy abnormal tissue. This procedure is very specific because only tissue around the
focus point is damaged. Similar to cryotherapy, laser ablation is effective for dysplasia
and localized cancers. However, laser treatment is usually administered under
anesthesia in the surgery room. excision: Excision therapy in the cervix involves the removal of a "cone" shaped piece of tissue
from around the cervical os, usually encompassing the entire transformation zone. This
large biopsy serves two purposes. One, diseased tissue is removed. Two, the specimen
can be evaluated by histopathology for diagnostic purposes. Excisions vary in depth,
28 deeper cuts also treat endocervical diseases, and can be conducted using a scalpel, a
laser, or an electric current (Loop Electrosurgical Excision Procedure or LEEP). LEEP is
typically the treatment of choice for HSIL in the United States. Our clinical studies, to be
described in subsequent chapters, uses LEEP specimens and it will be discussed in more
detail then. If diagnosis of the specimen is important, a scalpel is often used to avoid
charring the edges. Depending on the size of the tissue specimen removed, excision can
be a clinic or surgery room procedure. Excision is useful for treating dysplasia and
localized cancers. hysterectomy: Total hysterectomy involves complete removal of the uterus and cervix. This
removes the risk of recurrence of cervical cancer, but similar cancers may still develop in
remaining parts of the genital tract, such as the vagina. Hysterectomy is effective for any
cancer contained within the cervix and uterus, but is a last option for young women who
still wish to have children. Hysterectomy is conducted in the surgery room under
anesthesia and is generally not used for dysplasia and localized cancers. radiation therapy: Radiation therapy uses localized doses of high energy x-rays to kill cancerous
cells. There are two general types of radiation therapy. The first type, external beam
radiation therapy, uses an external instrument to apply a focused beam of x-rays to the
affected area. In the second type, internal radiation therapy, radioactive material is
placed in the vagina and the emitted radiation kills cancerous cells. Radiation therapy,
usually combined with chemotherapy, can be used at all stages of cervical cancer[29].
29 However, early stage diseases such as dysplasia rarely use radiation therapy because of
the side-effects, which may include fatigue, upset stomach, and incontinence. chemotherapy: Chemotherapy uses cytotoxic drugs, such as cisplatin, to kill cancerous cells. The
drug can be delivered intravenously or directly injected into the affected area.
Chemotherapy is often coupled with radiation therapy to make cancer cells more
sensitive to x-ray radiation. Chemotherapy is usually used for advanced stage cancers
that have spread beyond the cervix or even to other parts of the body. The side-effects
include weakness, hair loss, and reduced immune response because the drugs do not
specifically target cancer cells.
2.3.5. Patient management
Up to this point in the section, we have discussed the various screening, diagnostic, and
treatment options available for clinical use today. In this section we discuss accepted patient
management guidelines in the United States. These guidelines determine which screening,
diagnosis, and treatment options are used after a certain set of test results and the patient's
medical history. It is important to note that the discussion in this section only addresses a
subset of all management guidelines. In particular, we will not discuss in detail management of
glandular abnormalities, invasive cancer, pregnant women, adolescent girls, and HIV positive
patients. Also, the management paths presented here are chosen from different clinically
accepted standards recommended by three professional organizations: the United States
Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), and the American
College of Obstetrics and Gynecology (ACOG).
30 Cervical cancer management begins at the screening stage. Women are recommended to have a Pap test three years after first sexual intercourse or age 21, whichever comes first[30-
32]. Assuming normal results, patients should return for annual Pap smears until age 30. After
age 30, patients who have had three consecutive normal Pap smear results or one normal Pap
result and one negative HPV test can return every three years. Screening can stop once a
patient reaches 65 years of age or undergoes a medical procedure that removes the cervix,
such as total hysterectomy. These guidelines are summarized in the flow chart of Fig. 2.10.
Management guidelines for patients who obtain positive Pap results (Table 2.1) follows
a number of different paths, some of which are illustrated in Fig. 2.10. Patients with ASCUS
receive a HPV test. If the result is negative the patient can return in one year for regular
screening. However, if the patient tests HPV positive, she is referred for colposcopy. If
colposcopy + biopsy results in LSIL or less, the patient returns for regular screening in 6 months
(rather than 1 year). If HSIL or worse is detected, the patient receives treatment, usually with a
LEEP. Patients with ASCH are at greater risk of severe cervical dysplasia and cancer. These
patients are referred directly for colposcopy and ECC. If HSIL, adenocarcinoma in situ, or worse
is detected, the patient receives treatment, else the patient returns for regular screening in 6
months. Patients with LSIL Pap smear results follow the same paths as patients with ASCUS and
positive HPV test. Patients with HSIL Pap results are at high risk of having HSIL or worse (>
50%)[33]. Therefore, these patients receive treatment, although colposcopy and ECC may be conducted prior to treatment to examine the spatial extent of disease and if more severe disease such as invasive cancer is present.
31 -...... ,non h,~ w~ ~'4 3 A ii
~C~
FCW + I~r~v-~
LS L
PSRL or w,.,orsr, giandur b roty
Figure 2.10: Flow chart showing cervical cancer management guidelines in the United States. This chart only presents a portion of all patient management paths.
32 References
1. D. G. Ferris, J. T. Cox, D. M. O'Connor, V. C. Wright, and J. Foerster, Modern Colposcopy: Textbook and Atlas (Kendall Hunt, 2004). 2. A. Singer, and J. Jordan, The Cervix (Blackwell Publishing, Malden, 2006). 3. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: impact of anatomy," (2008). 4. C. Lau, J. Mirkovic, C. C. Yu, G. O'Donoghue, L. Galindo, R. Dasari, V. Feng, K. Badizadegan, A. d. I. Morenas, E. Stier, and M. Feld, "Early cancer detection using Quantitative Spectroscopic Imaging: a phase I clinical trial in the cervix," (2008). 5. J. M. Walboomers, M. V. Jacobs, M. M. Manos, F. X. Bosch, J. A. Kummer, K. V. Shah, P. J. Snijders, J. Peto, C. J. Meijer, and N. Munoz, "Human papillomavirus is a necessary cause of invasive cervical cancer worldwide," J Pathol 189, 12-19 (1999). 6. N. Munoz, F. X. Bosch, S. de Sanjose, R. Herrero, X. Castellsague, K. V. Shah, P. J. Snijders, and C. J. Meijer, "Epidemiologic classification of human papillomavirus types associated with cervical cancer," N Engl J Med 348, 518-527 (2003). 7. D. M. Harper, E. L. Franco, C. M. Wheeler, A. B. Moscicki, B. Romanowski, C. M. Roteli- Martins, D. Jenkins, A. Schuind, S. A. Costa Clemens, and G. Dubin, "Sustained efficacy up to 4.5 years of a bivalent Li virus-like particle vaccine against human papillomavirus types 16 and 18: follow-up from a randomised control trial," Lancet 367, 1247-1255 (2006). 8. K. S. Cuschieri, H. A. Cubie, M. W. Whitley, A. L. Seagar, M. J. Arends, C. Moore, G. Gilkisson, and E. McGoogan, "Multiple high risk HPV infections are common in cervical neoplasia and young women in a cervical screening population," J Clin Pathol 57, 68-72 (2004). 9. Qiagen, "the digene HPV test," (2008), www.hpvtest.com. 10. "The 1988 Bethesda System for reporting cervical/vaginal cytological diagnoses. National Cancer Institute Workshop," JAMA 262, 931-934 (1989). 11. J. W. Sellors, and R. Sankaranarayanan, Colposcopy and Treatment of Cervical Intraepithelial Neoplasia: A Beginner's Manual (international Agency for Research on Cancer, Lyon, 2003). 12. T. Sakuma, T. Hasegawa, F. Tsutsui, and S. Kurihara, "Quantitative analysis of the whiteness of the atypical cervical transformation zone," J Reprod Med 30, 773-776 (1985). 13. R. E. Scully, T. A. Bonfiglio, and S. G. Silverberg, Histologic Typing of Female Genital Tract Tumours. World Health Organization International Histological Classification of Tumours (Springer-Verlag, New York, 1994). 14. T. C. Wright, R. J. Kurman, and A. Ferenczy, Blaustein's Pathology of the Female Genital Tract (Springer-Verlag, New York, 1994). 15. G. N. Papanicolaou, and H. F. Traut, Diagnosis of uterine cancer by the vaginal smear (The Commonwealth Fund, New York, 1943). 16. "The revised Bethesda System for reporting cervical/vaginal cytologic diagnoses: report of the 1991 Bethesda workshop," Acta Cytol 36, 273-276 (1992). 17. "Evidence Report/Technology Assessment: Number 5," (Agency for Health Care Policy and Research, Rockville, 1999).
33 18. M. E. Sherman, "Chapter 11: Future directions in cervical pathology," J Natl Cancer Inst Monogr, 72-79 (2003). 19. S. L. Kulasingam, J. P. Hughes, N. B. Kiviat, C. Mao, N. S. Weiss, J. M. Kuypers, and L. A. Koutsky, "Evaluation of human papillomavirus testing in primary screening for cervical abnormalities: comparison of sensitivity, specificity, and frequency of referral," JAMA 288, 1749-1757 (2002). 20. E. L. Franco, and A. Ferenczy, "Assessing gains in diagnostic utility when human papillomavirus testing is used as an adjunct to papanicolaou smear in the triage of women with cervical cytologic abnormalities," Am J Obstet Gynecol 181, 382-386 (1999). 21. D. Solomon, M. Schiffman, and R. Tarone, "Comparison of three management strategies for patients with atypical squamous cells of undetermined significance: baseline results from a randomized trial," J Natl Cancer Inst 93, 293-299 (2001). 22. G. D. Zielinski, L. Rozendaal, F. J. Voorhorst, J. Berkhof, P. J. Snijders, E. J. Risse, A. P. Runsink, F. A. de Schipper, and C. J. Meijer, "HPV testing can reduce the number of follow-up visits in women treated for cervical intraepithelial neoplasia grade 3," Gynecol Oncol 91, 67-73 (2003). 23. T. C. Wright, Jr., J. T. Cox, L. S. Massad, J. Carlson, L. B. Twiggs, and E. J. Wilkinson, "2001 Consensus Guidelines for the Management of Women with Cervical Intraepithelial Neoplasia," J Low Genit Tract Dis 7, 154-167 (2003). 24. R. S. Cotran, V. Kumar, and T. Collins, Robbins Pathologic Basis of Disease (W. B. Saunders Company, Philadelphia, 1999). 25. D. G. Ferris, and M. Litaker, "Interobserver agreement for colposcopy quality control using digitized colposcopic images during the ALTS trial," J Low Genit Tract Dis 9, 29-35 (2005). 26. E. H. Hopman, F. J. Voorhorst, P. Kenemans, C. J. Meyer, and T. J. Helmerhorst, "Observer agreement on interpreting colposcopic images of CIN," Gynecol Oncol 58, 206-209 (1995). 27. M. F. Mitchell, D. Schottenfeld, G. Tortolero-Luna, S. B. Cantor, and R. Richards-Kortum, "Colposcopy for the diagnosis of squamous intraepithelial lesions: a meta-analysis," Obstet Gynecol 91, 626-631 (1998). 28. C. M. Feltmate, and S. Feldman, "Colposcopy," (UpToDate, 2008), http://utdol.com. 29. (National Cancer Institute, 2008), http://www.cancer.gov/cancertopics/types/cervical. 30. "Screening for cervical cancer: recommendations and rationale," (US Preventive Services Task Force, 2003), www.ahrq.gov. 31. "ACOG Practice Bulletin: clinical management guidelines for obstetrician-gynecologists. Number 45, August 2003. Cervical cytology screening (replaces committee opinion 152, March 1995)," Obstet Gynecol 102, 417-427 (2003). 32. D. Saslow, C. D. Runowicz, D. Solomon, A. B. Moscicki, R. A. Smith, H. J. Eyre, and C. Cohen, "American Cancer Society guideline for the early detection of cervical neoplasia and cancer," CA Cancer J Clin 52, 342-362 (2002). 33. T. S. Dunn, M. Burke, and J. Shwayder, "A "see and treat" management for high-grade squamous intraepithelial lesion pap smears," J Low Genit Tract Dis 7, 104-106 (2003).
34 3. Imaging technology
Chapter 2 reviewed some of the clinical technologies used to detect cervical dysplasia and cancer, such as Pap smear, HPV testing, and colposcopy. These technologies are currently used in combination with biopsy to manage patients at risk of cervical cancer. However, patient management guidelines following a positive screening result can have the patient make multiple visits to the clinic without certain knowledge as to whether or not she has cervical dysplasia/cancer. Also, patients are often subjected to additional diagnostic procedures even though she ultimately had no severe dysplasia or cancer. These occur because all of the clinical technologies have suboptimal accuracy and require time to produce an answer. For example, colposcopy has approximately 85% sensitivity and 69% specificity at distinguishing HSIL + invasive cancer from all other conditions and biopsy results require several weeks to process[l].
During that time, the patient often experiences anxiety due to the uncertain state of her health.
Often, her concern was unnecessary because the initial colposcopic examination called unnecessary biopsies (LSIL or less, depending on the colposcopy threshold used)[2].
Technologies that can more accurately and rapidly detect cervical dysplasia will have significant impact on cervical cancer screening.
In this chapter we will explore both clinically accepted and investigational technologies that are used to help detect cervical abnormalities. We will discuss the physical principles and clinical applications, both current and potential, of ultrasound, magnetic resonance imaging
(MRI), X-ray tomography (CT), confocal imaging, optical coherence tomography (OCT), and optical spectroscopy. Quantitative spectroscopic imaging (QSI), which is a form of optical
35 spectroscopy, will be the focus of subsequent chapters. These technologies are currently not as frequently used in screening and diagnosis as colposcopy, Pap smear, and HPV testing, but they can provide valuable information and some may form the basis of screening/diagnosis procedures in the coming decades.
3.1. Ultrasound
Ultrasound refers to all acoustic waves with frequency beyond the upper limit audible to humans, which is approximately 20kHz. Acoustic waves, or sound waves, are waves of varying pressure that propagate in a medium, such as human tissue. These waves propagate indefinitely until they encounter a change in acoustic impedance or an absorbing medium.
When a medium with different acoustic impedance is encountered, which is equivalent to entering a new medium with different mass density from the previous medium, part of the wave's energy is reflected while the remainder is refracted. Reflection and refraction of sound waves is similar to those undergone by light waves and obey similar laws of reflection and refraction. If the density interface is not smooth, scattering can also occur, which causes the wave's energy to be redirected in multiple directions. In the case of an absorbing medium, the wave's energy is gradually dissipated, usually as heat, as it progresses through the medium.
Ultrasonography is the use of ultrasound to conduct imaging. The fundamental aspect of medical ultrasonography is to use a transducer to send pressure waves into the body and to record the time required for part of the waves to reflect back to the transducer along with their amplitudes[3]. As discussed earlier, every time a sound wave encounters a density interface, part of the wave's energy will be reflected and possibly collected by the transducer. Sound
36 reflected from interfaces further from the transducer take longer to return. By combining measured amplitude and time of flight information, ultrasound can provide tomograms of mass density in the body. The spatial resolution of this approach is determined by the volume of the sound wave and the time between successive acoustic pulses. In the axial direction (direction of sound propagation), sub-millimeter resolution can be achieved while resolution in the lateral direction (perpendicular to the propagation direction) is on the order of millimeters.
In Obstetrics and Gynecology, the medical department that manages cervical cancer, ultrasound is most commonly used to provide real-time images of a fetus. The ability to track moving objects is one of the strengths of ultrasound. Ultrasound is also used to determine the size and depth of penetration of invasive cervical cancers, which has implications on treatment options[4]. This is possible because tumors, like the fetus, often have different mass density than the surrounding medium. Also, large tumors can be resolved by the millimeter scale resolution of ultrasound.
3.2. Magnetic resonance imaging
Magnetic resonance imaging (MRI) relies on the nuclear magnetic resonance (NMR) phenomenon. In NMR, atomic nuclei are placed in a strong magnetic field and exposed to electromagnetic radiation at the appropriate frequency. The energy of the radiation is absorbed and later released. The frequency required for this to occur depends on the nuclei and its surrounding medium. In MRI, the body is placed in a strong magnetic field and radio frequency radiation excites nuclei, predominantly hydrogen atoms found in water, in the body[3]. The resulting NMR signal, which is a time varying magnetic field caused by motion of the nuclei,
37 induces an electric current in a receiver coil which is recorded by a computer. The amplitude of the current depends on the density of nuclei in the body. MRI is able to tomographically map nuclear density in the body by using spatially varying magnetic field gradients and controlling the timing of radio frequency excitation pulses. The result is MRI can chart the location of different tissue types in the body with submillimeter resolution and is particularly suited for distinguishing between different soft tissues.
MRI is one of the most important medical developments in the 2 0 th century. It is used extensively to image the brain for abnormalities such as tumors and can also track water diffusion[5]. However, MRI is not currently used to its maximum potential because of the high costs and long duration of scans. In the cervix, MRI is occasionally used to determine the size and extent of invasive cancers, which have important implications for treatment[4].
3.3. X-ray tomography
In conventional x-ray imaging, x-rays illuminate an object and only some of the energy will be transmitted through the object. The rest is dissipated by absorption. An x-ray sensitive detector placed to receive the transmitted radiation will record a 2D projection of attenuation
(due to absorption) in the 3D object. This is the basic principle of important medical technologies such as mammography. X-ray tomography, or computed tomography (CT), involves recording multiple 2D projections at slightly different angles of illumination and mathematically combining them to obtain a 3D tomogram of attenuation in the object[3]. The key mathematical operation is the Radon transform and the computations are done by a computer. This concept has recently been extended to optical wavelengths for obtaining
38 tomograms of refractive index variations in a transparent object[6]. The spatial resolution of CT depends on a number of factors, such as the number of 2D projections and the pixel size of the detector. Typically, clinical CT systems have millimeter scale resolution.
CT is one of the most important imaging technologies in modern medicine. For example, it is routinely used to detect lung and heart abnormalities such as pneumonia and pulmonary embolisms. In the cervix, CT is occasionally used to help determine the size and extent of invasive cervical cancer, which is important for determining the course of treatment[4].
However, diagnostic options such as CT and MR[ are usually not available in developing countries where the majority of cervical cancers occur.
3.4. Confocal imaging
Confocal imaging is an optical technique where light illuminating the tissue is focused by optics to a very tight focus. At the same time, only light returning from the focal volume due to scattering or fluorescence emission is collected by the detector[7]. This can be accomplished by using a pinhole with high numerical aperture optics to define a small focal volume for light delivery and collection. After light is collected from one point, the focus is shifted to another point. This scanning process is repeated until the entire volume of interest is examined.
Compared to regular wide-field imaging, like that employed by a standard colposcope, confocal imaging has superior spatial resolution in both lateral (direction parallel to the surface of the sample) and axial (direction normal to surface of the sample) directions. A typical confocal imaging system using visible light can achieve micron scale resolution in both directions.
39 In in vivo medical applications, confocal imaging can provide high resolution tomograms
(higher than conventional ultrasound, MRI, and CT) of superficial tissue. There are two general types of tissue confocal imaging. Reflectance confocal imaging illuminates the tissue with visible wavelength light and collects scattered light returning from the focal volume. This produces tomograms related to refractive index variations in the tissue. Fluorescence confocal imaging illuminates the tissue with ultraviolet light and collects light emitted from fluorophores within the focal volume. This produces tomograms related to flurophore concentrations in the tissue.
The penetration depth is limited by the turbidity of the medium (scattering and absorption).
Highly turbid media enlarge the focal spot (lower resolution) and reduce the amount of light energy returning to the detector, thus reducing the effectiveness of confocal imaging. In tissue, a confocal technique can penetrate approximately 200 microns[8]. Due to its high resolution and relatively superficial penetration depth, confocal imaging has been investigated as a tool for conducting "optical biopsy", which uses optical techniques to image tissue in vivo with cellular level resolution such that excisional biopsy is not required for disease diagnosis.
Confocal imaging has been investigated as an optical technique for examining cervical epithelium using visible light (reflectance) and ultraviolet (fluorescence) illumination for dysplasia. Tan et al. reported that fluorescence confocal imaging can permit visualization of anatomical features such as endocervical glands and grading of dysplasia[9]. Carlson et a/. demonstrated the ability of reflectance and fluorescence confocal imaging to distinguish between normal and abnormal cervical epithelium[10]. Confocal imaging may one day enable real-time optical biopsies of cervical epithelium to confirm or reject the presence of cervical dysplasia.
40 3.5. Optical coherence tomography
Optical coherence tomography (OCT) is often regarded as an optical version of ultrasonography[7]. In OCT, low-coherence light is focused to a small focal volume, but typically not as small as that of confocal imaging. Light scattered in the sample and returning to the surface is interfered with a reference beam and collected by an interferometer setup. The detector records an oscillatory signal (either in time or space, depending on the setup) along with an invariant signal. The oscillatory signal is due to light scattered from within the coherence window in the sample. Therefore, the lateral resolution of OCT is similar to that of a confocal system while the axial resolution is determined by the coherence length of the illumination light source. Typically, a low-coherence source, such as a superluminescent diode, has coherence length on the order of microns. Tomography can be achieved by scanning the focal point across the volume of interest (Time-domain OCT). Alternatively, if the detector is color sensitive, the entire axial direction can be collected at once and only a transverse scan is required (Fourier-domain OCT) to achieve tomography[7]. The end result is high resolution tomograms related to refractive index variations in the sample.
To date, OCT has proven to be very effective for ocular imaging because the eye is a relatively clear medium, which permits the small focal volume to be maintained to deeper depths. In turbid tissues however, OCT currently has a penetration depth limited to approximately 300 - 400pm[11]. OCT, like confocal imaging, has been investigated as a technique for conducting optical biopsy in the cervix. Zuluaga et al. developed computer algorithms to automatically analyze in vivo OCT images and found that epithelial reflectivity was
41 higher in abnormal tissue of premenopausal women, but no statistically significant trend was observed for postmenopausal women[12]. Escobar et al. used OCT in combination with colposcopy and visual inspection with acetic acid and found that OCT improved the specificity of both for detecting HSIL[13]. In the future, OCT may enable real-time optical biopsies of cervical epithelium to confirm or reject the presence of cervical dysplasia.
3.6. Optical spectroscopy
So far in this chapter we have reviewed clinically accepted technologies such as ultrasound, MRI, and CT and also investigational technologies such as confocal imaging and
OCT. Ultrasound, MRI, and CT have proven useful for staging invasive cervical cancer and guiding the course of treatment. However, these technologies have millimeter scale resolution, which is not adequate for identifying cervical dysplasia. Confocal imaging and OCT have the micron scale resolution necessary for conducting optical biopsies, but little research has been conducted on their effectiveness. Turbidity in epithelial tissues reduces the resolution of both technologies compared to free-space values, making it difficult to resolve subcellular features such as nuclear size near the basement membrane, an important criterion of histopathology[14, 15]. Also, it is not clear if confocal imaging and OCT can be used efficiently to image a wide field of view, such as the entire cervix, which spans several cm 2. This renders confocal imaging and OCT impractical for screening applications. For diagnostic applications, a complementary technology such as colposcopy is required to determine the site needing
"optical biopsy." Also, imaging technologies typically require an expert to interpret the data,
42 which makes them susceptible to interobserver disagreement, similar to that found in colposcopy, cytology, and pathology[16-19].
Optical spectroscopy is based on the same physical principles as other optical technologies such as OCT, but its approach and the data extracted are significantly different[20]. In spectroscopy, light illuminates a relatively large area of tissue, typically greater
1mm 2. In comparison, confocal and OCT illuminate Im 2 area of tissue at any one time. The returning light, which can be due to scattering or fluorescence, is wavelength resolved by a color sensitive detector, such as a spectrometer. The light energy recorded at each wavelength
(spectrum) depends on the optical properties of the sample. To examine a large area of tissue
(> cm 2 ), the illumination spot can be scanned or the illumination area can be expanded to cover several cm 2 and a spatially resolved detector, such as a charge coupled device (CCD), used to record the spectrum. Each spectrum is computer analyzed to quantitatively determine the properties of the sample at that site. It is important to note spectroscopy does not provide cellular resolution images of tissue architecture. Rather, this information is averaged over the illumination volume and represented in the spectrum. Therefore, turbidity can be measured by spectroscopy and is not necessarily an undesirable confounder.
There are three general types of optical spectroscopy employed in medical applications: elastic scattering spectroscopy (ESS), fluorescence spectroscopy (FS), and Raman spectroscopy
(RaS). ESS, which is often referred to as reflectance spectroscopy, measures light that is elastically scattered (incident and scattered radiation have the same wavelength) from the tissue and the resulting spectra depend on tissue scattering and absorption properties. There
43 are two branches of ESS. Light scattering spectroscopy (LSS) measures singly backscattered light returning from superficial tissue layers using various depth resolution techniques, such as polarization and angular gating[21, 22]. Diffuse reflectance spectroscopy (DRS) measures diffusely reflected light (scattered many times) that has penetrated deeper into the tissue. FS involves illuminating the tissue with ultraviolet light and recording the resulting emission spectra, which depend on the fluorophores present, such as collagen and NADH, and the scattering and absorption properties of the tissue. The fluorescence spectra are considered bulk fluorescence if scattering and absorption are not accounted for and intrinsic fluorescence if scattering and absorption are compensated for. RaS involves illuminating the tissue with near- infrared light and recording the stokes-shifted Raman scattering spectrum, which is at a longer wavelength. Raman scattering is a form of inelastic light scattering. Approximately one in ten million photons that undergo scattering are inelastically scattered while the rest are elastically scattered[23]. The Raman spectra depend on chemical constituents in the tissue and each chemical has a very distinct spectrum, making RaS potentially a very specific technique.
All spectra, regardless of the spectroscopic modality, consist of multiple energy measurements at different wavelengths. In order to conduct disease diagnosis, this information must be converted into a diagnosis, such as HSIL or non-HSIL. Converting a spectrum to a diagnosis is typically a rigorous two step process that can be done by a computer.
1. The first step is to reduce the order, or amount, of data to only the most important
components. A typical spectrometer has more than 1000 pixels, meaning a spectrum
consists of more than 1000 energy measurements at different wavelengths. Many of these
44 measurements represent the same information, such as the hemoglobin concentration, and are thus redundant. One common technique is principal components analysis (PCA)[24].
With PCA, a training set of spectra measured from relevant samples are used to identify the components of the data set, which are related to the eigenvectors of the set. These components are themselves spectra. Each component has a corresponding score which represents the importance of that component in the set of spectra. PCA reduces the set to only the principal components, which are those with the highest scores. Subsequently, future spectra can be fit to the principal components to determine the weight of each principal component in the spectrum. These weights are the reduced representation of the full spectrum. Instead of more than 1000 measurements, each spectrum is now represented as several weights. A second technique to reduce the data is called model-based or quantitative spectroscopy (QS) and is one of the key topics of this thesis[25, 26]. In QS, the measured spectra (elastic scattering, fluorescence, or Raman) are compared to physical models of light propagation in tissue, which predict the spectrum given the optical properties of the tissue. This allows QS to determine the properties of the tissue, such as its blood concentration, oxygen saturation, and structural density[27-30]. A model-based approach offers the advantage of converting spectra into measurements of physiologically understood quantities familiar to clinicians rather than abstract principal components. Also, the resulting measurements will not vary if the instrument is altered as long as the models are adjusted accordingly, which is very convenient during the research and development stages where instrument modifications occur frequently. QS will be described in more detail in subsequent chapters.
45 2. The second step is to input the reduced measurements into a diagnostic algorithm that
converts spectral measurements to a diagnosis. For example, one possible algorithm is if the
structural density is lower than a certain threshold, the site is HSIL, else it is not HSIL. A
diagnostic algorithm must be determined from training data. During training, a set of
spectra are collected from tissue sites on patients at risk of disease. These tissue sites are
later independently evaluated by a gold standard, typically biopsy with histopathology, to
determine the true disease state. The training set now consists of spectral measurements
matched with a disease state. Inference techniques based on logistic regression, Bayes' rule,
or similar concepts are applied to this set to develop a diagnostic algorithm for future use
on prospective measurements. In medical research, step two usually requires significantly
more resources to accomplish than step one since recruiting patients is not an easy task.
Note that step two allows optical spectroscopy to provide a quantitative diagnosis that is
significantly less prone to interobserver disagreement.
Optical spectroscopy has been evaluated extensively as a minimally invasive technology for detecting early stage cancer, such as dysplasia, in a variety of organs. In the cervix, several research groups have investigated single point and wide area versions of spectroscopy for identifying dysplasia. Utzinger et al. conducted single point Raman spectroscopy on 13 patients undergoing colposcopy[31]. They reduced the data set by using only energy measurements at three near infrared wavelengths and found that an effective diagnostic algorithm for identifying
HSIL could be formed from the two combinations of energy ratios. Chang et al. conducted single point reflectance and fluorescence spectroscopy on 161 patients referred for colposcopy[32]. They reduced the data set using PCA and developed diagnostic algorithms to
46 distinguish different tissue types. They found that by using FS alone, HSIL could be distinguished from normal squamous tissue with 83% sensitivity and 80% specificity. However, reflectance was required to yield 72% sensitivity and 83% specificity distinguishing HSIL from normal columnar tissue. Georgakoudi et al. conducted single point quantitative spectroscopy using reflectance and fluorescence spectroscopy on 44 patients referred for colposcopy[33]. They found that measurements of the reduced scattering coefficient (related to tissue structural density), NADH concentration, and nuclear size distribution were useful for developing a diagnostic algorithm to distinguish SILs from biopsied non-SILs. MediSpectra, Inc. developed a novel dysplasia detection system (LUMATM) that received FDA approval to serve as an adjunct to colposcopy[34] for detecting HSIL. LUMA conducts reflectance and fluorescence spectroscopy over a wide area. MediSpectra conducted three studies to develop a diagnostic algorithm for detecting cervical dysplasia and to prospectively evaluate its performance. In the first study, Nordstrom et al. used a prototype version of LUMA to measure spectra from 41 patients referred for colposcopy due to an abnormal Pap smear result[35]. They assembled a training set and used Mahalanobis distances, which is not quantitative spectroscopy, to reduce the spectral data and develop a diagnostic algorithm for categorizing the tissue sites into normal tissue, LSIL, or HSIL. After cross-validation, they were able to distinguish HSIL from normal tissue with 91% sensitivity and 93% specificity and LSIL from normal tissue with 86% sensitivity and 87% specificity. In the second study, Huh et al. used LUMA to measure spectra from 604 patients[36]. Using the same methodology as Nordstrom et al., the authors developed a diagnostic algorithm to classify tissue sites as normal transformation zone, normal ectocervix, normal endocervix, LSIL, and HSIL based on the measured spectra. They determined LUMA was
47 able to distinguish HSIL from all other tissue conditions with 92% sensitivity and 50% specificity.
Alvarez et al. used the LUMA to record spectra from 2299 women referred for colposcopy and applied the algorithm developed by Huh et al. prospectively[37]. They compared the use of
LUMA as an adjunct to colposcopy to guide biopsy versus colposcopy alone. Amongst patients with ASCUS or LSIL Pap smear results, the authors found LUMA + colposcopy was 27% more likely to correctly identify HSIL/cancer in a woman with HSIL/cancer than colposcopy alone.
However, this came at the expense of a 33% increase in the number of biopsies required per patient and there was no improvement for patients with HSIL cytology. Mirkovic et al. conducted QS with reflectance and fluorescence on 43 patients undergoing colposcopy and made two significant findings that were previously not known to optical spectroscopists working on cervical dysplasia[38, 39]. They discovered that in order to properly evaluate the accuracy of spectroscopy for detecting cervical dysplasia, it is important to consider the cervix as three separate regions, endocervix, transformation zone, and ectocervix. Also, to identify
HSIL within the transformation zone of a patient, it is beneficial to normalize spectroscopy measurements by corresponding measurements obtained from the ectocervix of the same patient, which helps to reduce the effects of interpatient variation. After normalization, only the A parameter, the reduced scattering coefficient at 700nm was needed to identify HSIL. The
QSI research we present in subsequent chapters will follow from these important findings.
48 References
1. M. F. Mitchell, D. Schottenfeld, G. Tortolero-Luna, S. B. Cantor, and R. Richards-Kortum, "Colposcopy for the diagnosis of squamous intraepithelial lesions: a meta-analysis," Obstet Gynecol 91, 626-631 (1998). 2. S. B. Cantor, M. Cardenas-Turanzas, D. D. Cox, E. N. Atkinson, G. M. Nogueras-Gonzalez, J. R. Beck, M. Follen, and J. L. Benedet, "Accuracy of colposcopy in the diagnostic setting compared with the screening setting," Obstet Gynecol 111, 7-14 (2008). 3. J. T. Bushberg, J. A. Seibert, E. M. L. Jr., and J. M. Moore, The Essential Physics of Medical Imaging (Lippincott Williams & Wilkins, Philadelphia, 2002). 4. (National Cancer Institute, 2008), http://www.cancer.gov/cancertopics/types/cervical. 5. D. Le Bihan, E. Breton, D. Lallemand, P. Grenier, E. Cabanis, and M. Laval-Jeantet, "MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders," Radiology 161, 401-407 (1986). 6. W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, "Tomographic phase microscopy," Nat Methods 4, 717-719 (2007). 7. L. V. Wang, and H.-l. Wu, Biomedical Optics - Principles and Imaging (John Wiley & Sons, Hoboken, NJ, 2007). 8. C. L. Smithpeter, A. K. Dunn, A. J. Welch, and R. Richards-Kortum, "Penetration depth limits of in vivo confocal reflectance imaging," Applied Optics 37, 2749-2754 (1998). 9. J. Tan, P. Delaney, and W. J. McLaren, "Confocal endomicroscopy: a novel imaging technique for in vivo histology of cervical intraepithelial neoplasia," Expert Rev Med Devices 4, 863-871 (2007). 10. K. Carlson, I. Pavlova, T. Collier, M. Descour, M. Follen, and R. Richards-Kortum, "Confocal microscopy: imaging cervical precancerous lesions," Gynecol Oncol 99, S84-88 (2005). 11. A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, "Optical coherence tomography - principles and applications," Reports on Progress in Physics 66, 239-303 (2003). 12. A. F. Zuluaga, M. Follen, I. Boiko, A. Malpica, and R. Richards-Kortum, "Optical coherence tomography: a pilot study of a new imaging technique for noninvasive examination of cervical tissue," Am J Obstet Gynecol 193, 83-88 (2005). 13. P. F. Escobar, L. Rojas-Espaillat, S. Tisci, C. Enerson, J. Brainard, J. Smith, N. J. Tresser, F. I. Feldchtein, L. B. Rojas, and J. L. Belinson, "Optical coherence tomography as a diagnostic aid to visual inspection and colposcopy for preinvasive and invasive cancer of the uterine cervix," Int J Gynecol Cancer 16, 1815-1822 (2006). 14. R. J. Kurman, Blaustein's Pathology of the Female Genital Tract (Springer-Verlag, New York, 2002). 15. R. S. Cotran, V. Kumar, and T. Collins, Robbins Pathologic Basis of Disease (W. B. Saunders Company, Philadelphia, 1999). 16. K. M. Ceballos, W. Chapman, D. Daya, J. A. Julian, A. Lytwyn, C. M. McLachlin, and L. Elit, "Reproducibility of the histological diagnosis of cervical dysplasia among pathologists from 4 continents," Int J Gynecol Pathol 27, 101-107 (2008). 17. D. G. Ferris, and M. Litaker, "Interobserver agreement for colposcopy quality control using digitized colposcopic images during the ALTS trial," J Low Genit Tract Dis 9, 29-35 (2005).
49 18. E. H. Hopman, F. J. Voorhorst, P. Kenemans, C. J. Meyer, and T. J. Helmerhorst, "Observer agreement on interpreting colposcopic images of CIN," Gynecol Oncol 58, 206-209 (1995). 19. M. H. Stoler, and M. Schiffman, "Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study," JAMA 285, 1500-1505 (2001). 20. T. Vo-Dinh, Biomedical Photonics Handbook (CRC Press LCC, Boca Raton, FL, 2002). 21. V. Backman, R. Gurjar, K. Badizadegan, L. Itzkan, R. R. Dasari, L. T. Perelman, and M. S. Feld, "Polarized light scattering spectroscopy for quantitative measurement of epithelial cellular structures in situ," leee Journal of Selected Topics in Quantum Electronics 5, 1019-1026 (1999). 22. C. C. Yu, C. Lau, J. W. Tunnell, M. Hunter, M. Kalashnikov, and C. Fang-Yen, "Assessing epithelial cell nuclear morphology by using azimuthal light scattering spectroscopy," Optics Letters 31, 3119-3121 (2006). 23. J. J. Laserna, Modern Techniques in Raman Spectroscopy (John Wiley & Sons Ltd., West Sussex, UK, 1996). 24. 1. T. Jolliffe, Principal Component Analysis (Springer-Verlag, New York, 2002). 25. C. C. Yu, C. Lau, G. O'Donoghue, J. Mirkovic, S. McGee, L. Galindo, A. Elackattu, E. Stier, G. Grillone, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Quantitative spectroscopic imaging for non-invasive early cancer detection," Opt Express 16, 16227-16239 (2008). 26. D. Arifler, C. MacAulay, M. Follen, and R. Richards-Kortum, "Spatially resolved reflectance spectroscopy for diagnosis of cervical precancer: Monte Carlo modeling and comparison to clinical measurements," Journal of Biomedical Optics 11, - (2006). 27. K. L. Bechtel, W. C. Shih, and M. S. Feld, "Intrinsic Raman spectroscopy for quantitative biological spectroscopy Part II: Experimental applications," Optics Express 16, 12737-12745 (2008). 28. W. C. Shih, K. L. Bechtel, and M. S. Feld, "Intrinsic Raman spectroscopy for quantitative biological spectroscopy Part 1: Theory and simulations," Optics Express 16, 12726-12736 (2008). 29. G. Zonios, L. T. Perelman, V. M. Backman, R. Manoharan, M. Fitzmaurice, J. Van Dam, and M. S. Feld, "Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo," Applied Optics 38, 6628-6637 (1999). 30. M. G. Muller, 1. Georgakoudi, Q. G. Zhang, J. Wu, and M. S. Feld, "Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption," Applied Optics 40, 4633-4646 (2001). 31. U. Utzinger, D. L. Heintzelman, A. Mahadevan-Jansen, A. Malpica, M. Follen, and R. Richards-Kortum, "Near-infrared Raman spectroscopy for in vivo detection of cervical precancers," Applied Spectroscopy 55, 955-959 (2001). 32. S. K. Chang, Y. N. Mirabal, E. N. Atkinson, D. Cox, A. Malpica, M. Follen, and R. Richards- Kortum, "Combined reflectance and fluorescence spectroscopy for in vivo detection of cervical pre-cancer," Journal of Biomedical Optics 10, - (2005). 33. 1. Georgakoudi, E. E. Sheets, M. G. Muller, V. Backman, C. P. Crum, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo," Am J Obstet Gynecol 186, 374-382 (2002). 34. D.-B. Tillman 2006).
50 35. R. J. Nordstrom, L. Burke, J. M. Niloff, and J. F. Myrtle, "Identification of cervical intraepithelial neoplasia (CIN) using UV-excited fluorescence and diffuse-reflectance tissue spectroscopy," Lasers in Surgery and Medicine 29, 118-127 (2001). 36. W. K. Huh, R. M. Cestero, F. A. Garcia, M. A. Gold, R. S. Guido, K. McIntyre-Seltman, D. M. Harper, L. Burke, S. T. Sum, R. F. Flewelling, and R. D. Alvarez, "Optical detection of high- grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study," Am J Obstet Gynecol 190, 1249-1257 (2004). 37. R. D. Alvarez, and T. C. Wright, "Effective cervical neoplasia detection with a novel optical detection system: a randomized trial," Gynecol Oncol 104, 281-289 (2007). 38. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: impact of anatomy," (2008). 39. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: development of a diagnostic algorithm," (2008).
51 4. Quantitative Spectroscopy
In previous chapters we reviewed both clinically accepted and investigational technologies for detecting cervical dysplasia. We introduced quantitative spectroscopy (QS) and described its strengths and weaknesses relative to other technologies. We also briefly reviewed some of the research that has been conducted in the cervix using optical spectroscopy. In this chapter, we will describe in detail the physical models and instrumentation necessary to conduct QS in both single point and wide area implementations. QS can be implemented with reflectance, fluorescence, and Raman spectroscopy and we will cover the first two in this chapter. Quantitative spectroscopic imaging (QSI), the main topic in subsequent chapters of this thesis, will only use reflectance and fluorescence.
4.1. Physical models
QS involves measuring spectra from a sample and reducing the order of the data with physical models of light propagation and fluorescence such that the resulting measurements are physical properties of the sample. For example, QS in tissue can measure the hemoglobin concentration, oxygen saturation, fat density, collagen concentration, and NADH concentration.
In this section, we will present the physical models that make QS possible and also discuss their implementation.
4.1.1. Reflectance spectroscopy models
Elastic scattering (ESS) or reflectance spectroscopy (RS) measures light elastically scattered from the sample at multiple wavelengths. In quantitative spectroscopy using RS, models of light scattering and absorption in tissue are used to convert the measured spectra to
52 physiologically understood quantities such as blood concentration. In this section, we will focus on QS using diffuse reflectance spectroscopy (DRS) as that is one of the key topics of this thesis.
DRS measures the spectrum of diffusely reflected light (scattered multiple times) returning to the tissue surface. In general, returning light is never completely diffuse because diffuse scattering requires isotropic scattering, which only occurs for infinitesimally small scatterers[1]. However, if a RS measurement involves many photons and most photons undergo multiple scattering events in the sample before returning to the surface to be collected, the reflected light can be considered diffuse. In a turbid sample, this occurs when scattering dominates over absorption and there is sufficient space between light delivery and collection areas (delivery-collection separation) such that multiple scattering events can occur[2-4]. Assuming the above conditions are achieved, the fraction of light energy delivered to a point on the sample that is eventually collected by the instrument from another point has been derived by Farrell et al.[3].
R4= 4 1+ - +(1+4A) ( +- " 2 7r ys+I1a K 1i 3 T2 T2
RO is the reflectance, the fraction of illumination energy ultimately collected by the instrument. p,' is the reduced scattering coefficient and is related to the scattering coefficient (ps) by
I' = M,(1 - g), where g is the anisotropy coefficient. The scattering coefficient (mm') is approximately the number of scattering events a photon undergoes while propagating a certain distance in the sample. It is proportional to the number density of scatterers in the sample. The anisotropy coefficient represents the directionality of scattering and varies from -1 to 1. If g = 1, all light is scattered in the exact forward direction (same as the direction of illumination). If g =
53 0, scattering is isotropic. If g = -1, all scattering is in the exact backwards direction (opposite to the direction of illumination). p (mm 1) is the absorption coefficient and is approximately the number of absorption events a photon would undergo after propagating a certain distance in the medium. zo =- and y = V3pa(1s + Ma). r (mm) is the distance between the light
2 delivery and collection points. r1 = z + r . A is related to the refractive index mismatch between the sample and the surrounding environment, typically air, and can be expressed as
2/ 1_[(ntissue1)(ntissue +1)]2 -1+1cosOCI3 A =nair nair (4.2) 1-ICOSOC12 where ntissue is the refractive index of the tissue and nair = 1 is the refractive index of air. Oe is the critical angle for total internal reflection[5]. A accounts for specular reflection when light
2 traverses the interface. r2 = (+ 3A) zo + r
Equation 4.1 is the reflectance point spread function, an optical version of the impulse response. However, Eq. 4.1 is not a good representation of most spectroscopy instruments because the light delivery (AD) and collection areas (Ac) are usually not infinitesimally small. To account for finite instrument geometries, Eq. 4.1 is integrated over AD and Ac[2].
RT = A, fAc RO(r) dAc dAD, (4.3) where RT is the reflectance measured by the instrument and r is the Euclidean distance between the differential area elements dAc and dAD. Equation 4.3 can be integrated to determine the reflectance that would be measured by an instrument from a sample with known optical properties. Note in spectroscopy, this integral is computed at each wavelength
54 because optical properties often vary with color. There are alternative methods to compute RT when the diffusion approximations are not satisfied by the instrument or the sample. Some methods include Monte Carlo simulations[6], higher order solutions to the transport equation[7], and tissue phantom calibration[4]. These approaches tend to be more accurate than diffusion-based models but require extensive computations and/or are less flexible. We will not discuss these methods in detail in this thesis but the reader is referred to the cited literature for further details.
Once a reflectance spectrum has been measured from the tissue (Rm(A)), where A is the wavelength in units of nanometers, the model of Eq. 4.3 can be fitted to Rm by varying Pa and p, at each wavelength until the best agreement between measurement and theory is obtained.
This can be accomplished as long as light delivery-collection geometries, represented by AD and
Ac, are known. The end result is measurements of the tissue's scattering and absorption properties. However, the remainder of this thesis does not use this approach because inverting with Eq. 4.3 requires extensive computations to obtain RT for a large range of scattering and absorption coefficients. Furthermore, if AD and Ac cannot be independently measured with sufficient accuracy, RT may also have to be computed for a small range over the two areas.
Instead, we compute RT by assuming light is delivered in an infinitesimally small point at the center of a circular collection area, as written in Eq. 4.4.
RT = 21cf rRo(r)dr. (4.4)
This is a closed form integral that when evaluated, takes the form below[2].
55 RT = ALS (e-1zO + e-(+jA)Pzo _ e -ri(1+A) -r), (4.5) Ps+ r11 3 r2
2 2 where r' = z + r' and r 2' = (+ 3A) z0 + rc . r' is the radius of the circular collection area.
In reflectance spectroscopy, Eq. 4.5 can be fit to each measured reflectance spectrum by optimizing the least-squares objective function of Eq. 4.6.
min 4 (k),la() ZI(R (A) - R(A))2. (4.6)
R is the RT spectrum from Eq. 4.5 that satisfies Eq. 4.6. This fit spectrum has been used extensively in previous quantitative spectroscopy studies[2, 8-13]. The objective of RS is to obtain the I' and Pa that solve Eq. 4.6. However, since Eq. 4.5 only involves one measurement for R, it is mathematically impossible to identify two unique solutions y' and Ila unless the coefficients are dependent, which is not usually the case in tissue. To get around this non- uniqueness, we utilize the known spectral properties of scatterers and absorbers in tissue.
Scatterers in tissue have a visible wavelengths reduced scattering coefficient spectrum that can be described by
1'(A) = A , (4.7) where AO is a reference wavelength we set to 700nm and A and B are the amplitude and wavelength dependence of the scattering coefficient, respectively[14]. Absorption in epithelial tissue is primarily due to the oxygenated and deoxygenated forms of hemoglobin, which together give the absorption coefficient the spectral form
56 y. () = [Hb](acEHbO2 + (1 - a)EHb), (4.8) where [Hb] is the total concentration of both forms of hemoglobin, a is the fraction of
hemoglobin that is oxygenated, and EHb and EHbO 2 are the extinction coefficients of
deoxygenated and oxygenated hemoglobin, respectively. It is important to note that other
absorbers, such as beta-carotene and melanin, may also contribute to the absorption
coefficient in some parts of the body[15, 16]. Using Eqs. 4.7 and 4.8, the optimization in Eq. 4.6
can be rewritten as
minA,B,[Hb],a ZX(R (A) - R (a)) (4.9)
As long as a sufficient number of wavelengths are measured in the R spectrum, the reflectance
spectroscopy parameters A, B, [Hb], and a can be independently obtained. These parameters
represent structural and absorption properties of the tissue.
To obtain the reflectance spectroscopy parameters using Eqs. 4.5 and 4.9, it is important
to accurately determine the instrument's geometry parameter re'. This can be done by using
the instrument to measure R(L) from phantoms with known scattering and absorption
properties. We will describe this calibration in more detail when the quantitative spectroscopic
imaging system is described in chapter 5.
4.1.2. Fluorescence spectroscopy models
Fluorescence spectroscopy (FS) measures auto-fluorescence spectra from native tissue
fluorophores such as collagen and NADH. The net fluorescence spectrum is the linear sum of
different amounts of basis spectra corresponding to emission from different fluorophores. The
57 objective of FS is to measure the amounts of each fluorophore contributing to the total
fluorescence.
Unfortunately, the fluorescence spectrum initially recorded by the instrument, known as
the bulk fluorescence spectrum, is not the same as the net fluorescence spectrum that would
be recorded if the fluorophores were removed from the tissue's turbidity (scattering and
absorption), known as the intrinsic fluorescence spectrum. This occurs because turbidity alters
the intrinsic spectrum. Therefore, unless turbidity is corrected for, the measured fluorescence
spectrum is not the linear sum of the intrinsic fluorescence spectra of the different
fluorophores. In this section, we focus on correcting the effects of turbidity such that the
measured bulk fluorescence spectra can be converted into the corresponding intrinsic
fluorescence spectra. This approach is called intrinsic fluorescence spectroscopy (IFS) in a turbid
medium.
Reflectance spectroscopy measures the scattering and absorption properties of tissue.
In the previous section we described how to use the diffusion approximation to extract the
tissue's optical properties from the measured reflectance spectrum. If FS is conducted in the
same volume of tissue as RS, it is possible to use the reflectance measurements to correct for
the effects of turbidity. Wu et al. and Muller et al. developed an analytical method for
conducting IFS with the aid of reflectance measurements[17, 18].
F(Am) f () = 1 Rn(Ax)Ro(Am) R(Ax) (R(Am) (4.10) Ms(Ax)l E(Ax)E(Am) Ro(Ax) (RO(Am) f and F are the intrinsic and bulk fluorescence respectively. A, and Am are the excitation and
emission wavelengths. R is the measured reflectance and RO is the reflectance that would be
58 measured in the absence of absorption. E = ef - 1 and fl = S(1 - g). S and / are two instrument geometry parameters that can be calibrated for using tissue phantoms. This process will be discussed in more detail when the QSI system is described in chapter 5. Equation 4.10 gives the intrinsic fluorescence spectrum from the measured bulk fluorescence and reflectance spectra. Ro can be obtained using Eq. 4.5 with p, set to 0. ps is obtained from A, B, and g, with the latter obtained from separate studies of tissue[17].
The model of Eq. 4.10 assumes RS and FS sample the same volume of tissue and also makes a few simplifying approximations to obtain an analytical form for E. To avoid these limitations, empirical and Monte Carlo methods have been proposed which can map how intrinsic fluorescence from a fluorophore is altered by varying degrees of turbidity[19-21].
These methods can be incorporated into future spectroscopy systems, but this thesis will use the approach of Wu[18] and Muller[17].
The intrinsic fluorescence spectrum obtained using Eq. 4.10 is the linear sum of emission spectra from different native tissue fluorophores. Unfortunately, the emission spectra depend on the environment surrounding each fluorophore (tissue)[22], and are thus difficult to measure in a laboratory setting. Therefore, to perform IFS and extract the amount of each fluorophore, it is necessary to simultaneously extract the amount and emission spectrum of each fluorophore. One technique used to obtain the spectral shape of emission spectra contributing to the total fluorescence spectrum is multivariate curve resolution (MVCR)[9, 10,
23, 24]. MVCR is a technique where a training set of spectra measured from tissue is fitted to
59 obtain the optimal number of fluorophores and their basis emission spectra by solving the optimization problem in Eq. 4.11.
eN : min fgbasis =C fs(
N is the number of fluorophores contributing tof, the ith measured intrinsic fluorescence spectrum in the training set consisting of K spectra. cj is the concentration of the jth fluorophore in arbitrary units and f-basis is the corresponding amplitude normalized emission spectrum. eN is the optimal fitting error for N fluorophores. To use Eq. 4.11, MVCR begins by assuming N = 1 and obtains el and t0as for the fluorophore. The number of fluorophores is successively increased and the corresponding fitting errors and basis spectra are obtained until the error falls below a certain threshold. The result is the number of fluorophores contributing to the data set and each of their basis emission spectrum. Any future prospective intrinsic fluorescence spectrum is the linear sum of different amounts of each basis spectrum. These amounts are the concentrations of native fluorophores, in arbitrary units, present in the tissue and can be obtained by least-squares fitting. The primary fluorophores emitting after 337nm excitation are collagen and the reduced form of nicotinamide adenine dinucleotide (NADH). The amounts of collagen and NADH measured by QSI is denoted by the fluorescence spectroscopy parameters Coll and NADH, respectively. We will describe this process specifically for the QSI system in chapter 5.
4.2. Instrumentation
In section 4.1 we described the principles of reflectance and auto-fluorescence spectroscopy. This section will focus on describing some of the clinical instrumentation that has
60 been used to conduct RS and FS separately or in combination. Note quantitative spectroscopy
and other forms of data order reduction typically use the same instrumentation.
4.2.1. Reflectance spectroscopy instruments
Reflectance spectroscopy measures tissue reflectivity, which is due to elastic scattering
and absorption, at multiple wavelengths. There are two general approaches to RS. The first
approach uses white-light illumination with color resolved detection. The second approach uses
successive monochromatic illuminations at different wavelengths with B/W detection at each
wavelength. We will present examples of both approaches.
White-light illumination
In an early study, Mourant et al. developed a RS system where white-light (300nm -
750nm) from a xenon arc lamp was coupled into a 200pm diameter delivery fiber[25]. The light
is relayed by the delivery fiber onto the tissue surface. Light backscattered by the tissue
towards the surface is collected by a 200pm collection fiber located 300 to 400im (center-to-
center) from the delivery fiber. The light is then relayed by the collection fiber to a
spectrometer which color resolves the energy. The energy collected at each wavelength is displayed on a computer in the form of a spectrum. Concurrent to the tissue measurement,
light from the xenon lamp is also delivered to a white spectralon reflectance standard. Light scattered by the standard is collected by another spectrograph and dispersed. The tissue spectrum is divided by the standard spectrum at each wavelength to obtain the instrument independent measurement of tissue reflectivity, R(A). This instrument where delivery and
61 collection are implemented through a flexible fiber optic probe is amenable to studying many parts of the body, including enclosed regions such as the gastrointestinal tract.
Monochromatic illuminations
Gurjar et al. developed a wide-area RS imaging system[26]. Light from an arc lamp is collimated, color filtered by band-pass filters, and linearly polarized prior to illuminating a
1.3cm x 1.3cm region of the tissue. Backscattered light traveling near the exact opposite direction of the illumination is passed through a second linear polarizer (aligned parallel to the first) and collected by a charge coupled device (CCD) detector. Each data acquisition involves using 11 band-pass filters between 450nm and 700nm in succession to acquire both spatially and spectrally resolved reflectance data. The instrument has 4nm wavelength resolution and
25pm spatial resolution. The polarizers serve to favor collecting light that has undergone fewer scattering events and thus, the instrument is more sensitive to superficial tissue properties.
4.2.2. Fluorescence spectroscopy instruments
Fluorescence spectroscopy involves exciting the tissue with ultraviolet light and collecting the returning light, due to fluorescence emission from native tissue fluorophores, at multiple wavelengths. There are two general approaches to FS. The first involves varying the excitation wavelength and collecting the emitted light power (excitation spectroscopy). The second involves using one excitation wavelength and collecting the spectrum of emitted light power (emission spectroscopy). It is possible to combine the two approaches to collect what is known as the excitation-emission matrix (EEM).
62 Excitation spectroscopy
Kollias et al. and Gillies et al. used the Fluorolog 212 (SPEX Industries) to measure excitation spectra from mouse and human skin in vivo[27, 28]. The Flurolog system is capable of measuring EEMs, but here we will focus on its excitation spectroscopy capabilities. Light from a xenon arc-lamp is coupled into the input side of a monochromator. The monochromator transmits one color of narrowband light which is coupled into a set of delivery fibers (0.1mm diameter). These fibers are bundled together as part of a probe and light is relayed to the tissue surface. Emitted light is collected by a set of collection fibers, which are also bundled with the delivery fibers, and transmitted to a detector. To conduct excitation spectroscopy, the monochromator's grating is rotated to multiple excitation wavelength settings and the emitted energy is collected at each setting.
Emission spectroscopy
Ramanujam et al. developed a FS system that excites the tissue with 337nm light[29]. In the instrument, light from a nitrogen laser is coupled into a delivery fiber (200pm diameter,
0.22 NA) that relays the light to the tissue surface. Light returning from the tissue, due to fluorescence, scattering, and absorption, is collected by nine collection fibers (100pm diameter,
0.22 NA) arranged around the larger delivery fiber. The emitted light is relayed to a multi- channel spectrometer that records the energy spectrum collected by each fiber from 350nm -
700nm. The instrument was used to obtain measurements from normal cervical tissue and cervical dysplasia.
4.2.3. Multimodal spectroscopy instruments
63 Multimodal spectroscopy, which combines reflectance and fluorescence spectroscopy into one system for clinical data acquisition, provides a number of benefits over using either modality alone. For example, numerous researchers have found that combined RS and FS provides superior diagnostic ability for detecting dysplasia/cancer in a number of different organs[8-10, 30, 31]. Also, acquiring reflectance data with fluorescence data allows for intrinsic fluorescence spectroscopy, which enables direct measurements of the amounts of native tissue fluorophores[17, 18, 21, 32]. In this section we will describe one contact-probe and one wide- area imaging implementation of multimodal spectroscopy.
Probe
A probe instrument examines a small area of tissue during one acquisition. The instruments of Mourant et al., Ramanujam et al., and the Fluorolog 212 were all probes.
Tunnell et al. developed a multimodal probe, the fastEEM, to conduct clinical studies in a number of organs[33]. The fastEEM has two light sources, a Xe flashlamp and a XeCI excimer laser. For reflectance spectroscopy measurements, broadband visible light (300nm - 800nm) from the flashlamp is coupled into a delivery fiber (0.2mm diameter, 0.22 NA). The fiber relays the light to the tissue and backscattered light is collected by six collection fibers (0.2mm diameter, 0.22 NA) positioned around the delivery fiber. The delivery and collection fibers constitute the "probe." Reflected light is relayed by the collection fibers to a spectrograph/CCD that records the six spectra and displays them on a computer. For fluorescence measurements, the laser sequentially illuminates nine quartz cuvettes, each containing a different laser dye, and one prism. Light entering the dyes are emitted at a longer wavelength (342nm, 360nm,
64 383nm, 400nm, 407nm, 425nm, 441nm, 461nm, 483nm) and light traversing the prism maintains the original laser wavelength (308nm). All light originating from the excimer laser is coupled into the delivery fiber and relayed to the tissue. Fluoresced light due to each excitation wavelength is collected by the collection fibers and relayed to the spectrograph/CCD. Prior to entering the spectrograph, short-pass filters remove all light energy with shorter wavelength than the excitation light because emission light is lower energy than excitation light. The combination of multiple excitation wavelengths and recording emission spectra allows the fastEEM to record an excitation-emission matrix.
Imaging
A wide-area spectroscopic imaging system records spectra from multiple sites across a wide area of tissue, typically several cm 2. Imaging offers a distinct advantage over probe systems because the latter usually cannot completely sample at-risk organs, possibly resulting in under diagnosis[34]. The instrument of Gurjar et al. is a spectroscopic imaging system.
TM Schomacker et al. developed a multimodal spectroscopic imaging system, LUMA , for detecting cervical dysplasia in vivo[35]. This instrument is similar to the QSI system that will be described in chapter 5. For RS, broadband visible light (360nm - 720nm) from two xenon arc lamps is coupled into four relay fibers. Light exiting the fibers is imaged by four lenses onto the cervix. The fibers illuminate identical 3.2cm diameter circles on the cervix, each from a different direction to reduce the adverse effects of specular reflection. Light backscattered from a certain
1.1mm diameter interrogation site on the cervix is reflected off a 2D scanning mirror and coupled into a receiving fiber (0.2mm diameter, 0.22 NA). The receiving fiber transmits the light
65 to a spectrograph/CCD unit. Imaging is performed by scanning the 2D mirror after each CCD exposure such that the interrogation site is scanned across the 2.5cm diameter field of view.
For FS, 337nm light from a nitrogren laser is reflected by the 2D scanning mirror onto the interrogation site. The emitted light also reflects off the scanning mirror and is coupled into the relay fiber which delivers the light to the spectrograph/CCD unit. To separate the excitation and emission light, a dichroic mirror that reflects 337nm and transmits visible wavelengths is placed between the scanning mirror and the relay fiber. Imaging is obtained by acquiring a fluorescence measurement immediately after the reflectance measurement and then scanning the mirror to change the location of the interrogation site on the cervix.
In chapter 5 we will describe the QSI system and compare it to LUMA, so it is important to note some of the key features in LUMA. First, white-light illuminates the entire cervix during
RS and UV light illuminates only the interrogation site during FS. Second, for both RS and FS, returning light is collected only from the interrogation site and imaging is accomplished by scanning the interrogation site.
66 References
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67 14. R. L. van Veen, W. Verkruysse, and H. J. Sterenborg, "Diffuse-reflectance spectroscopy from 500 to 1060 nm by correction for inhomogeneously distributed absorbers," Opt Lett 27, 246-248 (2002). 15. S. McGee, J. Mirkovic, V. Mardirossian, A. Elackattu, C. C. Yu, S. Kabani, G. Gallagher, R. Pistey, L. Galindo, K. Badizadegan, Z. Wang, R. Dasari, M. S. Feld, and G. Grillone, "Model-based spectroscopic analysis of the oral cavity: impact of anatomy," J Biomed Opt 13, 064034 (2008). 16. G. Zonios, A. Dimou, I. Bassukas, D. Galaris, A. Tsolakidis, and E. Kaxiras, "Melanin absorption spectroscopy: new method for noninvasive skin investigation and melanoma detection," J Biomed Opt 13, 014017 (2008). 17. M. G. Muller, I. Georgakoudi, Q. G. Zhang, J. Wu, and M. S. Feld, "Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption," Applied Optics 40, 4633-4646 (2001). 18. J. Wu, M. S. Feld, and R. P. Rava, "Analytical Model for Extracting Intrinsic Fluorescence in Turbid Media," Applied Optics 32, 3585-3595 (1993). 19. K. R. Diamond, M. S. Patterson, and T. J. Farrell, "Quantification of fluorophore concentration in tissue-simulating media by fluorescence measurements with a single optical fiber," Applied Optics 42, 2436-2442 (2003). 20. N. C. Biswal, S. Gupta, N. Ghosh, and A. Pradhan, "Recovery of turbidity free fluorescence from measured fluorescence: an experimental approach," Optics Express 11, 3320-3331 (2003). 21. G. M. Palmer, and N. Ramanujam, "Monte-Carlo-based model for the extraction of intrinsic fluorescence from turbid media," Journal of Biomedical Optics 13, - (2008). 22. J. R. Lackowicz, Principles of Fluorescence Spectroscopy (Springer-Verlag, New York, 2006). 23. M. C. G. Antunes, and J. C. G. E. da Silva, "Multivariate curve resolution analysis excitation-emission matrices of fluorescence of humic substances," Analytica Chimica Acta 546, 52-59 (2005). 24. J. C. G. E. da Silva, and R. Tauler, "Multivariate curve resolution of synchronous fluorescence spectra matrices of fulvic acids obtained as a function of pH," Applied Spectroscopy 60, 1315-1321 (2006). 25. J. R. Mourant, I. J. Bigio, J. Boyer, T. M. Johnson, J. Lacey, A. G. Bohorfoush, and M. Mellow, "ELASTIC SCATTERING SPECTROSCOPY AS A DIAGNOSTIC TOOL FOR DIFFERENTIATING PATHOLOGIES IN THE GASTROINTESTINAL TRACT: PRELIMINARY TESTING," Journal of Biomedical Optics 1, 9 (1996). 26. R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, "Imaging human epithelial properties with polarized light-scattering spectroscopy," Nat Med 7, 1245-1248 (2001). 27. R. Gillies, G. Zonios, R. R. Anderson, and N. Kollias, "Fluorescence excitation spectroscopy provides information about human skin in vivo," J Invest Dermatol 115, 704-707 (2000). 28. N. Kollias, R. Gillies, M. Moran, I. E. Kochevar, and R. R. Anderson, "Endogenous skin fluorescence includes bands that may serve as quantitative markers of aging and photoaging," J Invest Dermatol 111, 776-780 (1998).
68 29. N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, E. Silva, and R. Richardskortum, "Fluorescence Spectroscopy - a Diagnostic-Tool for Cervical Intraepithelial Neoplasia (Cin)," Gynecologic Oncology 52, 31-38 (1994). 30. S. K. Chang, Y. N. Mirabal, E. N. Atkinson, D. Cox, A. Malpica, M. Follen, and R. Richards- Kortum, "Combined reflectance and fluorescence spectroscopy for in vivo detection of cervical pre-cancer," Journal of Biomedical Optics 10, - (2005). 31. D. C. G. de Veld, M. Skurichina, M. J. H. Wities, R. P. W. Duin, H. J. C. M. Sterenborg, and J. L. N. Roodenburg, "Autofluorescence and diffuse reflectance spectroscopy for oral oncology," Lasers in Surgery and Medicine 36, 356-364 (2005). 32. C. M. Gardner, S. L. Jacques, and A. J. Welch, "Fluorescence spectroscopy of tissue: Recovery of intrinsic fluorescence from measured fluorescence," Applied Optics 35, 1780-1792 (1996). 33. J. W. Tunnell, A. E. Desjardins, L. Galindo, I. Georgakoudi, S. A. McGee, J. Mirkovic, M. G. Mueller, J. Nazemi, F. T. Nguyen, A. Wax, Q. Zhang, R. R. Dasari, and M. S. Feld, "Instrumentation for multi-modal spectroscopic diagnosis of epithelial dysplasia," Technol Cancer Res Treat 2, 505-514 (2003). 34. C. C. Yu, C. Lau, G. O'Donoghue, J. Mirkovic, S. McGee, L. Galindo, A. Elackattu, E. Stier, G. Grillone, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Quantitative spectroscopic imaging for non-invasive early cancer detection," Opt Express 16, 16227-16239 (2008). 35. K. T. Schomacker, T. M. Meese, C. Jiang, C. C. Abele, K. Dickson, S. T. Sum, and R. F. Flewelling, "Novel optical detection system for in vivo identification and localization of cervical intraepithelial neoplasia," J Biomed Opt 11, 34009 (2006).
69 5. Probe to imaging
In chapter 4 we reviewed the principles of quantitative spectroscopy (QS) and also
discussed examples of probe and imaging instruments for conducting optical spectroscopy. In
this chapter, we will describe the first instrument designed to conduct QS in a wide-area
imaging modality, which is the primary contribution of this thesis. First, we describe the
concept of a virtual probe and why it is important for quantitative spectroscopy. Second, we
describe the Quantitative Spectroscopic Imaging (QSI) system, which implements the virtual
probe for wide-area spectroscopic imaging.
5.1. Virtual probe
Previous implementations of spectroscopic imaging, particularly for reflectance
spectroscopy (eg. LUMA), used wide-area illumination[1-6]. A large area of tissue (several cm 2 )
was simultaneously illuminated and spectroscopy was conducted by using either multiple
narrowband illuminations or color-resolved detection. One reason this method of illumination
is very popular because spatially resolved detectors, such as charge coupled device (CCD) and
complementary metal oxide semiconductor (CMOS) cameras, have reached an advance stage.
As a result, wide-area illumination with spatially resolved detection enables rapid acquisition of
data across an area of tissue. In comparison, small-area illumination (like that of a contact-
probe) with spatially averaged detection (photodiode) requires scanning, which typically results
in slower data acquisition. However, wide-area illumination is not compatible with quantitative spectroscopy. In this section, we will explain the incompatibility of wide-area illumination and the concept of a virtual probe.
70 QS converts measured spectra into measurements of tissue structural and biochemical properties using physical models of light propagation and fluorescence in tissue[7-9]. This requires being able to independently measure scattering and absorption because tissue structural properties are primarily scatterers while biochemical properties are primarily fluorophores. A wide-area illumination spectroscopic imaging system is unable to do this. To demonstrate this deficiency, which we refer to as the scale-size problem, we numerically integrate the point spread function of Farrell et a/. (Eq. 4.3) over an infinitesimally small collection area and various diameters of delivery (illumination) areas for samples with different p,'[10]. The delivery and collection areas are concentric. The integral can be written as
RT = ifrrR(r,)dr, (5.1) rd (51 where rd is the radius of the delivery area. Figure 5.1 plots the computed reflectance, RT, as a function of the reduced scattering coefficient for various illumination diameters. The reflectance curves have been mean-centered to highlight their slopes. The absorption coefficient is fixed at 0.05mm 1. From the simulation, we see an instrument measuring tissue reflectivity with larger illumination area is less sensitive to changes in reduced scattering coefficient. As a result, the inversion procedures used by quantitative spectroscopy to extract pS'from the measured reflectance will produce more precise results if a smaller illumination area is used. Therefore, small-area illumination allows QS to more precisely measure reflectance and fluorescence tissue properties compared to using wide-area illumination. This is one of the reasons why the QSI system, to be described later in this chapter, uses small-area illumination and spatially resolved detection.
71 1.6 - rd = 0.5mm r = 1mm 1.4 d r d = 4mm 1.2 _ r = 7mm d - 1 -- rd = 10mm
0.8
0.6 0.8 0.9 1 1.1 1.2 1.3 (mm 1 ) S
Figure 5.1: Mean-centered reflectances computed using various illumination radii and reduced scattering coefficients. Mean-centered refers to dividing each reflectance value by the average reflectance value on that curve.
As Fig. 5.1 illustrates, small-area illumination is more appropriate for quantitative spectroscopy. Contact-probe instruments that have previously been used to conduct QS used small-area illumination[11, 12]. Unfortunately, a contact-probe is not amenable to QSI. Instead, a non-contact probe, which we will refer to as a "virtual probe," can be used to conduct imaging. The concept of a virtual probe involves using optics to relay illumination light to a small area (~ 1mm 2) on the tissue surface, the interrogation site. This is the same as the delivery area in Eq. 5.1 with a small value of rd. Light returning to the tissue surface within the collection area, which often overlaps with the delivery area, is relayed by optics to the spectrometer. A virtual probe is amenable to imaging because the interrogation site can be moved across the tissue surface by using a 2D scanning mirror to deflect the direction of the
72 probe or by using multiple virtual probes that are sequentially activated. The QSI system, to be
described in the next section, implements QSI using one virtual probe and a 2D scanning mirror.
A virtual probe offers an additional significant advantage over wide-area illumination for
spectroscopic imaging. This advantage applies to the broader field of optical spectroscopy, not just quantitative spectroscopy. Light entering and returning from tissue through a virtual probe
traverses a smaller volume of tissue than through a wide-area illumination instrument.
Therefore, the virtual probe has better "resolution" and can potentially detect smaller masses
of diseased tissue. The physical principles for this difference are similar to those separating
confocal microscopy from traditional brightfield microscopy[13]. In 2D confocal microscopy, a
small spot of light is scanned across a region of tissue and light returning from the same small
area is collected. This is very similar to a virtual probe. In brightfield microscopy, flood
illumination illuminates a region of tissue and light returning from each small area (typically
corresponding to the pixels of a CCD camera) is collected. This is very similar to wide-area
illumination spectroscopic imaging. Confocal microscopy has better resolution than brightfield
microscopy because much of the out-of-focus light, due to multiple scattering, is rejected by
the confocal setup. Similarly, a virtual probe rejects more of the multiply scattered light than
wide-area illumination, leading to a smaller sampling volume and superior "resolution."
5.2. QSI system
The previous section presented the resolution and precision advantages of using a
virtual probe to conduct quantitative spectroscopic imaging. In this section, we will describe the
instrument, the QSI system, which has been built to conduct QSI in the cervix in vivo. We will
73 begin by describing the hardware and software design of the instrument followed by a detailed description of calibration procedures and results. Much of these results have been published in the peer-reviewed paper of Yu et al.[7].
5.2.1. Hardware
The hardware of the QSI system has been developed for a clinical setting, and as a result must be portable. As shown in Fig. 5.2, portability is achieved by placing the heaviest components in a mobile cart and creating a separate light weight, mobile optical head to be operated by the physician. The design is modular. The three larger modules (1 - 3), mounted in a cart, are connected to the optical head (module 4) by means of optical fibers. Module 1, the light source module, consists of a 75W CW white light arc lamp (Simplicity series, Newport
Corp.) and a nitrogen laser that delivers 337nm light pulses of duration < 3.5ns and energy
175pJ at 20 Hz (NL100, Stanford Research Systems Inc.). Module 2 contains a spectrograph/CCD unit (Princeton Instruments Corp.). Module 3 is the computer, which controls data acquisition and contains algorithms to analyze the data (refer to chapter 4). National Instruments Labview software and DAQ data acquisition hardware are used to control and coordinate the various components. The software will be described in more detail later. Module 4 is mounted on an articulating arm for easy maneuvering. It is located on a smaller cart with wheels, so that the larger equipment cart can be located away from the patient, since space in hospital procedure rooms is limited.
74 Module 3
Comipurer Module 2 Module 4 Opti al fibers
ivD 'To patient C31 Sp~ectrograph
Module Arc L ampt Optical fibers
laser Fiber coupler
Figure 5.2: Modules of the QSI system.
White-light from the arc-lamp and UV light (337nm) from the laser are coupled into optical fibers and relayed to the optical head, shown in Fig. 5.3. The optical head is designed to implement the virtual probe spectroscopic imaging concept. For reflectance spectroscopy, the exit tip of the xenon lamp relay fiber (100pam diameter, 0.12 NA) is imaged 1:10 onto the tissue surface by an achromatic doublet lens to form a 1mm diameter delivery area, the interrogation site. The illumination light beam has a convergence angle of 0.70 and is deflected twice along the way, first by a 450 rod mirror and second by the 2D scanning mirror (OlM 102, Optics in
Motion LLC.). Light reflected by the tissue within a 2mm diameter collection area concentric with the delivery area is collected. This light passes through the beamsplitter, is deflected by the scanning mirror, passes through the dichroic mirror (reflects 337nm, transmits visible), and is coupled by eight achromatic lenses located in the aperture openings into eight collection
75 relay fibers (200tm diameter, 0.12 NA). Each lens is 1cm in diameter and is located
approximately 50cm from the tissue surface. Light is collected from eight different
backscattering directions to reduce the adverse effects of specular reflection. The collection
relay fibers deliver light to the spectrograph/CCD unit to be stored as spectra (400nm - 700nm).
For fluorescence spectroscopy, the exit tip of the laser relay fiber (100pim diameter, 0.22 NA) is
imaged 1:10 by a UV lens onto the same interrogation site as for RS. This illumination light
beam has a convergence angle of approximately 10 and is deflected twice along the way, first by
the dichroic mirror and second by the scanning mirror. Light emitted by the tissue within the
same 2mm diameter collection area as in RS is collected along the same optical path.
Collection fibers Enface view of Light from aperture non a Aperturel Color video CCD
Aod Firor Lens Light from laser -Dichroic mirror Zoom lens Lens 1 1------
2D scanning mirror m 9OTI1OR I Beamsplilter'
22. 5cm to patient
Figure 5.3: Schematic diagram of the optical head. For spectroscopic imaging, the interrogation site is raster scanned across a 2.1cm x
2.1cm region of the cervix in 1mm steps by the 2D scanning mirror for a total of 441 data
76 points. At each mirror position, a RS measurement is acquired, followed by a FS measurement.
Each set of measurements is associated with a tissue location, which correlates with the mirror
position. Approximately every one second during the scan, which corresponds to approximately
seven scan points, a photograph of the cervix is acquired using white-light LED illumination (CCS
Inc., not shown in figure) and a color video camera (QICAM, Qlmaging Corp.), The photographs
are used to correct for patient motion during the scan. The LED is turned off during reflectance
and fluorescence measurements. A more detailed description of the scanning process will be
given in the next section.
Figure 5.4 shows the operating screen of the QSI system. Whenever spectroscopy is not
being conducted, the system displays a live view of the cervix (10 frames/s) as seen by the color
video camera with LED illumination. This video display is very similar to that of a digital
colposcope and allows the clinician to properly position the instrument relative to the cervix.
For example, the field of view in Fig. 5.4 is centered on the os and the optical head is positioned
22.5cm away from the cervix. The optics of the video camera have a f-number of 5, which
means the live image will become noticeably defocused if the instrument-cervix separation is
more than a few millimeters off of 22.5cm. The magnification of the video view can be adjusted
using the "IX" (1:10 magnification), "2X" (1:20 magnification), and "16X" (1:160 magnification)
buttons. A still frame photograph can be acquired using the "Snapshot" button and this saves the most recent frame recorded by the video camera to memory. Also, the cervix can be viewed with blue or green filters, features found in many colposcopes, using the "Blue" and "Green" buttons. To begin data collection, the clinician presses the "Collect" button, which starts the acquisition cycle described in the next section.
77 x 2
Snapsho
Figure 5.4: Operating screen, or front panel, of the QSI system. The instrument is controlled by LabView hardware control software and the front panel is a virtual instrument (VI) in "run" mode.
5.2.2. Software
The previous section described the hardware of the QSI system. This section will describe the software and algorithms that control the instrument. In particular, we will focus on the scanning process and the analysis algorithms that are used after data acquisition is complete.
Figure 5.5 shows the timing diagram of the QSI system during a 2.1cm x 2.1cm scan. At time Os, the "Collect" button on the operating screen is pressed to start data acquisition. The following processes occur in order. 1) The LED and video camera acquisition are turned off. 2)
An idle time of approximately 50ms is required to initiate the LabView virtual instruments (VI).
3) The LED is turned on for 100ms and a photograph of the cervix is captured (100ms exposure)
78 and saved to memory (50ms). 4) A reflectance measurement is acquired in 100ms. 5) A
fluorescence measurement is acquired in 55ms. 6) The scanning mirror is moved to the next
position (5ms). 7) Steps 4 to 6 are repeated six additional times. 8) Steps 3 to 7 are repeated
until spectroscopic data has been acquired from all 441 points across the field of view. This
requires a total of approximately 80s.
LED onh Tr7 off Video camera
Save photograph onhn off 1] Reflectance measurement onh offE- 7 I[7 F T_ I Fluorescence measurement off n n n n n n Move mirror on off -I- 0 0.5 1 1.5 time (s)
Figure 5.5: High-level timing diagram illustrating the acquisition of reflectance and fluorescence spectra at one interrogation site. The on state indicates the process is active while the off state means the process is idle.
For each of the 441 reflectance measurements, the timing diagram of Fig. 5.6 shows the states of important components in the QSI system. During each reflectance measurement, the shutter is opened for 50ms to allow light from the arc-lamp to reach the tissue surface. This shutter is kept closed at all other times. While the shutter is opened, the spectrograph/CCD exposes for 50ms and the frame is saved in memory during the next 50ms. This cycle is repeated 441 times during a QSI scan.
79 Shutter
on -
Off
CCD exposure
on -
off"
Save spectra
on
-
-f0 0 50 100 150 time (ms)
Figure 5.6: A timing diagram illustrating the states of important components during a RS measurement. Time Oms is the beginning of a reflectance measurement in Fig. 5.5.
Similarly for the 441 fluorescence measurements, the timing diagram in Fig. 5.7 shows the states of fluorescence spectroscopy components during a scan. At the beginning of a fluorescence measurement in Fig. 5.5, the CCD exposes for 5ms. In the middle of this exposure period, one 3.5ns pulse from the UV laser excites the tissue. After the exposure, 50ms are required to write the frame to memory. This cycle, like that of RS, is repeated 441 times during a scan. The end result of a scan of the cervix is 441 reflectance CCD frames, 441 fluorescence frames, and approximately 60 photographs. Each cervix scan is also accompanied by a background scan and a normalization scan to account for background light and the instrument's color dependence. These will be described in more detail later in this section and in chapter 6.
80 CCD expose
on.
off
Laser pulse
on
Save spectra
on
time (ms)
Figure 5.7: A timing diagram illustrating the states of important components during a FS measurement. Time Oms is the beginning of a reflectance measurement in Fig. 5.5.
After the scan process is complete and spectra have been recorded from all 441 measurement sites on the cervix, the data is analyzed offline at MIT. Data analysis consists of several steps. 1) Each CCD frame is analyzed to extract and combine the eight spectra. 2) The 60 photographs of the cervix acquired at one second intervals are analyzed to determine patient motion relative to the instrument during the scan. 3) Reflectance and fluorescence spectra are fitted to the models described in chapter 4 to reduce the data order and extract direct measures of tissue properties, which we refer to as spectroscopy parameters, such as hemoglobin concentration. 4) The maps of spectroscopy parameters measured at all 441 sites are corrected to account for patient motion. The details of each of these steps are described below.
Spectral extraction
81 The first step involves converting each of the 882 CCD frames into eight spectra. Figure
5.8(a) shows the light energy (counts) recorded at each of the pixels on the CCD during one frame. The eight bands with higher counts than the background dark current corresponds to the spectra of light arriving from the eight collection relay fibers. The left side of the CCD records blue light around 400nm and the right side records red light around 700nm. The wavelength of each pixel is determined using the calibrated emission of a mercury-tungsten lamp (Ocean Optics, Inc.). Each band is converted into a spectrum by averaging the two most illuminated rows as shown in Fig. 5.8(b). These spectra couple the reflectance/fluorescence spectrum with the instrument's spectral response along with that of any background light.
During calibration and clinical measurements, these tissue-independent spectral dependences will be removed using normalization and background measurements as described later in this chapter and chapter 6.
x104
2 5 2
fiber45
fiber76
0
0.8
05 0A 10 20 30 4 0 5 0 60 70 30 0 j Pixel (a) (b)
Figure 5.8: (a) A frame recorded by the Princeton Instruments CCD during a cervix scan. The CCD has 512 x 512 pixels, but during data acquisition, every 10 x 6 group of pixels is binned into one pixel resulting in a 51 x 85 frame. The colorbar indicates the light energy
82 recorded by each pixel in units of counts. The dark current of this CCD generates approximately 5000 counts. (b) The counts of each fiber from the frame in (a). Fiber 1 is the top band in (a) and fiber 8 is the bottom band. Each spectrum was obtained by averaging the counts in the two most illuminated rows of pixels for that fiber.
Patient motion
After each frame is converted into eight spectra, the 60 photographs are analyzed to determine the patient's motion relative to the instrument during the scan. Each photograph records the cervix at a slightly different position relative to the instrument. We recover the transverse translation of the cervix (in the plane of the cervix) from a series of photographs using a technique called phase correlation[14]. Consider the two photographs of the same cervix taken at different points in time in Fig. 5.9. Phase correlation computes the pair dx
(horizontal shift) and dy (vertical shift) that best aligns Fig. 5.9(b) with Fig. 5.9(a). This is done by first computing the cross-correlation of the two photographs as shown in Eq. 5.2.
394 4 0 r(x, y) = Z Z= zj0Z10 I(x, yO) 12(x - xO, y - Yo), (5.2) x and y are the horizontal and vertical coordinates, in units of CCD pixels, of the correlated photograph, r. xo and yo are the horizontal and vertical coordinates, in units of pixels, of the original photographs, /1 and 12. All photographs are expressed in units of CCD counts. The video
CCD camera has a chip with 1394 x 1040 pixels, which determine the ranges of the summations.
Once r is computed, the offset of /1 from 12 (pixels) is given by dx = xmax - 697 and dy = ymax - 520, where xmax and Ymax are the coordinates of the pixel with maximum counts in r. To track the motion over an entire 80s scan, dx and dy are computed for images 2 through
60 relative to the first image and saved in memory. Phase correlation is a computationally
83 efficient process because the cross-correlation in Eq. 5.2 can be done rapidly using 2- dimensional fast fourier transforms.
(a) (b)
Figure 5.9: Two photographs of the cervix taken during a QSI scan by the video CCD. Notice the cervix in (b) is translated downwards relative to the cervix in (a).
Fitting
After patient motion during the scan is determined, the next step in data analysis is to fit all the reflectance and fluorescence spectra to the tissue light propagation and fluorescence models described in Ch. 4[8, 9, 15]. Each reflectance and fluorescence frame (Fig. 5.8(a)) results in eight spectra from the same tissue site (Fig. 5.8 (b)). For the reflectance frames, some of the spectra may be corrupted by specular reflection. We remove such spectra prior to further analysis by excluding all spectra with mean reflectance value from 400nm - 700nm greater than
0.2. The reflectance value is the number of counts measured from the sample divided by the number of counts measured from a reflectance spectralon after background light has been removed. Also, some of the collection directions may be blocked by foreign objects, such as the speculum. This causes the corresponding spectrum to have very low reflectivity values. We
84 remove such spectra by excluding all spectra with mean reflectance less than 0.05. For the fluorescence frames, we also remove spectra with low mean values, but since fluorescence measurements are not affected by specular reflection, there are no fluorescence spectra with high mean values. Once all corrupted spectra have been excluded, the remaining reflectance spectra are fit to the model of Zonios et al. (Eq. 4.9) to obtain the reflectance spectroscopy parameters A, B, [Hb], and a. Similarly, the remaining fluorescence spectra are processed using
Eq. 4.10 to obtain the intrinsic fluorescence spectra and MVCR (Eq. 4.11) is subsequently applied, resulting in the fluorescence spectroscopy parameters Coll and NADH. These are the amounts of collagen and NADH, in arbitrary units, contributing to the measured fluorescence emission after 337nm excitation by the QSI system. Once every spectrum in a frame has been fit to obtain several sets of spectroscopy parameters, the values of each parameter are averaged to obtain the final spectroscopy parameters for the frame. These are the spectroscopy parameters measured from the corresponding tissue site. After all frames have been analyzed, the spectroscopy parameters are combined to form six spectroscopy parameter maps showing the value of each parameter at each site on the cervix. Several example parameter maps and a photograph of the cervix are shown in Fig. 5.10.
85 (c) 17 A Hb concentration
1.3 12 0,5 12 04 1.2 03 10 0.2 (b) 21 (d) 01 2 0.9 5 1 5 11
1d 0.8
04 2 5 10 15 20
202 (a) Hb oxygenation NADH concentration (i 16 0.9 |4
9.8 12 10 )7 Collagen concentration 1(g) 06 2 60
0.5 50
0.4 8 46
30
20
10 (f)
5 10 15 20 '
Figure 5.10: (a) Photograph of the cervix. (b) A parameter map, in units of mm 1 , measured from the cervix in (a). The 21 x 21 pixels correspond to the 2.1cm x 2.1cm QSI scan area. Pixels that are completely black had all eight reflectance spectra in the frame corrupted. (c) B parameter map. (d) Hemoglobin concentration parameter map, in units of mg/mL. (e) Oxygen saturation parameter map. (f) Collagen parameter map, in A.U. (g) NADH parameter map, in A.U.
Motion compensation
The features in Fig. 5.10(b) align with the features in Fig. 5.10(a). This is the case because the parameter maps obtained from the steps described in section Fitting are subsequently motion compensated using dx and dy determined by phase correlation. To
86 accomplish this, recall there is one set of dx; and dy; for each photograph i. Since one photograph is taken for every seven interrogation sites, dx; and dy; represent the displacement of the cervix (from its position at the start of the scan) at the time of interrogation site 7i - 6.
The displacements for interrogation sites 7i - 5 to 7i are obtained by applying linear interpolation to all the dxi's and dy;'s. In the case of no motion, each interrogation site corresponds to a certain pixel on the first photograph. With motion, the spectroscopy parameters measured at each interrogation site are remapped to the appropriate pixel on the first photograph using the dx and dy of each interrogation site. This forms the motion compensated spectroscopy parameter maps, one of which is shown in Fig. 5.10(b).
5.2.3. Calibration
The spectroscopy parameter maps, such as the one in Fig. 5.10(b), are direct measurements of native tissue scattering, absorption, and fluorescence properties. However, to insure their accuracy, it is necessary to calibrate the QSI system on tissue phantoms with known optical properties. In this section we will present RS and FS calibration studies using phantoms of intralipid, hemoglobin, furan, and polystyrene spheres. Most of the results in this section have been published by Yu et al.[7], but we will present the experiments in more detail here.
Reflectance spectroscopy
Calibrating the QSI system to accurately measure reflectance spectroscopy parameters is a multi-step process. The first step is to determine rc' in Eqs. 4.5 and 4.6 and the second step is to test the instrument's accuracy on tissue phantoms with known scattering and absorption properties. We determine rc' using Eq. 4.3 in conjunction with Eq. 4.5. Equation 4.3 is used to
87 compute the blue reflectance spectrum in Fig. 5.11, assuming AD and Ac are concentric circles with 1mm and 2mm diameters, respectively. The scattering and absorption coefficients are
0 p,'(A) = 1.2(A/700nm)- .7s (A = 1.2mm-1, B = 0.75) and ga(A) = 0.5 (EHbO 2 () + EHb
([Hb] = 1.Omg/mL, a = 0.5), respectively. Eq. 4.5 is then fit to this spectrum using an objective function similar to Eq. 4.6, except here the optimization variable is rc' and the constraints are A
= 1.2mm 1, B = 1.0, [Hb] = 1.Omg/mL, and a = 0.5. The optimal fit spectrum is plotted in red on
Fig. 5.11. The optimal rc' for the QSI system is 0.69mm and this value is used in all subsequent fitting of reflectance spectra.
1.8 X
1.6
1.4
Q 1.2
1 -...... REq. 4.3 0.-REq. 4.5 0.8
0.6 40 500 600 700 Wavelength (nm)
Figure 5.11: Reflectance spectra computed using Eq. 4.3 (blue) and fitted to Eq. 4.5 (red). The two curves are almost perfectly overlapped.
Now that rc' has been determined, the next step is to test the QSI system's accuracy by
measuring tissue phantoms with known optical properties. The first phantom is a semi-infinite
88 water based solution of 1pm diameter polystyrene spheres (Polysciences, Inc.) with a particle concentration of 1.1 x 1010 spheres/mL. For this phantom, the reduced scattering coefficient can be computed with Mie theory[16]. We fit this scattering coefficient spectrum to the form of
Eq. 4.7 and obtain AMie = 2.23mm'1 and Bmie = 0.93. Next, the QSI system measures one reflectance spectrum from the phantom (sample), one reflectance spectrum from a neutral density filter (background), and one spectrum from a 99% reflective spectralon standard
(standard). The instrument-independent reflectance spectrum is defined as R(A) =
Rsampie-Rbackground . R is processed as in section Fitting. In the remainder of this thesis, a Rstandard-Rbackground measured reflectance spectrum will refer to the instrument independent spectrum. Only one spectrum, instead of 441, is measured because it is prohibitively expensive to make a large volume phantom of polystyrene spheres. The resulting values of A and B are 2.12mm 1 and
1.06, respectively. These are close to the computed values and this experiment shows the QSI system can accurately measure scattering.
The spheres experiment above has several limitations. It cannot confirm QSI's accuracy
over a range of scattering and absorption coefficients and the small phantom does not test the
instrument's entire field of view. To address these problems, we create nine large (greater than
2.1cm x 2.1cm), homogeneous, and semi-infinite phantoms consisting of all combinations of
10% intralipid (Fresenius Kabi AG) diluted with water and hemoglobin (Sigma Aldrich, Co.). The three dilution ratios (with corresponding mass concentrations in parentheses) for intralipid are
1:9 (1%), 2:8 (2%), and 3:7 (3%). The three concentrations of hemoglobin were 0.5, 1.0, and
1.5mg/mL. The QSI system measures reflectance spectra from 441 interrogation sites on all
89 nine phantoms. These spectra are fitted using Eqs. 4.5 and 4.9 to obtain the reflectance spectroscopy parameter maps. Table 5.1 shows the mean value and standard deviation of each parameter measured across its corresponding map from each phantom. Since the phantoms are exposed to air, the oxygen saturation is 1.0 and all the measurements came out close to
1.0. For hemoglobin concentration, we see the measured values are close to the prepared values and there is minimal variation across the field of view. For the A parameter (reduced scattering coefficient at AO), the measured values are proportional to the intralipid concentration, which is as expected because the scattering coefficient is proportional to scatterer concentration[17]. The measured B parameters vary with intralipid concentration, but we see they vary little with hemoglobin concentration, providing evidence that the QSI system
can independently measure tissue scattering and absorption properties.
Table 5.1: Reflectance parameters measured from tissue phantoms. The uncertainty of each parameter is the standard deviation of values measured from all 441 sites on the phantom. Phantoms labeled (a), (b), and (c) are included in Fig. 5.12.
Intralipid mass Hemoglobin [Hb] A (AO = 700nm) B (AO = 700nm) concentration concentration 1% (c) 0.5 mg/mL 0.53 0.01 mg/mL 1.13 0.01 mm 1 0.99 0.02 1% 1.0 mg/mL 0.96 0.01 mg/mL 1.13 0.01 mm' 1.00 0.02 1% 1.5 mg/mL 1.56 0.03 mg/mL 1.11 0.01 mm~1 1.02 0.02 2% 0.5 mg/mL 0.55 0.01 mg/mL 1.90 0.02 mm 1 1.32 0.02 2% (b) 1.0 mg/mL 1.04 0.01 mg/mL 1.86 0.02 mm' 1.36 0.02 2% 1.5 mg/mL 1.49 0.01 mg/mL 1.86 0.02 mm 1 1.40 0.02 3% 0.5 mg/mL 0.53 0.01 mg/mL 2.67 0.04 mm' 1.38 0.02 3% 1.0 mg/mL 1.05 0.01 mg/mL 2.63 0.04 mm' 1.45 0.02 3% (a) 1.5 mg/mL 1.50 0.01 mg/mL 2.61 0.03 mm' 1.48 0.02
Figure 5.12 shows several reflectance spectra measured from the phantoms along with their
corresponding fits. Notice the quality of fit is excellent. The results of these experiments
90 confirm the QSI system can accurately measure a physiologically relevant range of reflectance
spectroscopy parameters across a 2.1cm x 2.1cm field of view.
0.5-
0.45 -
U
On 0.35
0.10.3 0
0 0.45- U
0.2
ClU 0.15- U C- 4- 0.1 U
aD 0.05
Y50 460 450 500 550 600 650 700 750 Wavelength (nm)
Figure 5.12: Calibrated reflectance spectra (solid lines) measured from different tissue phantoms. The best fit spectra obtained using the approach of Zonios et al. are also plotted (dashed lines)[8}. The characteristic absorption bands of hemoglobin at 420nm, 540nm, and 580nm are clearly visible.
Fluorescence spectroscopy
Similar to the reflectance spectroscopy experiments, we perform a comparable set of
calibration experiments to determine the accuracy of fluorescence spectroscopy parameters.
The first step is to determine S and / in Eq. 4.10. We prepare seven phantoms consisting of
intralipid, hemoglobin, and furan (Lambda Physik). Furan is used as a fluorophore because it has a similar emission spectrum to collagen, one of the most important epithelial fluorophores at
337nm excitation[4, 18, 19]. The concentrations of each constituent are listed in table 5.2.
91 Table 5.2: Constituents of each phantom used to determine S and I in Eq. 4.10.
Phantom # 1 2 3 4 5 6 7 Intralipid mass 2% 1% 3% 2% 2% 2% 2% concentration Hemoglobin 0.5 0.5 0.5 0.25 1.0 0.5 0.5 concentration mg/mL mg/mL mg/mL mg/mL mg/mL mg/mL mg/mL Furan 0.25 0.25 0.25 0.25 0.25 0.125 0.5 concentration ptg/mL pg/mL pg/mL ptg/mL ig/mL ptg/mL ptg/mL
The QSI system is used to measure one reflectance and one fluorescence spectrum from each phantom and the fluorescence spectra are plotted in red on Fig. 5.13. Recall these furan emission spectra are confounded by turbidity due to the presence of intralipid and hemoglobin.
The QSI system is also used to measure emission spectra from each furan concentration in water alone (no intralipid or hemoglobin). This provides a direct measure of the intrinsic fluorescence spectra and these are plotted in blue on Fig. 5.13. Eq. 4.10 is then applied with
different values of S and / until the extracted intrinsic fluorescence spectra, which are plotted in
magenta on Fig. 5.13, best agree with the measured intrinsic spectra. As expected, the bulk
spectra vary with the intralipid and hemoglobin concentrations but the extracted intrinsic
spectra only vary significantly with furan concentration. Figure 5.13 shows that the agreement
between measured and extracted intrinsic spectra is good across a range of optical properties
and the optimal values of S and / are 0.000564 and 644Vm, respectively.
92 0
7- - Measured IFS - -. Bulk Fluorescence U- Extracted IFS $6
4
0 0 1 2 3 5
4 -
0
c2
0 . 0 ------
Figure 5.13: Bulk and intrinsic fluorescence spectra measured from seven tissue phantoms. The numbers 1 through 7 indicate the phantom (table 5.2) from which the measurements were taken. Each of the seven sets of measurements spans a spectral range of 400nm - 700nm. The wavelength range is not shown on the horizontal axis, but is identical for each of the seven sets.
Now that S and / have been determined, the next step is to measure a set of tissue
phantoms with known scattering, absorption, and fluorescence properties to determine the
accuracy with which the QSI system can measure fluorescence spectroscopy parameters. We
prepare six phantoms consisting of intralipid, hemoglobin, and furan. The concentrations of each constituent are listed in the first three columns of table 5.3. The QSI system is used to scan a 2.1cm x 2.1cm region of each phantom, the NDF (background), the 99% reflectance spectralon from the previous section, and a fluorescence spectralon (standard), resulting in 441 fluorescence spectra. The instrument independent fluorescence spectrum is the ratio
F(A) = Fsample-Fbackground . Each spectrum is processed using Eqs. Fstandard-Fbackground 4.10 and 4.11 to obtain the furan concentration. The 4th column of table 5.3 shows the mean furan concentration
93 measured from the 441 sites along with the standard deviation. Comparing columns three and
four, we see the QSI system can accurately measure fluorophore concentrations over a wide
field of view in the presence of confounding turbidity.
Table 5.3: Fluorescence parameters measured from tissue phantoms. The measured furan concentration for 1% intralipid has been normalized to 0.25 and all other measured concentrations are normalized by the same factor.
Intralipid mass Hemoglobin Prepared furan Measured furan concentration concentration concentration concentration 1% (a) 0.5 mg/mL 0.25 Lg/mL 0.25 0.004 pg/mL 2% (b) 0.5 mg/mL 0.25 Ig/mL 0.26 0.005 pg/mL 2% 1.0 mg/mL 0.25 pg/mL 0.23 0.004 pg/mL 2% 0.5 mg/mL 0.125 pg/mL 0.11 0.004 pg/mL 2% 0.5 mg/mL 0.5 pg/mL 0.47 0.006 pg/mL 3% (c) 0.5 mg/mL 0.25 pg/mL 0.26 0.005 pg/mL
Figure 5.14 plots with solid lines fluorescence spectra measured from three intralipid,
hemoglobin, and furan phantoms along with one spectrum from furan in water (no turbidity).
After applying Eq. 4.10 to obtain the intrinsic fluorescence spectra, we see the three spectra
measured from turbid phantoms (dashed lines) overlap the spectrum measured from furan in
water. This is the reason for QSI's accuracy. Eq. 4.10 is able to remove the confounding effects
of turbidity across a range of scattering and absorption properties. In the following chapter, fluorescence is due to multiple fluorophores, primarily collagen and NADH, and MVCR will be
needed to extract the concentrations of both tissue constituents.
94 2.5-
Cu L) AIFS cD 2-
001.5- ab
4.- 0
to
6-
950 400 450 s0 550 600 50 700 750 Wavelength (nm)
Figure 5.14: Bulk fluorescence spectra measured from different tissue phantoms (as indicated in table 5.3). The corresponding intrinsic fluorescence spectra extracted using the method of Miller et cal. are also plotted[9]. The dashed green spectrum is the fluorescence measured from furan in water without intralipid or hemoglobin present. It is nearly identical to the red, black, and blue dashed lines. Note that the IFS spectra and the spectrum of furan in water all overlap, as they should.
95 References
1. K. T. Schomacker, T. M. Meese, C. Jiang, C. C. Abele, K. Dickson, S. T. Sum, and R. F. Flewelling, "Novel optical detection system for in vivo identification and localization of cervical intraepithelial neoplasia," J Biomed Opt 11, 34009 (2006). 2. R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, "Imaging human epithelial properties with polarized light-scattering spectroscopy," Nat Med 7, 1245-1248 (2001). 3. R. J. Nordstrom, L. Burke, J. M. Niloff, and J. F. Myrtle, "Identification of cervical intraepithelial neoplasia (CIN) using UV-excited fluorescence and diffuse-reflectance tissue spectroscopy," Lasers in Surgery and Medicine 29, 118-127 (2001). 4. W. K. Huh, R. M. Cestero, F. A. Garcia, M. A. Gold, R. S. Guido, K. McIntyre-Seltman, D. M. Harper, L. Burke, S. T. Sum, R. F. Flewelling, and R. D. Alvarez, "Optical detection of high- grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study," Am J Obstet Gynecol 190, 1249-1257 (2004). 5. R. D. Alvarez, and T. C. Wright, "Effective cervical neoplasia detection with a novel optical detection system: a randomized trial," Gynecol Oncol 104, 281-289 (2007). 6. A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, D. Van Niekirk, E. N. Atkinson, N. Hadad, N. Mackinnon, C. Macaulay, R. Richards- Kortum, and M. Follen, "Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia," Gynecol Oncol 99, S67-75 (2005). 7. C. C. Yu, C. Lau, G. O'Donoghue, J. Mirkovic, S. McGee, L. Galindo, A. Elackattu, E. Stier, G. Grillone, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Quantitative spectroscopic imaging for non-invasive early cancer detection," Opt Express 16, 16227-16239 (2008). 8. G. Zonios, L. T. Perelman, V. M. Backman, R. Manoharan, M. Fitzmaurice, J. Van Dam, and M. S. Feld, "Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo," Applied Optics 38, 6628-6637 (1999). 9. M. G. Muller, I. Georgakoudi, Q. G. Zhang, J. Wu, and M. S. Feld, "Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption," Applied Optics 40, 4633-4646 (2001). 10. T. J. Farrell, M. S. Patterson, and B. Wilson, "A Diffusion-Theory Model of Spatially Resolved, Steady-State Diffuse Reflectance for the Noninvasive Determination of Tissue Optical- Properties Invivo," Medical Physics 19, 879-888 (1992). 11. J. W. Tunnell, A. E. Desjardins, L. Galindo, I. Georgakoudi, S. A. McGee, J. Mirkovic, M. G. Mueller, J. Nazemi, F. T. Nguyen, A. Wax, Q. Zhang, R. R. Dasari, and M. S. Feld, "Instrumentation for multi-modal spectroscopic diagnosis of epithelial dysplasia," Technol Cancer Res Treat 2, 505-514 (2003). 12. S. K. Chang, N. Marin, M. Follen, and R. Richards-Kortum, "Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia," J Biomed Opt 11, 024008 (2006). 13. C. Sheppard, Confocal Laser Scanning Microscopy (Springer, New York, 1997). 14. C. D. Kuglin, and D. C. Hines, "The Phase Correlation Image Alignment Method," in SPIE (International Conference Cybernetics and Society)(1975).
96 15. 1. Georgakoudi, E. E. Sheets, M. G. Muller, V. Backman, C. P. Crum, K. Bad izadegan, R. R. Dasari, and M. S. Feld, "Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo," Am J Obstet Gynecol 186, 374-382 (2002). 16. H. C. v. d. Hulst, Light Scattering by Small Particles (John Wiley & Sons, New York, 1957). 17. L. V. Wang, and H.-l. Wu, Biomedical Optics - Principles and Imaging (John Wiley & Sons, Hoboken, NJ, 2007). 18. S. McGee, J. Mirkovic, V. Mardirossian, A. Elackattu, C. C. Yu, S. Kabani, G. Gallagher, R. Pistey, L. Galindo, K. Badizadegan, Z. Wang, R. Dasari, M. S. Feld, and G. Grillone, "Model-based spectroscopic analysis of the oral cavity: impact of anatomy," J Biomed Opt 13, 064034 (2008). 19. R. Drezek, K. Sokolov, U. Utzinger, I. Boiko, A. Malpica, M. Follen, and R. Richards- Kortum, "Understanding the contributions of NADH and collagen to cervical tissue fluorescence spectra: modeling, measurements, and implications," J Biomed Opt 6, 385-396 (2001).
97 6. Clinical study
In chapter 5 we described the hardware, software, and calibration of the quantitative spectroscopic imaging (QSI) system that extends quantitative spectroscopy (QS) from a small area, contact-probe to wide area spectroscopic imaging. In this chapter we will present the procedures and results of our clinical study using QSI to detect cervical dysplasia in vivo. This study follows from the findings of Mirkovic et a/.[1, 2], which were summarized near the end of chapter 3. The objective is to calibrate the QSI system to distinguish regions of cervical transformation zone with high-grade squamous intraepithelial lesions (HSIL) from those without (non-HSIL) and compare the accuracy to that of expert colposcopy. Performing the distinction strictly within the transformation zone is important for proper comparision with colposcopy[2]. This study uses data from loop electrosurgical excision procedure (LEEP) patients to develop a diagnostic algorithm out of the measured spectroscopy parameters and data from colposcopy patients to prospectively evaluate the accuracy of QSI. We first describe the clinical procedures, such as those for patient recruitment, data acquisition, and data analysis. We then present the findings of the study. The chapter concludes by comparing the findings of this study to those of similar studies.
6.1. Procedures
In this section we detail the experimental procedures, which are divided into subsections. First, we describe our patient recruitment and regulatory protocols. Second, we describe data acquisition using the QSI system. Third, we present the pathology procedures used by our pathologists from the Boston Medical Center (BMC) and Brigham and Women's
98 Hospital (BWH). Fourth, we describe the data analysis procedures that convert spectroscopy
parameter maps into diagnostic maps. Fifth, we show how to use a portion of the data set to train QSI and the remaining data for prospectively evaluation. We finish this section by stating the data exclusion criteria.
6.1.1. Patient recruitment
The QSI system is used to examine patients referred for colposcopy or Loop
Electrosurgical Excision Procedure (LEEP) at the Boston Medical Center due to a prior abnormal
Pap smear or biopsy result, respectively. Pregnant women and those under 18 or over 65 years of age are not recruited for the study. When an eligible patient arrives at the hospital for her scheduled colposcopy or LEEP, a research coordinator invites the patient to join the study. The study is described in appropriate detail to the patient and she is informed of all her rights. If the patient is able to comprehend the study and agrees to join, she signs the research consent form, which documents the study and the patient's rights, and is led to the study room where the instrument is located. If the patient refuses to join the study or is unable to comprehend
(usually because the patient does not speak the language of the consent form), she is led to the procedure room and undergoes the scheduled LEEP or colposcopy. If at any time during or after data acquisition the patient decides to withdraw from the study, all data acquired from her is removed from the database. All aspects of the study were approved by the Institutional Review
Board (IRB) of the BMC and the Committee On the use of Humans as Experimental Subjects
(COUHES) of the Massachusetts Institute of Technology.
6.1.2. Data acquisition
99 At the beginning of a study session, standard and background measurements are taken, identical to those for the tissue phantoms in chapter 5. These will be used for all patients studied that day. Standardization is done by using the QSI system to scan a 99% reflectance spectralon and a fluorescence spectralon. Measurements from the reflectance standard are used to divide reflectance spectra measured from patients to account for the color dependence of the instrument and day to day instrument variations. Similarly, measurements from the fluorescence standard are divided into the fluorescence spectra to account for instrument color dependence and day to day variations. Background is accounted for by using the QSI system to scan an absorptive neutral density filter. This measurement is subtracted from the spectra measured from patients and both spectralons to account for room light and other forms of stray light. All standard and background measurements are completed before any patient enters the study room.
After a patient consents to the study, she is led into the study room. The patient is seated on the examination chair in the lithotomy position. The colposcopist then inserts the speculum into the patient's vagina to expose the cervix for visual examination. Five percent acetic acid is applied to the cervix using cotton-tipped applicators to remove residual mucous and initiate the acetowhitening effect. The QSI system is positioned at the correct orientation and distance (22.5 cm) from the patient by using the live video feed to put the cervix into focus and center the os in the field of view. QSI then examines a 2.1 cm x 2.1 cm area of the cervix in approximately 80s. The resulting 882 CCD frames and approximately 80 photographs are saved in memory. Once the scan is complete, the patient proceeds with her scheduled colposcopy or
LEEP.
100 After the procedure, any excised biopsy or LEEP specimens are placed in saline solution and transported from the clinic to the pathology laboratory. For LEEP specimens, the gynecologist uses four pins to mark the squamo-columnar junction at 12 o'clock, 3 o'clock, 6 o'clock, and 9 o'clock to help register pathology with spectroscopy. The meaning of these directions will be described in section 6.1.3.
All data saved during a clinic session, which may include measurements from multiple patients, is brought back to MIT and analyzed as described in chapter 5 to yield the six motion compensated parameter maps. The tissue specimens submitted to BMC pathology are evaluated per standard of care (described below) and the results are returned to researchers at
MIT as soon as they are available.
6.1.3. Pathology
Colposcopy and LEEP result in biopsy and LEEP specimens, respectively. For this study,
only tissue areas the colposcopist considered suspicious for dysplasia or cancer are biopsied.
Tissue specimens are submitted to the pathology department at BMC for standard
histopathological analysis. In the case of biopsies, specimens are sectioned, stained, and
evaluated per standard of care. Each specimen is classified as either HSIL or non-HSIL. The latter
includes non-dysplastic tissue and low-grade squamous intraepithelial lesions (LSIL). No invasive
cancer was observed in this study. LEEP involves removal of all areas of the cervix that are at
risk of HSIL. These specimens are processed differently from biopsies due to complexities
arising from size and orientation. After the specimen is excised from the patient, the location
on the cervix corresponding to the two ends of the specimen is recorded. Location is defined
101 using notation derived from the twelve hour hands of a clock and the squamous-columnar junction (SCJ), the border between endocervix and transformation zone. For example, 12 o'clock referred to tissue on a line extending from the cervical os (exit of the birth canal) to the edge of the cervix in a direction towards the top of the QSI system's field of view. Similarly, 3 o'clock is to the right of the field of view and 6 o'clock is towards the bottom. Upon submission to pathology, the specimen is cut into twelve pieces corresponding to the 12 clock directions per standard of care. From each piece a section extending from endocervix through the transformation zone to ectocervix is prepared for evaluation under a microscope. For each section, a pathologist records the extent and location of any HSIL present with millimeter precision. The end result is a map showing the location of HSIL on the entire specimen. This is converted to a disease map showing the location of HSIL on the cervix by identifying the SCJ on a photograph taken by the QSI system and marking the extent of HSIL at each of the 12 o'clock positions.
All biopsy and LEEP specimens are independently evaluated by three cervical pathologists using the procedures described above to form a consensus diagnosis, which is the gold standard of this study. For each biopsy, the consensus diagnosis is the diagnosis of at least two of the pathologists. As an example, if two pathologists diagnose HSIL and the third diagnoses non-HSIL, the consensus diagnosis is HSIL. For LEEP specimens, the on service pathologist examines all twelve sections to form the disease map and also chooses the sections with the most severe disease. These sections are evaluated by the two other pathologists to determine the most severe disease state present on the patient. The consensus diagnosis of most severe disease for the patient is that independently determined by at least two of the
102 pathologists. Only disease maps from patients where the lead pathologist's diagnosis agrees with the consensus is used to identify HSIL in the spectroscopy study. The remaining LEEP data is still used for other purposes, such as helping to identify ectocervix and endocervix.
6.1.4. Data analysis
The goal of this study is to compare the consensus disease map determined by pathology to the diagnostic map generated by QSI. To obtain a diagnostic map from measured quantitative spectroscopy parameter maps, four steps are required. Step one uses spectroscopy parameters and edge detection to identify the location of the endocervix. The subsequent search for HSIL will not be conducted in the endocervix. Step two uses spectroscopy parameters to identify a region of normal ectocervix tissue for normalizing the effects of patient-to-patient variation. This region will serve as an on-site standard. Step three takes spectroscopy parameters measured from the standard and uses them to normalize corresponding spectroscopy parameters measured from all other non-endocervix sites on the patient. Step four uses the normalized spectroscopy parameters to distinguish HSIL from non-
HSIL tissue in the transformation zone. Each of these steps is described in more detail below.
Step one identifies the endocervix. To identify the endocervix, we develop spectroscopic and blue-light contrast criteria for segmenting endocervix from other tissues. Endocervix can be recognized by an intense red color due to the superficial blood vessels located directly beneath the single layer of columnar epithelial cells. With quantitative spectroscopy, we search for regions on the cervix with reduced A and elevated Hb relative to neighboring tissue. A and Hb values from endocervix and transformation zone are inputted into Bayes' rule to compute the
103 posterior probability (PP) of endocervix[3]. For each patient we obtain a PP map of endocervix.
Pixels with PP higher than a threshold are classified as endocervix. Once spectroscopy has selected regions to be endocervix, a Canny edge detection algorithm[4] is applied to the blue light picture from the color CCD to identify sharp changes in intensity on the cervix. Regions of the cervix where both spectroscopy identifies as endocervix and edge detection detects a boundary are classified as endocervix.
Step two identifies a region of normal ectocervix suitable for normalizing the effects of patient-to-patient variation on spectroscopy parameters. Squamous epithelium in the ectocervix has minimal occurrence of disease and the spectroscopic signal is affected by many sources of interpatient variation. Therefore, normal ectocervix can serve as an internal standard for normalizing interpatient variation. To identify a suitable standard, we search for regions of each cervix with elevated A and Coll relative to the surrounding tissue. Once squamous tissue is identified, we identify spatially continuous areas larger than one pixel with homogeneous A and
Coll measurements and choose these areas as the standard. Requiring homogeneity reduces the likelihood of selecting ectocervix sites where measurement quality was poor. After identifying the area to be the standard, we compute the average values of each spectroscopy parameter in the area.
Step three uses the average parameter values identified in step two to normalize spectroscopy parameters measured from the transformation zone to reduce the effects of interpatient variation. Normalization for a patient involves dividing each spectroscopy
104 parameter measured from each pixel in the transformation zone by its corresponding average value in the standard to obtain the normalized spectroscopy parameter for that pixel.
Step four uses normalized spectroscopy parameters to identify HSIL regions. Normalized spectroscopy parameters measured from each tissue pixel are inputted into Bayes' Rule (similar to the procedure in step one) to compute the posterior probability of HSIL for that pixel. This results in a PP map of HSIL for each patient. Pixels with PP greater than the threshold can be classified as HSIL to yield a diagnostic map. However, such an approach treats each of the 441 pixels independently without considering the measurements in neighboring pixels. Dysplasia often extends continuously over several mm 2, and thus will span a region of pixels. We enhance the accuracy of dysplasia detection and suppress the effects of measurement artifacts by switching the diagnosis of a HSIL pixel to non-HSIL if all its neighbors are diagnosed non-HSIL.
Similarly, we switch a non-HSIL pixel to HSIL if its neighbors are HSIL. This preserves the spatially continuous stretches of high or low PP and alters the PP of isolated pixels to be more similar to that of its neighbors. The drawback of accounting for spatial continuity is a reduction in QSI's ability to detect small dysplasia, which has been traded for improved accuracy of detecting large dysplasia. The output of QSI is a diagnostic map showing areas with HSIL for each patient.
6.1.5. Training and evaluation
Data from the LEEP patients were chosen to form a training set. Training is performed in three steps, corresponding to the steps of diagnosis described above that require training (one, two, and four).
105 In training step one, we select endocervix tissue from the training set using visual criteria, as in colposcopy. From pathology, we identify the transformation zone. We subsequently identify the differences in A and Hb spectroscopy parameters that distinguish transformation zone from endocervix and train the likelihood functions in Bayes' rule to compute the PP of endocervix given a spectroscopy measurement. We estimate the necessary prior probabilities of endocervix and non-endocervix by determining the area fraction of tissue in the training patients that are endocervix. The threshold is set by using the PP to identify endocervix in each training patient.
In training step two, we select normal squamous epithelium in the training set from pathology information and compare spectroscopy parameters measured from squamous to those measured from transformation zone. The A and Coll spectroscopy parameters are used to train the likelihood functions in Bayes' rule to compute the PP of ectocervix given a spectroscopy measurement. We estimate the necessary prior probabilities of ectocervix and non-ectocervix by determining the area fraction of tissue in the training patients that are ectocervix. The thresholds for spectroscopy determination and homogeneity are set by using step two in data analysis to identify ectocervix in each training patient.
In training step three, we select transformation zone tissue sites on the training patients with and without HSIL based on pathology. We then identify normalized spectroscopy parameters that distinguish HSIL from non-HSIL and train the likelihood functions in Bayes' rule to compute the posterior probabilities of HSIL. We estimate the necessary prior probabilities of
HSIL and non-HSIL by determining the area fraction of transformation zone tissue in the LEEP
106 patients with and without HSIL. Once the PP maps are computed for the training patients, we identify a suitable threshold to distinguish HSIL from non-HSIL.
After the four step diagnostic algorithm has been trained, we evaluate it retrospectively on the LEEP patients and prospectively on the colposcopy patients. For LEEP patients, we compare QSI's diagnosis in each quadrant with the consensus pathology diagnosis from that quadrant. For colposcopy patients, we compare QSI's diagnosis at each biopsy site with that of consensus pathology.
6.1.6. Exclusion criteria
Not all data that is collected can be used in the study. Below are the key exclusion criteria that are used to filter out data before any analysis is performed.
" LEEP or biopsy specimens with no pathology diagnosis. This occurs if the tissue specimen
was damaged prior to histopathological analysis.
" LEEP patients where the on service pathologist's diagnosis of the most severe disease state
disagreed with the consensus diagnosis. However, these patients are still available for
training steps one and two of the diagnostic algorithm (subsection 6.1.4).
* Spectroscopy measurements from tissue sites corrupted by specular reflection, blood, or
foreign objects (eg. intrauterine device).
* Biopsy sites not in the QSI system's field of view, which occur if the field of view is not
centered on the os or the transformation zone is receded within the os (common amongst
older patients).
6.2. Results
107 Using the procedures described above, a total of 34 LEEP patients and 47 colposcopy patients were recruited for the study period from August 2007 to January 2009. The patient pool consisted of 43 self-identified blacks, 19 white, 16 Hispanic/Latino, 2 Asian, and 1 other.
Their ages had a mean of 31.3 years and a standard deviation of 9.7 years. Twenty of the patients had referral cytology of ASCUS (refer to chapter 2), three had ASCH, 36 had LSIL, 18 had HSIL, and four had other. The colposcopy patients yielded 67 biopsies (16 HSIL, 51 non-
HSIL). Due to the requirements of consensus pathology, only data from 25 of the LEEP patients are used to train QSI to identify HSIL, but all 30 LEEP patients with spectroscopy measurements are used to identify endocervix and ectocervix (several of the LEEP patients early in the study were lost due to measurement error).
Training steps one and two train QSI using spectroscopy parameters from the LEEP patients to identify endocervix and ectocervix, respectively. Table 6.1 lists the means and standard deviations of the A, [Hb], and other spectroscopy parameters measured from endocervical and transformation zone tissue. Notice scattering is lower and absorption due to hemoglobin is higher in endocervix, which allows QSI to identify the endocervix (step 1).
Table 6.1: Mean and standard deviation of spectroscopy parameters measured from the endocervix and transformation zones of LEEP patients.
A{mm~) B [HbJ a Coll (A.U.) NADH (mg/mL) (A.U.) Endocervix 0.83 0.12 0.18 0.11 16.7 3.84 0.59 0.11 9.66 16.4 6.88 9.76 Transformation 0.96 0.12 0.56 0.24 8.68 5.52 0.53 0.12 11.5 26.5 5.98 11.1 zone
Similarly, table 6.2 lists the means and standard deviations of the A, Coll, and other spectroscopy parameters measured from ectocervical tissue. Ectocervix has higher scattering
108 and collagen content than transformation zone (Table 6.1) and this is used to identify a region of normal squamous for normalizing spectroscopy parameters (step 2).
Table 6.2: Mean and standard deviation of spectroscopy parameters measured from the ectocervix of LEEP patients.
A (mm 1 ) B [Hb] a Col (A.U.) NADH (A.U .) (mg/mi) Ectocervix 1.11 0.14 0.73 0.21 5.85 2.13 0.57 0.14 10.4 11.7 8.55 9.23
The values in tables 6.1 and 6.2 are used with Bayes' rule in diagnostic steps 1 and 2 to compute the probability of a certain being endocervix or ectocervix, respectively.
Spectroscopy measurements from the LEEP patients are also used to design and train diagnostic steps 3 and 4. Figure 6.1 shows spectroscopy parameters measured from HSIL and non-HSIL sites (determined by pathology) in the transformation zones of the LEEP patients. The medians of the groups for all parameters are very similar and the differences in medians are considerably less than the spreads of the groups. None of the spectroscopy parameters individually or in combination exhibit significant difference between HSIL and non-HSIL.
Therefore, based on these measurements alone, QSI has little ability to detect HSIL.
109 (a) (b) - 20 (c)
11 08- 15 -
E 0 6 -... E 03 E10 0.8 04 -
5 07- _L- 02 0.6 HSIL nori-HS IL HSIL non-HSIL HSIL non-HSIL
0.8 50 30 (d) T (e) T + (f) T 0.7- 40- 25 f 20 - 0.6- 30- 15- Z 0.5- 20 - o 10 0.4 0 10. z 0.3- 5 0. 0.2 - 0 - HSIL non-HSIL HSIL non-HSIL HSIL non-HSIL
Figure 6.1: Spectroscopy parameters measured from HSIL and non-HSIL sites in the transformation zones of the LEEP patients. The red lines are the medians of the data and the blue boxes span the 2 5 th to 7 5 th percentiles. The black lines span the extent of the data and the red crosses are outliers.
One possible explanation for the similarities in spectroscopy parameters measured from the two groups is normal patient-to-patient variation overwhelms HSIL related changes. To help avoid this, we perform per-patient normalization on each spectroscopy parameter map as described in diagnostic step 3. Figure 6.2 shows the normalized parameters measured from the
LEEP patients. Normalized parameters are denoted with a subscript n next to the regular parameter symbol (eg. An). An is significantly different between the two groups but none of the other parameters exhibit much change in contrast after normalization. Normalization improves the contrast between HSIL and non-HSIL and it turns out An alone best distinguishes HSIL from non-HSIL in the transformation zone and the addition of other parameters is insignificant.
Consequently, step 4 of the diagnostic algorithm will only use An. If a threshold of An = 0.83 is set on Fig. 6.2(a), HSIL can be retrospectively distinguished from non-HSIL with 89% sensitivity
110 and 83% specificity. This compares favorably with published values for colposcopy (sensitivity
65%, specificity ~ 52%), although these studies are typically prospective evaluations on much larger data sets and may be subject to interobserver variability[5, 6]. It is also important to note that published values describing the accuracy of colposcopy vary considerably and are highly dependent on the gold standard for determining whether a patient is truly free of disease and the threshold used by the colposcopist to determine if a biopsy is necessary. Also, the clinical threshold used to determine the need for biopsy, such as SIL or HSIL, affects colposcopy's accuracy.
(a) (b) (c) 2 1-- 5 1 2. 5 -- 0.9- -- P 2-
08- 0 6 1. 5
0.71 0 4
0.6 -6 - 0 2 0 HSIL non-HSIL HSIL non-HSIL HSIL non-HSIL 1.6 3 I 5
(d) (e) -- (f) 1.4- 2. T 4 1.2- 2
0 1. 5- 0 2 I 4- 0.8 0. 5 0.6. 0 0 HSIL non-HS IL HSIL non-HSIL H SIL non-HSIL
Figure 6.2: Normalized spectroscopy parameters measured from HSIL and non-HSIL sites in the transformation zones of the LEEP patients. The spectroscopy measurements used in this figure are identical to those used in Fig. 6.1.
Figure 6.3 shows the four step diagnostic algorithm of QSI applied retrospectively on two LEEP patients. In Fig. 6.3(a), a region of HSIL from approximately 8 o'clock to 12 o'clock is
111 diagnosed by pathology. QSI detects a similar sized region with HSIL. In Fig. 6.3(b), a region of
HSIL from approximately 9 o'clock to 2 o'clock is diagnosed. Again, QSI detects a similar sized region with HSIL. These examples show QSI is able to retrospectively determine the location of
HSIL on the cervix with accuracy. This is not surprising considering 89% sensitivity and 83% specificity was obtained from the training data selected from these same LEEP patients.
(a) (b)
Figure 6.3: Photographs of two LEEP cervices overlaid with imaging and pathology's diagnoses. The red regions are tissue areas identified retrospectively by the TMS imaging system to have HSIL using the four step diagnostic algorithm. The dashed white lines indicate a region with HSIL according to pathology.
The retrospective evaluations shown in Fig. 6.2 and Fig. 6.3 were obtained from the same data used to train QSI to identify HSIL. In order to ensure reproducibility, the four step algorithm is applied prospectively to the colposcopy patients to determine if QSI can reduce the number of unnecessary biopsies. Figure 6.4 shows QSI's diagnosis for two colposcopy patients along with the location and disease state of colposcopically determined biopsies. For the
112 patient in Fig. 6.4(a), QSI detects an extensive region of HSIL. This diagnosis accurately determined the need for a biopsy at approximately 10 o'clock. However, we cannot evaluate
QSI's accuracy at sites where no biopsies were taken. In Fig. 6.4(b), QSI detects no HSIL and would have saved this patient one unnecessary biopsy. However, we cannot determine if QSI missed HSIL at other sites.
AA
(a) (b)
Figure 6.4: Photographs of two colposcopy cervices overlaid with imaging and pathology's diagnoses. The red regions are tissue areas identified prospectively by the QSI system to have HSIL using the four step diagnostic algorithm. QSI did not identify any HSIL in (b). The white circle in (a) is the site of a HSIL positive biopsy (according to pathology). The green circle in (b) is the site of a HSIL negative biopsy.
We apply QSI's four step diagnostic algorithm on all of the colposcopy patients and recorded the number of instances QSI correctly and incorrectly determined the need for a biopsy. These results are summarized in table 6.3. We see the number of true positives and negatives greatly outnumber the false positives and negatives, which suggests QSI is accurate.
The sensitivity to detect HSIL is 81% and the specificity to detect non-HSIL is 78%. To further
113 quantify the accuracy, we see that of the 24 biopsies deemed necessary by the combination of colposcopy and QSI (true positives + false positives), 13/24 or 54% were truly HSIL. In comparison, a study by Massad et al. found colposcopic impression of HSIL is only HSIL
(49+77+16+70)/(418+170) or 36% of the time[7]. This prospective evaluation shows QSI used as
an adjunct to colposcopy has the potential to improve upon the accuracy of colposcopy alone
for detecting HSIL in cervical transformation zone.
Table 6.3: A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 13 true positives, 11 false positives, 3 false negatives, and 40 true negatives. The two-sided 95% confidence intervals are given in brackets. HSIL pathology non-HSIL pathology HSIL spectroscopy 13 (10 - 16) 11 (6 - 17) non-HSIL 3 (0 - 6) 40 (34 - 45) spectroscopy
6.3. Discussion
In this clinical study we used the quantitative spectroscopic imaging system described in
chapter 5 to measure reflectance and fluorescence spectra from patients referred for
colposcopy and loop electrosurgical excision procedure. The primary clinical goal is to
distinguish HSIL from non-HSIL in cervical transformation zone. Comparing spectroscopy
parameters measured from HSIL and non-HSIL sites (according to pathology) on the
transformation zones of LEEP patients, we found the two groups can be distinguished using a
per-patient normalized version of the reduced scattering coefficient, A,. The addition of other
parameters made little difference. This is in agreement with the findings of our contact probe
study[2], which strengthens the likelihood the results are reproducible. To use this finding to
detect HSIL in patients, we developed a four step diagnostic algorithm that identifies
114 transformation zone by eliminating other tissue types, selects a region of ectocervix for normalization, and thresholds the An parameter map to identify HSIL. We trained the four step algorithm using data from the LEEP patients and found QSI was able to retrospectively map the location of HSIL on the cervix. We next applied QSI prospectively on the colposcopy patients to determine if the biopsies directed by colposcopy alone were necessary. The prospective results show QSI with colposcopy more accurately detects HSIL than colposcopy alone and may reduce the number of unnecessary biopsies if detecting HSIL is the clinical target. Therefore, QSI has the potential to make cervical cancer screening and diagnosis a less invasive procedure.
The QSI clinical study produced two key findings. The first is the reduced scattering coefficient alone can distinguish HSIL from non-HSIL in the transformation zone and the second is QSI can prospectively reduce the number of unnecessary biopsies. In chapter 3 we reviewed some the findings of cervical reflectance and fluorescence spectroscopy studies conducted by other research groups[8-14]. These findings appear to be different from our findings. Below we will discuss possible reasons for this difference.
QSI's study concluded that measuring scattering was key to detecting HSIL and the addition of other parameters, particularly absorption and fluorescence measurements, added little to the diagnostic ability. However, other optical spectroscopy studies, such as those reviewed in chapter 3, concluded that reflectance and fluorescence spectroscopy measurements were crucial for detecting HSIL. There are several possible explanations for the discrepancy.
(1) Many studies used spectroscopy to distinguish between HSIL and other tissue
conditions, with the latter including benign and normal tissues in both transformation zone
115 and ectocervix. In the QSI study, the non-HSIL group only included transformation zone sites.
This difference is probably the most significant reason for the need of absorption and
fluorescence measurements observed in other studies. According to Mirkovic et al.[1], there
are significant differences in scattering, absorption, and fluorescence spectroscopy
measurements between normal transformation zone and ectocervix. Therefore, if disease
free ectocervix measurements are included in a training set to develop a diagnostic
algorithm to identify HSIL, absorption and fluorescence will be important measurements
because HSILs are almost exclusively found in transformation zone. Effectively, the
absorption and fluorescence measurements show the difference between transformation
zone and ectocervix, not HSIL and less severe conditions in the cervix.
(2) QSI uses light propagation and fluorescence models, described in chapter 4, to
independently measure scattering, absorption, and fluorescence. The majority of studies did
not use a model-based approach and cannot isolate the scattering contribution in their
reflectance and fluorescence measurements, and thus cannot observe scattering's
importance. For example, Nordstrom et al. observed that low-grade (LSIL) and high-grade
squamous intraepithelial leasions can be distinguished from squamous metaplasia (normal
transformation zone) using reflectance spectroscopy measurements[1O]. However, without a
model-based approach, they could not state whether scattering, absorption, or both
contributed to the diagnosis.
(3) Even though this study found A, was key to identifying HSIL in the transformation zone,
diagnostic steps 1 through 4 used other spectroscopy parameters, including absorption and
fluorescence parameters, to identify endocervix and ectocervix. Therefore, scattering,
116 absorption, and fluorescence measurements are all important if QSI is used to automatically
identify HSIL.
In addition to discovering the importance of normalization and the reduced scattering coefficient, the QSI study is also one of the few prospective evaluations of optical spectroscopy.
To the best of our knowledge, the only other published prospective evaluation was that of
Alvarez et al.[11] They found that the optical detection system (ODS) used as an adjunct to colposcopy detected 27% more HSIL than colposcopy alone amongst patients referred for colposcopy with ASCUS or LSIL cytology. However, this came at the expense of a 33% increase in the number of biopsies required and it is known that colposcopy is more sensitive when more biopsies are taken[6, 15]. In contrast, the QSI study determined that spectroscopic imaging used as an adjunct to colposcopy can reduce the number of unnecessary biopsies by
65% (assuming HSIL is the biopsy threshold) and possibly without significantly affecting HSIL detection in the long-term due to the frequency of screening[16] and the slow progression of cervical cancer[17]. In future studies, we can also evaluate QSI's ability to increase HSIL detection, although this will require upgrading the QSI system to provide a diagnosis in real- time and use that to determine biopsy sites.
The importance of the reduced scattering coefficient alone suggests a significantly simpler optical instrument can be built to distinguish HSIL from non-HSIL in cervical transformation zone. This idea will be discussed further in the next chapter.
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1. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: impact of anatomy," (2008). 2. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: development of a diagnostic algorithm," (2008). 3. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, Singapore, 2006). 4. J. Canny, "A Computational Approach to Edge-Detection," leee Transactions on Pattern Analysis and Machine Intelligence 8, 679-698 (1986). 5. J. T. Cox, M. Schiffman, and D. Solomon, "Prospective follow-up suggests similar risk of subsequent cervical intraepithelial neoplasia grade 2 or 3 among women with cervical intraepithelial neoplasia grade 1 or negative colposcopy and directed biopsy," Am J Obstet Gynecol 188, 1406-1412 (2003). 6. R. G. Pretorius, W. H. Zhang, J. L. Belinson, M. N. Huang, L. Y. Wu, X. Zhang, and Y. L. Qiao, "Colposcopically directed biopsy, random cervical biopsy, and endocervical curettage in the diagnosis of cervical intraepithelial neoplasia I or worse," Am J Obstet Gynecol 191, 430-434 (2004). 7. L. S. Massad, and Y. C. Collins, "Strength of correlations between colposcopic impression and biopsy histology," Gynecol Oncol 89, 424-428 (2003). 8. I. Georgakoudi, E. E. Sheets, M. G. Muller, V. Backman, C. P. Crum, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo," Am J Obstet Gynecol 186, 374-382 (2002). 9. S. K. Chang, Y. N. Mirabal, E. N. Atkinson, D. Cox, A. Malpica, M. Follen, and R. Richards-Kortum, "Combined reflectance and fluorescence spectroscopy for in vivo detection of cervical pre-cancer," Journal of Biomedical Optics 10, - (2005). 10. R. J. Nordstrom, L. Burke, J. M. Niloff, and J. F. Myrtle, "Identification of cervical intraepithelial neoplasia (CIN) using UV-excited fluorescence and diffuse-reflectance tissue spectroscopy," Lasers in Surgery and Medicine 29, 118-127 (2001). 11. R. D. Alvarez, and T. C. Wright, "Effective cervical neoplasia detection with a novel optical detection system: a randomized trial," Gynecol Oncol 104, 281-289 (2007). 12. W. K. Huh, R. M. Cestero, F. A. Garcia, M. A. Gold, R. S. Guido, K. McIntyre-Seltman, D. M. Harper, L. Burke, S. T. Sum, R. F. Flewelling, and R. D. Alvarez, "Optical detection of high-grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study," Am J Obstet Gynecol 190, 1249-1257 (2004). 13. J. R. Mourant, T. J. Bocklage, T. M. Powers, H. M. Greene, K. L. Bullock, L. R. Marr-Lyon, M. H. Dorin, A. G. Waxman, M. M. Zsemlye, and H. 0. Smith, "In vivo light scattering measurements for detection of precancerous conditions of the cervix," Gynecol Oncol 105, 439-445 (2007). 14. C. Balas, "A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix," leee Transactions on Biomedical Engineering 48, 96-104 (2001). 15. J. C. Gage, V. W. Hanson, K. Abbey, S. Dippery, S. Gardner, J. Kubota, M. Schiffman, D. Solomon, and J. Jeronimo, "Number of cervical biopsies and sensitivity of colposcopy," Obstet Gynecol 108, 264- 272 (2006). 16. "Screening for cervical cancer: recommendations and rationale," (US Preventive Services Task Force, 2003), www.ahrq.gov.
118 17. M. Goldstein, A. Goodman, M. d. Carmen, and D. Wilber, "Case 10-2009 - A 23 year old woman with an abnormal Papanicoloau Smear," New England Journal of Medicine 360 (2009).
119 7. Application - simple instrument
In the introduction of this thesis we discussed the prevalence of cervical cancer in developing regions of the world where women do not have access to established screening
(Pap smear) and diagnosis (colposcopy + biopsy) programs. These are typically low-resource
settings where implementing the conventional technology used in countries like the United
States is not practical[1]. Therefore, cervical cancer detection technology for developing regions
should be low-cost and easy to use. Cost and complexity are important issues in cervical cancer
detection and treatment.
The clinical study conducted using the quantitative spectroscopic imaging (QSI) system
in chapter 6 found that measuring the normalized reduced scattering coefficient at 700nm is
important for distinguishing high-grade squamous intraepithelial lesions (HSIL) from non-HSIL in
the transformation zone. This finding facilitates the development of a simple and cost-effective
optical instrument to detect HSIL, which is very important for cervical cancer prevention. Tissue
reflectance at the red and near-infrared (near-IR) wavelengths is dictated by scattering.
Absorption is minimal in this region of the electromagnetic spectrum, which is often called the
optical window. For example, using Eqs. 4.7 and 4.8, we find the reduced scattering coefficient
is approximately two orders of magnitude larger than the absorption coefficient for typical
values of A, B, [Hb], and a at 700nm. Therefore, tissue reflectance is uniquely related to the
reduced scattering coefficient. In other words, the reflectance measured from tissue
illuminated by red/near-IR light is equivalent to the reduced scattering coefficient and the A
parameter can be measured by a single monochromatic reflectance measurement. Using the
120 diffuse reflectance model of Eq. 4.5 and setting p, to zero, reflectance is related to the reduced
scattering coefficient in the optical window by Eq. 7.1.
1 (1+ A) [ (7.1) R =2 - - 1+s'rc, 1+ A+src
where the meanings of all symbols are defined in chapter 4. Note A in Eq. 7.1 is different from
the A spectroscopy parameter discussed in this chapter. To detect HSIL, R needs to be
zone and ectocervix to compute Rn = RTzone, which measured from sites in the transformation Recto
is diagnostically equivalent to An.
We use Eq. 7.1 to convert the LEEP training data in Fig. 6.2(a) from A, to R, by
converting A parameters measured from ectocervix and transformation zone in to the R scale.
Figure 7.1 shows the same boxplots in the Rn scale. Since Eq. 7.1 is a monotonic function that
uniquely maps pi'into R, these boxplots are nearly identical. The main difference is a relative
vertical shift of the two plots. The equivalent Rn threshold to obtain 89% sensitivity and 83%
specificity (refer to chapter 6) is 0.76. In comparison, the An threshold is 0.83. The two
thresholds are similar because the reduced scattering coefficient spans a small range in cervical
tissue and thus, Eq. 7.1 is close to linear. The Rn threshold will be used by a simple optical
instrument that uses one red/near-IR wavelength to measure reflectance to distinguish HSIL
from non-HSIL in the transformation zone.
121 1.2 -
0.8 -
0.6-
04 + 0.4 I HSIL nonHSIL
Figure 7.1: Rn values converted from An values in Fig. 6.2(a) using Eq. 7.1. The red line is the median and the blue box spans the 25 th and 7 5th percentiles. The black lines span the extent of the data and the red crosses are outliers.
Since Rn can be used to detect HSIL, a simple and inexpensive instrument can be built around a red wavelength laser diode, a photodiode, and a digital display. To conduct a measurement, acetic acid is first applied to the cervix. Next, light from the red laser illuminates a small region of the cervix, the delivery area. Light backscattered from a region near the delivery area (collection area) is collected by the photodiode. The power recorded by the photodiode is shown on the digital display. This number is proportional to tissue reflectance.
Measurements are first performed on transformation zone sites at risk of HSIL to record
RTzone's. Next, one measurement is acquired from the normal ectocervix to obtain Recto for normalization. The divisions are conducted using either a built-in logic circuit or a calculator. If any of the resulting Rn's are less than 0.76, the corresponding site has HSIL. A wide-area
122 imaging version of this instrument can also be conducted by using a 2D scanning mirror, like that in the QSI system, to raster scan the delivery and collection areas across the cervix.
Figure 7.2 shows a schematic of the different components of the basic instrument. Light from the red laser is focused by a lens onto the tissue surface. Light returning from within the area spanned by the 1.5mm diameter aperture is collected by the lens and relayed to a photodiode. The use of a polarizing beamsplitter cube avoids complications from specular
reflection.
Aperture Lens (f = 3cm) BS 655nm laser
3cm
Photodiode
Figure 7.2: Schematics of key components in the basic instrument. BS indicates a polarizing beamsplitter cube. The 1.5mm aperture is placed in contact with the tissue.
The basic instrument that examines only several transformation zone sites can probably
be built for significantly lower costs than a digital colposcope since the components are
inexpensive and the design is simple. Also, the instrument is relatively easy to use and is
probably suitable for low-resource settings, especially if the costs can be made comparable to
those of visual inspection with acetic acid. However, because only several sites are examined,
there is increased likelihood of missing HSIL. The wide-area imaging version will likely be more
expensive and complicated because a computer is needed to control the scanning mirror and
record measurements from the photodiode. Also, additional measurements may be required to
123 automatically identify transformation zone, like those used in QSI's four step diagnostic algorithm. However, imaging will not be prone to undersampling.
124 Reference
1. N. Thekkek, and R. Richards-Kortum, "Optical imaging for cervical cancer detection: solutions for a continuing global problem," Nat Rev Cancer 8, 725-731 (2008).
125 8. Conclusion
Cervical cancer is the second most common cancer amongst women in the world, with most of the mortalities occurring in low-resource developing countries[1]. The screening and diagnosis technology currently used in developed countries such as the United States, namely
Pap smear, colposcopy, and biopsy, are not suited to these regions[2]. In the past decade, there has been an extensive push to develop optical spectroscopy technologies to improve screening/diagnosis in the United States and around the world[3-15]. Most of these studies used contact-probe systems capable of only examining a small area of tissue. Also, little model- based analysis was performed and typically retrospective evaluation was the end point for determining accuracy.
In this thesis, we developed and evaluated quantitative spectroscopic imaging (QSI) for detecting cervical dysplasia[16]. QSI implements quantitative spectroscopy (QS) and is thus able to directly measure native tissue properties. Furthermore, wide-area imaging allows complete coverage of the cervix, unlike a contact-probe, to minimize the likelihood of missing dysplasia. A number of difficult technical challenges were overcome to reach this stage, such as the scale- size problem. After developing the QSI system, we used it to study patients referred for colposcopy and loop electrosurgical excision procedure (LEEP) with the objective of distinguishing high-grade squamous intraepithelial lesions (HSIL) from non-HSILs in the transformation zone. The retrospective results showed only the normalized reduced scattering coefficient (An) was needed to perform this distinction with 89% sensitivity and 83% specificity and the addition of other parameters made little difference. However, we noted in chapter 6
126 this does not mean absorption and fluorescence measurements are unimportant. Using An and a four step diagnostic algorithm, we evaluated QSI prospectively and found that when used in combination with colposcopy, the pair demonstrated superior accuracy to colposcopy alone when detecting HSIL.
The promising prospective results along with the importance of A, suggests a simpler optical instrument can be designed to identify HSIL. The concept is based on using a red/near- infrared wavelength laser to illuminate tissue and a photodiode to collect the backscattered light power, which is displayed on a digital meter. The power is proportional to reflectance, which is equivalent to measuring the reduced scattering coefficient because tissue has relatively low absorption in the optical window[17]. This instrument can take contact-probe or wide-area imaging forms and may be suitable for use in low-resource setting because of its simplicity and low costs.
The findings of this thesis suggest several future research directions.
(1) Build several of the simple probe instruments described in chapter 7 and use them as
adjuncts to colposcopy at hospitals in the United States to reduce the number of
unnecessary biopsies, just like the clinical study described in this thesis. If the reducing
biopsy study is successful, apply for approval to conduct a guiding biopsy study. In a guiding
biopsy study, the probe will determine where biopsies are required. If studies in the US are
successful, the probe should be tested as an adjunct or alternative to visual inspection with
acetic acid (VIA) in a low-resource setting.
127 (2) Design and construct the simple imaging instrument (chapter 7) to acquire data from the
cervix in vivo. If the instrument is to be used as an automatic tool to detect HSIL, methods
will be needed to identify endocervix and ectocervix as is done in the four step diagnostic
algorithm of chapter 6. The spectroscopic parameters used by the QSI system will likely not
be available since the simple instrument only uses red light. One possibility is to combine
the instrument with a digital colposcope and perform image analysis on the colposcope
picture to identify endocervix and ectocervix. More research is needed to determine if this
is possible, although automated digital colposcopy has been investigated and found to be
potentially useful for identifying HSIL[18].
(3) Develop a faster version of the QSI system capable of acquiring data from a 2cm x 2cm
region with 1mm steps in under one second, rather than the eighty seconds currently
required. Such a system would offer scientific and technical advantages. A fast system can
study reflectance and fluorescence temporal changes in the cervical epithelium in response
to acetic acid or other contrast agents, such as Lugol's solution. This may provide additional
diagnostic information[19, 20]. A fast system may also reduce the occurrence of
"unsatisfactory colposcopy," where parts of the transformation zone are receded within the
os and cannot be visualized, leading to an increased likelihood of missing dysplasia and
cancer. This is accomplished by using a manipulator, such as a cotton swab, to expose
receded regions of the transformation zone for a spectroscopic scan. Since the swab cannot
be held motionless for long periods of time, a fast system is required.
(4) The QSI system illuminates tissue and collects returning light through air. Epithelial cancers
occur in many parts of the body beyond the access of such a configuration, for example, the
128 colon and esophagus. In these organs, an endoscopic implementation of QSI is required.
One idea being pursued by the spectroscopy laboratory involves using the current QSI design to illuminate the proximal end of an imaging fiber bundle. The fiber relays the illumination area, spatial orientation intact, to the distal end, which is placed close to the tissue surface. Light returning from the tissue is collected by all channels of the fiber bundle, but only light exiting at the proximal end from the channels surrounding the originally illuminated channels is collected and dispersed. In essence, the imaging fiber bundle has replaced air as the medium of light propagation. A QSI endoscope can be used to image epithelial dysplasia in a variety of organs, including colon, esophagus, and larynx.
129 References
1. W. H. Organization, "Cervical Cancer Screening in Developing Countries," (World Health Organization, Geneva, 2002). 2. N. Thekkek, and R. Richards-Kortum, "Optical imaging for cervical cancer detection: solutions for a continuing global problem," Nat Rev Cancer 8, 725-731 (2008). 3. S. K. Chang, N. Marin, M. Follen, and R. Richards-Kortum, "Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia," J Biomed Opt 11, 024008 (2006). 4. S. K. Chang, Y. N. Mirabal, E. N. Atkinson, D. Cox, A. Malpica, M. Follen, and R. Richards- Kortum, "Combined reflectance and fluorescence spectroscopy for in vivo detection of cervical pre-cancer," Journal of Biomedical Optics 10, - (2005). 5. 1. Georgakoudi, E. E. Sheets, M. G. Muller, V. Backman, C. P. Crum, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo," Am J Obstet Gynecol 186, 374-382 (2002). 6. J. R. Mourant, T. J. Bocklage, T. M. Powers, H. M. Greene, K. L. Bullock, L. R. Marr-Lyon, M. H. Dorin, A. G. Waxman, M. M. Zsemlye, and H. 0. Smith, "In vivo light scattering measurements for detection of precancerous conditions of the cervix," Gynecol Oncol 105, 439- 445 (2007). 7. W. K. Huh, R. M. Cestero, F. A. Garcia, M. A. Gold, R. S. Guido, K. McIntyre-Seltman, D. M. Harper, L. Burke, S. T. Sum, R. F. Flewelling, and R. D. Alvarez, "Optical detection of high- grade cervical intraepithelial neoplasia in vivo: results of a 604-patient study," Am J Obstet Gynecol 190, 1249-1257 (2004). 8. R. D. Alvarez, and T. C. Wright, "Effective cervical neoplasia detection with a novel optical detection system: a randomized trial," Gynecol Oncol 104, 281-289 (2007). 9. N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, A. Malpica, T. Wright, N. Atkinson, and R. Richards-Kortum, "Spectroscopic diagnosis of cervical intraepithelial neoplasia (CIN) in vivo using laser-induced fluorescence spectra at multiple excitation wavelengths," Lasers Surg Med 19, 63-74 (1996). 10. N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Thomsen, E. Silva, and R. Richardskortum, "Fluorescence Spectroscopy - a Diagnostic-Tool for Cervical Intraepithelial Neoplasia (Cin)," Gynecologic Oncology 52, 31-38 (1994). 11. N. Ramanujam, M. F. Mitchell, A. Mahadevan, S. Warren, S. Thomsen, E. Silva, and R. Richards-Kortum, "In vivo diagnosis of cervical intraepithelial neoplasia using 337-nm-excited laser-induced fluorescence," Proc Natl Acad Sci U S A 91, 10193-10197 (1994). 12. U. Utzinger, D. L. Heintzelman, A. Mahadevan-Jansen, A. Malpica, M. Follen, and R. Richards-Kortum, "Near-infrared Raman spectroscopy for in vivo detection of cervical precancers," Applied Spectroscopy 55, 955-959 (2001). 13. R. J. Nordstrom, L. Burke, J. M. Niloff, and J. F. Myrtle, "Identification of cervical intraepithelial neoplasia (CIN) using UV-excited fluorescence and diffuse-reflectance tissue spectroscopy," Lasers in Surgery and Medicine 29, 118-127 (2001).
130 14. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. I. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: impact of anatomy," (2008). 15. J. Mirkovic, C. Lau, S. McGee, C. C. Yu, E. Stier, C. Crum, A. d. 1. Morenas, L. Galindo, J. Nazemi, V. Feng, C. McGahan, D. Schust, K. Badizadegan, R. Dasari, and M. Feld, "Quantitative spectroscopy for detection of cervical dysplasia in vivo: development of a diagnostic algorithm," (2008). 16. C. C. Yu, C. Lau, G. O'Donoghue, J. Mirkovic, S. McGee, L. Galindo, A. Elackattu, E. Stier, G. Grillone, K. Badizadegan, R. R. Dasari, and M. S. Feld, "Quantitative spectroscopic imaging for non-invasive early cancer detection," Opt Express 16, 16227-16239 (2008). 17. T. Vo-Dinh, Biomedical Photonics Handbook (CRC Press LCC, Boca Raton, FL, 2002). 18. S. Y. Park, M. Follen, A. Milbourne, H. Rhodes, A. Malpica, N. MacKinnon, C. MacAulay, M. K. Markey, and R. Richards-Kortum, "Automated image analysis of digital colposcopy for the detection of cervical neoplasia," J Biomed Opt 13, 014029 (2008). 19. C. Balas, "A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix," leee Transactions on Biomedical Engineering 48, 96-104 (2001). 20. T. T. Wu, T. H. Cheung, S. F. Yim, and J. Y. Qu, "Optical imaging of cervical precancerous lesions based on active stereo vision and motion tracking," Opt Express 16, 11224-11230 (2008).
131 Appendix A: SIL vs. non-SIL
The primary clinical goal of this thesis is to train and evaluate quantitative spectroscopic imaging's (QSI) ability to distinguish high-grade squamous intraepithelial lesions (HSIL) from non-HSILs in cervical transformation zone. This distinction is clinically important because HSIL and cancer require treatment while LSIL and benign conditions do not. However, another goal of interest is distinguishing squamous intraepithelial lesions (SIL) from non-SILs in transformation zone. This has clinical importance because LSIL and HSIL require closer observation or immediate treatment, respectively, while non-dysplastic conditions require no special attention[1]. In this appendix, we repeat the procedures described in chapter 6 to train and evaluate QSI's ability to distinguish SILs from non-SILs.
A.1. Procedures
Since distinguishing SIL from non-SIL uses the same data from loop electrosurgical excision procedure (LEEP) and colposcopy patients presented in chapter 6, the procedures are very similar and we will only highlight the differences. First, pathology categorizes LEEP and biopsy specimens as SIL or non-SIL rather than HSIL or non-HSIL. Second, training step three is performed by selecting transformation zone sites on the LEEP patients with SIL and those with non-SIL. Third, the resulting four step diagnostic algorithm is applied to the biopsy sites from the colposcopy patients to prospectively evaluate QSL. Lastly, we also retrospectively evaluate
QSI's ability to detect SIL by plotting spectroscopy parameters measured from SIL and non-SIL biopsy sites and presenting the optimal diagnostic algorithm.
A.2. Results
132 Figure A.1 shows spectroscopy parameters measured from SIL and non-SIL sites on the
LEEP patients. Similar to Fig. 6.1 in chapter 6, there is little difference between the two groups and this suggests QSI is not able to accurately detect SIL.
4 ".20 L 1. IT 30. 4_
13 _L the b bt2x 10u s
O.B j-. 5- 10 0.7-4 F1gure A.:Tre pctocp paaeesmaurdfo n n-IL stsi h 0.6 0 SIL non-SIL SIL non-SIL SIL non-SIL (a) (b) (C)
Figure A.1: Three spectroscopy parameters measured from SIL and non-SIL sites in the transformation zones of the LEEP patients. The red lines are the medians of the data and the blue boxes span the 25 th to 7 5th percentiles. The black lines span the extent of the data and the red crosses are outliers.
Similar to our HSIL study, we per-patient normalize the data (refer to chapter 6) to see if accounting for some of the effects of patient-to-patient variation improves the contrast between SIL and non-SIL. Figure A.2 shows the normalized spectroscopy parameters measured from the same sites. Similar to the case of HSIL versus non-HSIL, the normalized A parameter
(A) best distinguishes SIL from non-SIL and the addition of other parameters individually or in combination is insignificant. This result is not surprising considering LEEP patients have high occurrence of HSIL due to triage and consequently, the SIL data group consists largely of HSIL. If we set a threshold of An = 0.83, QSI has 70% sensitivity and 84% specificity for retrospectively distinguishing SIL from non-SIL in the transformation zone.
133 1.1 3.53
1 q 2 0 92.5 I2
S108 15
0.7 1
0.6 0.5 -- 0.5 0 SIL non-SIL SIL non-SIL SIL non-SIL (a) (b) (c)
Figure A.2: Three normalized spectroscopy parameters measured from SIL and non-SIL sites in the transformation zones of the LEEP patients.
Using the A, threshold obtained from the LEEP patients, we train and prospectively apply the four step diagnostic algorithm on the colposcopy patients at each biopsy site. Table
A.1 shows QSI's predictions of SIL or non-SIL compared to those of pathology, the gold standard. There are a total of 40 SIL and 27 non-SIL biopsies. Of these, QSI correctly identified
17 (true positives) of the SILs and 18 (true negatives) of the non-SILs. The accuracy is fairly low and as a result, the prospective test shows the algorithm based on A, developed with the LEEP patients is not reproducible.
Table A.1: A 2x2 accuracy table showing the number of SIL and non-SIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 17 true positives, 9 false positives, 23 false negatives, and 18 true negatives. The two-sided 95% confidence intervals are given in brackets.
SIL pathology non-SIL pathology SIL spectroscopy 17 (10 - 24) 9 (3 - 15) non-SIL spectroscopy 23 (16 - 30) 18 (12 - 24)
One possible reason for the discrepancy between spectroscopic parameters measured from LEEP and colposcopy patients is the abundance of HSIL in the SIL group of LEEP patients while the SIL group of colposcopy patients is more evenly balanced between HSIL and LSIL. In clinical practice, QSI is much more likely to be applied on colposcopy patients. To address this,
134 Figure A.3 plots regular and normalized spectroscopy parameters measured from the SIL and non-SIL biopsy sites. We see none of the parameters individually distinguishes SIL from non-SIL with significant accuracy. Using logistic regression, which uses spectroscopy parameters to compute the probability of SIL (similar to Bayes' Rule), we also find no combination of parameters shows significant ability to perform the distinction[2]. The combination of all six regular parameters yields 70% sensitivity and 59% specificity if the threshold probability is set to 0.56 while the combination of An and an yields 58% sensitivity and 63% specificity using a threshold of 0.63.
25 .4- (a) (b) 40 - (c) T 20 2, 30- E CO 15 E 20 -n10 0
5 0 10
SIL no n-SIL SIL non-SIL SIL non-SIL
2. 5 2 T (d) (e) (f) 2-
5 -F M 1.5
0 .7 7-I 05 0 0.
SJL non-SIL SIL non-SIL SW non-SIL
Figure A.3: Regular (a, b, c) and normalized (d, e, f) spectroscopy parameters measured from SIL and non-SIL biopsy sites on colposcopy patients.
A.3. Discussion
We have trained and evaluated QSI to distinguish SIL from non-SIL in cervical transformation zone. Spectroscopy measurements from SIL and non-SIL sites on LEEP patients
135 were used to train QSI's four step diagnostic algorithm. The algorithm was then applied prospectively to the colposcopy patients. Unlike the similar HSIL study presented in chapter 6,
QSI did not demonstrate good ability to distinguish the two groups. We then used measurements from only the biopsy sites and found no regular or normalized spectroscopy parameter individually or in combination was able to perform the distinction with accuracy.
This is consistent with our contact-probe study[3].
One possible reason for QSI's inability to identify SIL given that it can accurately identify
HSIL is that LSIL pathology suffers from greater interobserver disagreement than HSIL pathology[4]. In other words, the gold standard used for SIL is not as consistent as that for HSIL.
In future studies, a more consistent gold standard for diagnosing LSIL should be used. Also, training and prospective evaluation should both be done with colposcopy patients to ensure the amounts of HSIL and LSIL in the training and evaluation sets are similar.
136 References
1. "ACOG Practice Bulletin: clinical management guidelines for obstetrician-gynecologists. Number 45, August 2003. Cervical cytology screening (replaces committee opinion 152, March 1995)," Obstet Gynecol 102, 417-427 (2003). 2. A. Agresti, Categorical Data Analysis (Wiley-Interscience, New York, 2002). 3. J. Mirkovic, "Quantitative Spectroscopy for Detection of Cervical Dysplasia," in Health Sciences and Technology(Massachusetts Institute of Technology, Cambridge, 2009). 4. M. H. Stoler, and M. Schiffman, "Interobserver reproducibility of cervical cytologic and histologic interpretations: realistic estimates from the ASCUS-LSIL Triage Study," JAMA 285, 1500-1505 (2001).
137 Appendix B: Independent versus correlated diagnoses
In chapter 6, we described the four step diagnostic algorithm. The fourth step distinguished HSIL from non-HSIL in cervical transformation zone using information from adjacent pixels to help determine the diagnosis of the pixel of interest. We refer to such a method of pairwise comparison as a correlated diagnosis, where the diagnosis QSI gives to one pixel is correlated to that of nearby pixels using a weighted average. Conversely, an independent diagnosis generates a diagnosis at each pixel using spectroscopic measurements from that pixel only. Correlated diagnosis is potentially beneficial because HSIL is a spatially continuous disease that can span several of the 1mm x 1mm pixels. This aided the accurate mapping of HSIL in the LEEP patients of Fig. 6.3 by removing incorrect diagnoses due to measurement artifacts. However, if pixels of the imaging system are close to or larger than the size of dysplasia present on the patient, correlated diagnosis may weaken QSI's ability to detect small areas of dysplasia. In this appendix, we apply the four step diagnostic algorithm with and without correlated diagnosis on the colposcopy patients to determine which method allows QSI to more accurately reduce unnecessary biopsy.
B.1. Independent diagnosis
To evaluate the accuracy of independent diagnosis for detecting HSIL, we modify step four of the diagnostic algorithm by applying the threshold to the posterior probability (PP) map and simply using the resulting binary map as the diagnostic map. The diagnosis at adjacent pixels does not influence the diagnosis of the pixel of interest. This independent four step
138 algorithm is applied to the colposcopy patients. Table B.1 tabulates the diagnosis of QSI at each biopsy site and compares it to that of pathology.
Table B.1: A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 14 true positives, 16 false positives, 2 false negatives, and 35 true negatives. The two-sided 95% confidence intervals are given in brackets.
HSIL pathology non-HSIL pathology HSIL spectroscopy 14 (10 - 16) 16 (9 - 24)
sprscpy 2(0-6) 35(27-42)
B.2. Correlated diagnosis
To evaluate the accuracy of correlated diagnosis for detecting HSIL, we again apply the four step diagnostic algorithm presented in chapter 6 to spectroscopy parameter maps measured from the colposcopy patients. However, in this appendix we use a more general technique for weighing the contribution of neighboring pixels to the diagnosis at the pixel of interest. Once the threshold is applied to the PP map to obtain the binary map where HSIL pixels have value of 1 and non-HSIL pixels are 0, the binary map is convolved with a symmetric
2-dimensional Gaussian function. The Gaussian function has standard deviation equal to /2 pixels (each pixel is 1mm on the tissue) and is computed at each of the 441 pixels in QSI's field of view. The resulting convolution is threshold at 0.5 such that pixels greater than 0.5 are HSIL and those less than 0.5 are non-HSIL. This is the correlated diagnostic map. Unlike the correlation method in chapter 6 that only considered the diagnosis at adjacent pixels, the continuous Gaussian function gives some weight to the diagnosis of distant pixels.
139 The four step algorithm with the correlated diagnostic map is applied to the colposcopy patients. QSI's diagnosis at each of the biopsy sites is compared to that of pathology and the results are given in table B.2.
Table B.2: A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies accurately detected by spectroscopic imaging. There are 10 true positives, 12 false positives, 6 false negatives, and 39 true negatives. The two-sided 95% confidence intervals are given in brackets.
HSIL pathology non-HSIL pathology HSIL spectroscopy 10 (5 - 15) 12 (5 - 19) non-HSIL spectroscopy 6(1-11) 39 (32-46)
B.3. Discussion
Comparing the results in tables B.1 and B.2, we see correlated diagnosis has slightly higher specificity than independent diagnosis but significantly lower sensitivity for detecting
HSIL. This is different from the results obtained when correlated diagnosis was applied to the
LEEP patients retrospectively in chapter 6. On LEEP patients, as illustrated by Fig. 6.3, using information from neighboring pixels improves QSI's ability to accurately distinguish HSIL from non-HSIL in transformation zone. One reason for this difference is HSIL in colposcopy patients may be similar to or smaller than 1mm 2 (the size of a QSI pixel) in size while HSIL in LEEP patients is larger. As discussed earlier in this appendix, correlated diagnosis may not be an advantage if HSIL is similar to or smaller than one of QSI's pixels. Therefore, correlated diagnosis for detecting HSIL is beneficial in LEEP patients but possibly less effective in colposcopy patients.
140 In future studies, correlated diagnosis should be used if the disease is typically larger than the pixel size or resolution of the imaging system. However, if the disease is small or pixels are large, independent diagnosis will probably be more accurate.
141 List of figures
2.1 A diagram of the female pelvis showing the location of the cervix relative to other organs. The cervix is approximately 2cm in diameter, but it can expand considerably, along with the vagina and endocervical canal, during child birth. This figure is replicated from Ferris et aI.[1]
2.2 White-light photograph of the cervix acquired by a digital colposcope (CooperSurgical, Inc.) after the application of 5% acetic acid. A speculum, the shadow of which can be seen at the top of the figure, has been put in place to open the vagina and permit visualization.
2.3 Hematoxylin and eosin (H&E) stained sections of (a) columnar epithelium and (b) endocervical stroma recorded under magnification. The black arrow in (b) points to an endocervical gland. The dark spots in both pictures are cell nuclei. This figure is replicated from Ferris et al.[1]
2.4 H&E stained section of stratified squamous epithelium obtained under magnification.
2.5 (a) and (b) are hematoxylin and eosin stained sections of tissue undergoing squamous metaplasia observed under magnification. The black arrows in (a) mark the appearance of reserve cells. This figure is replicated from Ferris et al.[1]
2.6 White-light photograph of a cervix with extensive acetowhite regions. 5% acetic acid had been applied to the cervix. Histopathology obtained after the colposcopic examination showed the patient had HSIL.
2.7 H&E stained section of adenocarcinoma in situ observed under magnification. The black arrows indicate multiple layers of columnar cells, which is not found in normal glandular epithelium. This figure is replicated from Ferris et al.[1]
2.8 H&E stained sections of (a) LSIL and (b) HSIL observed undermagnification. (a) The triangles point to koilocytes. (b) The arrows indicate the intact basement membrane.
2.9 A typical colposcope with key components indicated. This figure is adapted from www.leisegang.de.
2.10 Flow chart showing cervical cancer management guidelines in the United States. This chart only presents a portion of all guidelines.
142 5.1 Mean-centered reflectances computed using various illumination radii and reduced scattering coefficients. Mean-centered refers to dividing each reflectance value by the average reflectance value on that curve.
5.2 Modules of the QSI system.
5.3 Schematic diagram of the optical head.
5.4 Operating screen, or front panel, of the QSI system. The instrument is controlled by LabView hardware control software and the front panel is a VI in "run" mode.
5.5 High-level timing diagram illustrating the acquisition of reflectance and fluorescence spectra at one interrogation site. The on state indicates the process is active while the off state means the process is idle.
5.6 A timing diagram illustrating the states of important components during a RS measurement. Time Oms is the beginning of a reflectance measurement in Fig. 5.5.
5.7 A timing diagram illustrating the states of important components during a FS measurement. Time Oms is the beginning of a reflectance measurement in Fig. 5.5.
5.8 (a) A frame recorded by the Princeton Instruments CCD during a cervix scan. The CCD has 512 x 512 pixels, but during data acquisition, every 10 x 6 group of pixels is binned into one pixel resulting in a 51 x 85 frame. The colorbar indicates the light energy recorded by each pixel in units of counts. The dark current of this CCD generates approximately 5000 counts. (b) The counts of each fiber from the frame in (a). Fiber 1 is the top band in (a) and fiber 8 is the bottom band. Each spectrum was obtained by averaging the counts in the two most illuminated rows of pixels for that fiber.
5.9 Two photographs of the cervix taken during a QSI scan by the video CCD. Notice the cervix in (b) is translated downwards relative to the cervix in (a).
5.10 (a) Photograph of the cervix. (b) A parameter map, in units of mm', measured from the cervix in (a). The 21 x 21 pixels correspond to the 2.1cm x 2.1cm QSI scan area. Pixels that are completely black had all eight reflectance spectra in the frame corrupted.
5.11 Reflectance spectra computed using Eq. 4.3 (blue) and fitted to Eq. 4.5 (red). The two curves are almost perfectly overlapped.
5.12 Calibrated reflectance spectra (solid lines) measured from different tissue phantoms. The best fit spectra obtained using the approach of Zonios et al. are also plotted (dashed lines)[8]. The characteristic absorption bands of hemoglobin at 420nm, 540nm, and 580nm are clearly visible.
143 5.13 Bulk and intrinsic fluorescence spectra measured from seven tissue phantoms. The numbers 1 through 7 indicate the phantom (table 5.2) from which the measurements were taken. Each of the seven sets of measurements spans a spectral range of 400nm - 700nm. The wavelength range is not shown on the horizontal axis, but is identical for each of the seven sets.
5.14 Bulk fluorescence spectra measured from different tissue phantoms (as indicated in Table 5.3). The corresponding intrinsic fluorescence spectra extracted using the method of MUller et al. are also plotted[9]. The dashed green spectrum is the fluorescence measured from furan in water without intralipid or hemoglobin present. It is nearly identical to the red, black, and blue dashed lines. Note that the IFS spectra and the spectrum of furan in water all overlap, as they should.
6.1 Three spectroscopy parameters measured from HSIL and non-HSIL sites in the transformation zones of the LEEP patients. The red lines are the medians of the data and
the blue boxes span the 2 5 th to 75 th percentiles. The black lines span the extent of the data and the red crosses are outliers.
6.2 Three normalized spectroscopy parameters measured from HSIL and non-HSIL sites in the transformation zones of the LEEP patients. The spectroscopy measurements used in this figure are identical to those used in Fig. 6.1.
6.3 Photographs of two LEEP cervices overlaid with imaging and pathology's diagnoses. The red regions are tissue areas identified retrospectively by the TMS imaging system to have HSIL using the four step diagnostic algorithm. The dashed white lines indicate a region with HSIL according to pathology.
6.4 Photographs of two colposcopy cervices overlaid with imaging and pathology's diagnoses. The red regions are tissue areas identified prospectively by the QSI system to have HSIL using the four step diagnostic algorithm. QSI did not identify any HSIL in (b). The white circle in (a) is the site of a HSIL positive biopsy (according to pathology). The green circle in (b) is the site of a HSIL negative biopsy.
7.1 Rn values converted from An values in Fig. 6.2(a) using Eq. 7.1. The red line is the median and the blue box spans the 25th and 75 h percentiles. The black lines span the extent of the data and the red crosses are outliers.
7.2 Schematics of key components in the basic instrument. BS indicates a polarizing beamsplitter cube. The 1.5mm aperture is placed in contact with the tissue.
144 A.1 Three spectroscopy parameters measured from SIL and non-SIL sites in the transformation zones of the LEEP patients. The red lines are the medians of the data and the blue boxes span the 25th to 75th percentiles. The black lines span the extent of the data and the red crosses are outliers.
A.2 Three normalized spectroscopy parameters measured from SIL and non-SIL sites in the transformation zones of the LEEP patients.
A.3 Regular (a, b, c) and normalized (d, e, f) spectroscopy parameters measured from SIL and non-SIL biopsy sites on colposcopy patients.
145 List of tables
2.1 Bethesda System categories for positive cytology results.
5.1 Reflectance parameters measured from tissue phantoms. The uncertainty of each parameter is the standard deviation of values measured from all 441 sites on the phantom. Phantoms labeled (a), (b), and (c) are included in Fig. 5.12.
5.2 Constituents of each phantom used to determine S and / in Eq. 4.10.
5.3 Fluorescence parameters measured from tissue phantoms. The measured furan concentration for 1% intralipid has been normalized to 0.25 and all other measured concentrations are normalized by the same factor.
6.1 Mean and standard deviation of spectroscopy parameters measured from the endocervix and transformation zones of LEEP patients.
6.2 Mean and standard deviation of spectroscopy parameters measured from the ectocervix and transformation zones of LEEP patients.
6.3 A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 13 true positives, 11 false positives, 3 false negatives, and 40 true negatives. The two-sided 95% confidence intervals are given in brackets.
A.1 A 2x2 accuracy table showing the number of SIL and non-SIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 17 true positives, 9 false positives, 23 false negatives, and 18 true negatives. The two-sided 95% confidence intervals are given in brackets.
B.1 A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies (according to pathology) accurately detected by spectroscopic imaging. There are 14 true positives, 16 false positives, 2 false negatives, and 35 true negatives. The two-sided 95% confidence intervals are given in brackets.
B.2 A 2x2 accuracy table showing the number of HSIL and non-HSIL biopsies accurately detected by spectroscopic imaging. There are 10 true positives, 12 false positives, 6 false negatives, and 39 true negatives. The two-sided 95% confidence intervals are given in brackets.
146