Introduction of the Cancer Tumors Into the Stomach and Pancreas to the Healthy Digital Human Phantom

Introduction of the cancer tumors into the stomach and pancreas to the healthy digital human phantom

A Dissertation submitted to the University of Manchester for the degree of Master of Science in the Faculty of Engineering and Physical Sciences

2016

Dimitra Paliatsa

School of Computer Science Contents Abstract ...... 11

Declaration ...... 12

Acknowledgments ...... 14

Chapter 1 ...... 15

Introduction ...... 15

1.1 Project Aim and Objectives ...... 17

1.2 Report Structure ...... 18

Chapter 2 ...... 19

Stomach Cancer...... 19

2.1 Structure of the stomach ...... 19

2.2 Stomach (or Gastric) Cancer ...... 21

2.3 Types of Stomach Cancer ...... 22

2.4 Detection mechanisms of Stomach Cancer ...... 23

2.4.1 Upper endoscopy ...... 23

2.4.2 Endoscopic Ultrasound ...... 24

2.4.3 Biopsy ...... 24

2.4.4 Barium meal X-ray ...... 25

2.4.5 Abdominal Computed Tomography (CT) ...... 25

2.4.6 Magnetic Resonance Imaging (MRI) ...... 26

2.4.7 Positron Emission Tomography (PET) ...... 26

Chapter 3 ...... 28

Pancreatic Cancer ...... 28

3.1 Structure of the pancreas ...... 28

3.2 Pancreatic Cancer...... 29

3.3 Types of Pancreatic Cancer ...... 31

2 3.3.1 Exocrine Tumor ...... 31

3.3.2 Endocrine Tumor ...... 32

3.4 Detection mechanisms of Pancreatic Cancer ...... 33

3.4.1 Ultrasound scan of the abdomen ...... 33

3.4.2 Endoscopic Ultrasound ...... 33

3.4.3 Endoscopic Retrograde Cholangio-Pancreatography (ERCP)...... 34

3.4.4 Abdominal Computed Tomography (CT) ...... 34

3.4.5 Magnetic Resonance Imaging (MRI) ...... 35

3.4.6 Positron Emission Tomography (PET) ...... 35

3.4.7 Laparoscopy ...... 35

3.4.8 Biopsy ...... 36

Chapter 4 ...... 37

Tumor Classification ...... 37

4.1 Stomach Tumor Classification ...... 37

4.1.1 Bormann Classification ...... 37

4.1.1.1 Classification of Early Gastric Cancer ...... 38

4.1.1.2 Classification of Advanced Gastric Cancer ...... 38

4.1.2 Lauren Classification ...... 39

4.1.3 TNM staging system ...... 40

4.1.3.1 Tumor (T)...... 40

4.1.3.2 Node (N) ...... 40

4.1.3.3 Metastasis (M) ...... 41

4.1.4 Stages Grouping ...... 41

4.2 Pancreatic Tumor Classification ...... 43

4.2.1 MD Anderson Cancer Center (MDACC) classification ...... 44

4.2.1.1 Resectable ...... 44

4.2.1.2 Borderline Resectable ...... 44

3 4.2.1.3 Locally Advanced ...... 44

4.2.1.4 Metastatic ...... 45

4.2.2 World Health Organization (WHO) classification ...... 45

4.2.3 TNM staging system ...... 46

4.2.3.1 Tumor (T)...... 46

4.2.3.2 Node (N) ...... 46

4.2.3.3 Metastasis (M) ...... 46

4.2.4 Stages Grouping ...... 47

Chapter 5 ...... 49

Digital Human Phantoms ...... 49

5.1 DHP Production ...... 49

5.2 DHP Structure ...... 51

Chapter 6 ...... 54

Design and Implementation ...... 54

6.1 Mathematical Models...... 54

6.2 Implementation ...... 56

6.2.1 Shell script to identify the location of stomach and pancreas into the DHP images ...... 57

6.2.2 Mathematical model for the representation of gastric and pancreatic cancer (ellipsoid tumor) ...... 57

6.2.3 Mathematical model for the representation of early gastric cancer type I and pancreatic cancer (spheroid tumor) ...... 59

6.2.4 Mathematical model for the representation of gastric cancer (paraboloid tumor)……………………………………………………………………………60

6.2.5 Implementation of the algorithm for tumor introduction ...... 62

6.2.6 Implementation of the Graphical User Interface (GUI) ...... 65

Chapter 7 ...... 75

Evaluation...... 75

4 7.1 Evaluation Metrics ...... 75

7.2 Relevance in gastric cancer representation ...... 76

7.2.1 Relevance in early gastric cancer types I, IIa, IIb and advanced type I . 76

7.2.2 Relevance in early gastric cancer type IIc and advanced type II ...... 79

7.3 Relevance in pancreatic cancer representation ...... 82

7.3.1 Relevance in exocrine tumors ...... 82

7.3.2 Relevance in endocrine tumors ...... 86

7.4 Efficiency ...... 87

7.4.1 Efficiency of spheroid tumor ...... 88

7.4.2 Efficiency of ellipsoid tumor ...... 91

7.4.3 Efficiency of paraboloid tumor ...... 95

7.5 Effectiveness ...... 99

7.6 Impact ...... 99

Chapter 8 ...... 100

Conclusions and Future Plans ...... 100

8.1 Conclusions ...... 100

8.2 Future Plans ...... 101

References ...... 102

Appendix A ...... 110

Appendix B ...... 111

Word count: 16173

5 List of Figures

Figure 2.1: Parts of the stomach ...... 20 Figure 2.2: The layers of the stomach wall ...... 21 Figure 2.3: Endoscopic Diagnosis ...... 24 Figure 2.4: Barium X-ray showing gastric cancer ...... 25 Figure 2.5: CT scan image showing gastric cancer ...... 26 Figure 2.6: Axial PET scan and CT scan illustrate a primary adenocarcinoma of stomach (T arrow) with local lymph node involvement (LN arrow)...... 27 Figure 3.1 Anatomy of the pancreas ...... 29 Figure 3.2: CT scan of the upper abdomen (normal pancreas) ...... 34 Figure 3.3: MRI image (normal pancreas) ...... 35 Figure 4.1: Bormann classification of early gastric cancer...... 39 Figure 4.2: Bormann classification of advanced gastric cancer ...... 39 Figure 5.1: DHP image that includes stomach (v1_01090.pgm) ...... 50 Figure 5.2: DHP image that includes pancreas (v1_01150.pgm) ...... 51 Figure 5.3: DHP first sample image ...... 52 Figure 5.4: DHP second sample image ...... 53 Figure 6.1: The conversion of a real-world problem into a mathematical problem .... 55 Figure 6.2: Ellipsoid shape with a,b,c parameters ...... 58 Figure 6.3: A prolate ellipsoid (a=25, b=50) ...... 58 Figure 6.4: An oblate ellipsoid (a=50, b=25) ...... 58 Figure 6.5: Spheroid shape with r and c parameters ...... 59 Figure 6.6: Sphere shape with parameters r=30 and c=30 ...... 59 Figure 6.7: An elliptic paraboloid ...... 60 Figure 6.8: A hyperbolic paraboloid ...... 60 Figure 6.9: A parabolic cylinder ...... 61 Figure 6.10: The term (푏푥2 − 푎푦)2 ≤ 100ab ...... 61 Figure 6.11: An elliptic cylinder and the term (푧2 + 푦2) ≤ 100ab ...... 61 Figure 6.12: A view of the paraboloid tumor ...... 62 Figure 6.13: Another view of the same paraboloid tumor ...... 62 Figure 6.14: Paraboloid tumor with degree of rotation= -100 ...... 64 Figure 6.15: Paraboloid tumor with degree of rotation= 0 ...... 64

6 Figure 6.16: Paraboloid tumor with degree of rotation= 50 ...... 64 Figure 6.17: Paraboloid tumor with (Thickness, Aperture)=(6,1) ...... 64 Figure 6.18: Paraboloid tumor with (Thickness, Aperture)=(10,1) ...... 64 Figure 6.19: Paraboloid tumor with (Thickness, Aperture)=(10,3) ...... 64 Figure 6.20: The first page of the GUI with tumor’s position ...... 65 Figure 6.21: The second page of the GUI with tumor’s position ...... 66 Figure 6.22: Pancreatic cancer, Spheroid tumor ...... 67 Figure 6.23: Pancreatic cancer, Ellipsoid tumor ...... 67 Figure 6.24: Gastric cancer, Paraboloid tumor ...... 67 Figure 6.25: Forbidden values in (x,y), outside pancreas ...... 68 Figure 6.26: Forbidden value in z coordinate, image without pancreas ...... 69 Figure 6.27: Forbidden value for radius ...... 69 Figure 6.28: Forbidden value for degree of rotation ...... 70 Figure 6.29: Three tumors are introduced using Add button ...... 70 Figure 6.30: The second tumor is selected in order to be deleted ...... 71 Figure 6.31: The second tumor has been removed ...... 71 Figure 6.32: Results after the execution of the program ...... 72 Figure 6.33: Waiting bar during the execution of Save function ...... 74 Figure 7.1: A 76-year old man with a T1a gastric carcinoma. CT scan illustrates a thickening of the inner layer (arrow)………………………………………………....77 Figure 7.2: Produced image (v1_01112.pgm) with gastric cancer type T1a ...... 77 Figure 7.3: A 64-year old woman with a T3 gastric carcinoma. CT scan demonstrates a mass in the lesser curvature (arrow) and a perigastric fat stranding (arrowhead) .... 78 Figure 7.4: Produced image (v1_01062.pgm) with gastric cancer type T3 ...... 78 Figure 7.5: A 70–year-old woman with a low grade gastrointestinal stromal tumor. CT scans present submucosal soft tissue mass (arrow) in greater curvature side of stomach ...... 79 Figure 7.6: Produced image (v1_01048.pgm) with GIST type of stomach cancer ..... 79 Figure 7.7: Axial CT illustrates thickening (arrows) and mucosal enhancement of the lesser curvature of the stomach ...... 80 Figure 7.8: Produced image (v1_01081.pgm) with gastric cancer ...... 80 Figure 7.9: CT transverse scan shows an irregular wall thickening (gastric carcinoma) on the antro-pyloric tract (arrow) ...... 81 Figure 7.10: Produced image (v1_01083.pgm) with gastric carcinoma ...... 81

7 Figure 7.11: Side view of the image with gastric carcinoma ...... 81 Figure 7.12: a. MRI scan image shows a pancreatic tail cyst ...... 82 Figure 7.13: b. CT scan image illustrates a cystic fluid mass 4cm in diameter on the tail of the pancreas in a 68-year-old woman ...... 82 Figure 7.14: Produced image (v1_01174.pgm) with a cyst mass in the tail of the pancreas...... 83 Figure 7.15: MRI image demonstrates two lesions (red arrows) with 2.3 cm and 1.8 cm in diameter in body of pancreas in a 60-year-old patient ...... 83 Figure 7.16: Produced image (v1_01170.pgm) with two tumors inside the body of pancreas...... 83 Figure 7.17: A CT scan shows a ductal adenocarcinoma (long arrow) in the body of the pancreas and a duct dilatation (short arrow) ...... 84 Figure 7.18: Produced image (v1_01158.pgm) with a ductal adenocarcinoma ...... 84 Figure 7.19: Pancreatic solid pseudopapillary tumors (arrows in A, B, and C) in the tail of the pancreas ...... 85 Figure 7.20: Produced image (v1_01121.pgm) with a solid pseudo papillary tumors 85 Figure 7.21: A CT scan of an acinar cell carcinoma in the head of the pancreas ...... 85 Figure 7.22: Produced image (v1_01172.pgm) with an acinar cell carcinoma in the head of the pancreas ...... 86 Figure 7.23: A CT scan illustrates an insulinoma (yellow arrow) in the head of the pancreas ...... 86 Figure 7.24: Produced image (v1_01184.pgm) with insulinoma ...... 87 Figure 7.25: Average execution time (sec) of Run function for different layers ...... 88 Figure 7.26 : Average execution time (sec) of Run function for different values of radius (mm) ...... 89 Figure 7.27: Average execution time (sec) of Save function for different layers ...... 90 Figure 7.28: Average execution time (sec) of Save function for different values of radius (mm) ...... 90 Figure 7.29: Average execution time (sec) of Run function for different layers ...... 92 Figure 7.30: Average execution time (sec) of Run function for different values of a,b (mm) ...... 92 Figure 7.31: Average execution time (sec) of Save function for different layers ...... 94 Figure 7.32: Average execution time (sec) of Save function for different values of a,b (mm) ...... 94

8 Figure 7.33: Average execution time (sec) of Run function for different layers ...... 95 Figure 7.34: Average execution time (sec) of Run function for different values of thickness, aperture (mm) ...... 96 Figure 7.35: Average execution time (sec) of Save function for different layers ...... 97 Figure 7.36: Average execution time (sec) of Save function for different values of thickness, aperture (mm) ...... 98 Figure B.1: Average execution time (sec) of Run function for different layers………111 Figure B.2: Average execution time (sec) of Run function for different values of radius (mm) ...... 112 Figure B.3: Average execution time (sec) of Save function for different layers ...... 112 Figure B.4: Average execution time (sec) of Save function for different values of radius (mm) ...... 113 Figure B.5: Average execution time (sec) of Run function for different layers ...... 114 Figure B.6: Average execution time (sec) of Run function for different values of a,b (mm) ...... 114 Figure B.7: Average execution time (sec) of Save function for different of layers ... 115 Figure B.8: Average execution time (sec) of Save function for different values of a,b (mm) ...... 116

9 List of Tables

Table 4.1: Stage grouping system for gastric cancer ...... 43 Table 4.2: Stage grouping system for pancreatic cancer ...... 48 Table 5.1: The main structure of a pgm data file ...... 52 Table 5.2: Values of first sample image ...... 52 Table 5.3: Values of second sample image ...... 53 Table 7.1: Values of a, b and average execution time in gastric ellipsoid tumor ...... 93 Table 7.2: Values of thickness, aperture and average execution time in gastric paraboloid tumor ...... 96 Table B.1: Values of a, b and average execution time in pancreatic ellipsoid tumor…………………………………………………………………………………………………………………… 115

10 Abstract

In our days, cancer is one of the most dangerous diseases worldwide, which can start in any part of the human body. In all different types of cancer, cells start to grow out of control, divide without stopping and spread into surrounding tissues. The organ where the cancer starts is called primary tumor. In this project, we will focus on cancer primary tumors into the stomach and pancreas of the human body. Stomach cancer, also called gastric cancer, is the fifth most common cancer worldwide, while pancreatic cancer holds the twelfth position according to the World Cancer Research Fund International. In recent years, various approaches have been made to visualize the region of the affected organ. However, the existing detection algorithms confront several problems and some of them are unsuccessful in the detection of cancer at early stages. Therefore, it is needed a new detection mechanism which will detect and represent effectively the cancerous parts of the body. Our research group is developing a new technique to detect the cancerous areas at all stages. As it is under research, the group uses a digital human phantom for the numerical simulations. The main aim of this project is the implementation of an algorithm which will replace the healthy tissues with the cancerous cells in order to represent cancer in stomach and pancreas. After the end of the implementation the produced images with cancer in stomach and pancreas will be used by the research team to test the effectiveness of their techniques to detect gastric and pancreatic cancer.

11 Declaration

No portion of the work referred to in this dissertation has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

12 Copyright

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II. Copies of this dissertation, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has entered into. This page must form part of any such copies made.

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13 Acknowledgments

Firstly and primarily, I would like to thank my supervisor, Dr Fumie Costen, for her excellent supervision, inestimable support and valuable guidance over the dissertation’s period.

I would also like to express my deep gratitude to my family for their constant trust, continuous encouragement, financial and emotional support throughout my studies.

Finally, I would like to express my appreciation to my friends for their patience and moral support all of these years and for their useful remarks over the time period of my dissertation.

14 Chapter 1

Introduction

Undoubtedly, cancer is one of the most serious diseases in this century. It has been estimated that cancer is the second leading cause of death after heart disease [1]. The term of cancer refers to a collection of relevant diseases that can involve any tissue of human body and it evolves with different form in each area [2]. In all types of cancer, some cells grow and proliferate uncontrollably. These cells which are called cancerous cells expand abnormal and destroy surrounding healthy tissues and organs [2,3]. Nowadays, there are over 200 different types of cancer [2,4] which are usually named for the tissue or organ in which the cancer evolves. For each type there are several methods of diagnosis and treatment. Some types such as skin cancer may be diagnosed at an early stage by screening measures [5]. However, in most cases, cancer is detected after the growth and spread of a tumor. In our project, we deal with primary tumors that arise in stomach and pancreas of a human body.

Although the incidence of gastric cancer have declined over the world, gastric cancer remains the ﬁfth most common cancer and the third leading cause of cancer death. The case fatality rate is estimated around 75%. The highest incidence rate is observed in Japan, China, Central and South America and Eastern Europe [6]. The main type of gastric cancer is called adenocarcinoma and it applies in 90% of all stomach cancer cases [7]. Except of this, there are also 3 other types of gastric cancer. The first one is called lymphoma and it occurs in 4% of all cases. The second is the Gastrointestinal stromal tumor (GIST) and occurs in the wall of the stomach. The last type is the Carcinoid tumor and it appears in almost 3% of all stomach cancer incidences [7]. The type of gastric cancer is very essential, because based on it patient follows different treatment options. There are several detection mechanisms that are used for stomach cancer identification. The most common detection methods are described explicitly in Section 2.4. Concisely, these mechanisms are the following [8]:

o Upper endoscopy

o Endoscopic Ultrasound o Barium meal X-ray o Abdominal Computed Tomography (CT) o Magnetic Resonance Imaging (MRI) o Positron Emission Tomography (PET) o Biopsy

In this century, pancreatic cancer remains also a serious disease as gastric cancer with high lethal rates worldwide. The highest mortality portions are reached in developed countries [9,10]. More specific, in United States and in Europe, pancreatic cancer is one of the leading causes of cancer death and it reaches the 4th and 6th place respectively [9]. Unfortunately, as pancreatic cancer does not usually give symptoms at an early stage, it is hard to treat it. Indeed, it has been estimated that it presents the worst prognosis of any other type of cancer with a lack of screening tests to detect it [9,10]. There are two main types of pancreatic cancer, the exocrine cancer which is the most common type and the endocrine pancreatic cancer that occurs rarely [11]. Pancreatic ductual adenocarcinoma is the most known type of exocrine cancer that accounts approximately 90% of all exocrine types. There are also some rare types of exocrine tumors, such as acinar cell carcinoma, pancreatoblastoma and cystic tumors [11,12]. In regard to endocrine tumors the main types are the Gastrinomas, Glucagonomas, Insulinomas, Somatostatinomas and VIPomas [12]. The current detection mechanisms which are described in detailed in Section 3.4 are similar to gastric detection mechanisms and are the following [13] :

o Ultrasound scan of the abdomen o Endoscopic Ultrasound o Endoscopic Retrograde Cholangio-Pancreatography (ERCP) o Abdominal Computed Tomography (CT) o Magnetic Resonance Imaging (MRI) o Positron Emission Tomography (PET) o Laparoscopy o Biopsy

However, most of these methods have limited success to diagnose cancer at early stages. Our research group is developing a new technique to detect the cancerous

16 areas at all stages. As it is under research, the group uses a digital human phantom for the numerical simulations that is produced by scanning a healthy human body and then segmenting these scanned images. In this project, cancer will be introduced in different parts of stomach and pancreas in the healthy digital phantom.

1.1 Project Aim and Objectives

The aim of this project is the development of mathematical models of the tumors and the implementation of the algorithm which will replace the healthy tissues with the cancerous cells in order to represent cancer tumors in stomach and pancreas. The objectives of the project in order to produce the images with cancer in stomach and pancreas are:

o An intensive survey of the different types of gastric and pancreatic cancer to learn their locations, shapes and features.

o Learning the format of portable grayscale images that are used in digital human phantom.

o Learning shell scripting in Linux Operating Systems to identify the location of stomach and pancreas and isolate the images that contain these parts.

o Implementation of the mathematical models that represent gastric and pancreatic tumors.

o Development and implementation of the algorithm that is used for cancer insertion in the digital human phantom.

o Implementation of a graphical user interface in order to select the type of cancer (gastric or pancreatic), the type and the size of the tumor and its location.

1.2 Report Structure

The chapters that are included in this report are the following:

o Chapter 2 - Stomach Cancer: An intensive survey of the gastric cancer, its types and its detection mechanisms is presented.

o Chapter 3 - Pancreatic Cancer: A detailed survey of the pancreatic cancer, its types and its detection mechanisms is provided.

o Chapter 4 –Tumor Classification: Different methods and stages of gastric and pancreatic cancer are analysed.

o Chapter 5 - Digital Human Phantom: All the crucial information about Digital Human Phantom is provided and the format of the images that are used in this project is explained.

o Chapter 6 – Implementation: All the steps that were followed during the project are analysed. In particular, there is crucial information about the development and implementation of the mathematical models that are used for stomach and pancreatic cancer representation and there are also details about the implementation of the graphical user interface (GUI).

o Chapter 7 – Evaluation: The criteria that are used to check the evaluation of the project are provided. More specific, various real images with gastric or pancreatic cancer are compared with the produced images in order to check their similarity and test the effectiveness and impact of this work. Furthermore, the execution time of the project is calculated and it is presented in order to check the efficiency of the project.

o Chapter 8 – Conclusions and Future Plans: An overview of the aim of this project is presented and some plans for future work are suggested.

Chapter 2

Stomach Cancer

This chapter discusses stomach (or gastric) cancer. In Section 2.1 the structure of the stomach is analyzed to realize the potential positions where the cancer forms in stomach. Afterwards, in Section 2.2 general information about gastric cancer, such as the risk factors and the symptoms of the disease are shown. In Sector 2.3, the main types of gastric cancer are presented, while in Sector 2.4 we provide information about the existing detection mechanisms of stomach cancer.

2.1 Structure of the stomach

Before discussing about gastric cancer and in order to realize where it can be developed, it would be useful to present the normal structure and the function of the stomach. The stomach is a part of the digestive system. It is a muscular, elastic J- shaped bag and a thick walled organ that is located in the upper abdomen of the body. The upper part is connected to the esophagus and the other to the duodenum, which is the first part of the small intestine [14]. It temporarily stores food and it mixes it by secreting gastric juice. Finally, it sends the mixture on the small intestine [7].

The stomach has 5 main parts (Figure 2.1). The first portion is called cardia and it is the area where food enters the stomach from the oesophagus. Above the cardiac sphincter is the fundus site of the stomach at which undigested food is stored. The main and largest area is called the body of the stomach. The part near to the intestine, where the food is mixed with gastric juice and where the partial digestion occurs, is called antrum. The last portion of the stomach is the pylorus. This part controls the emptying of the stomach contents into the small intestine [7].

Figure 2.1: Parts of the stomach http://www.aboutcancer.com/Parts_of_stomach_utd.jpg

In order to determine the stage of the cancer it is important to know the layers of the stomach. The stomach wall has 4 layers (Figure 2.2). The outermost layers of stomach are the subserosa and serosa where there is a network of blood vessels that supply the blood to the stomach [15]. Inside of them, there is a thick layer of muscle that mixes the stomach contents and it is called muscularis propria. The next deeper layer is called submucosa and it contains blood and lymphatic vessels, muscle fibres and nerves. Finally, the deepest layer is called mucosa and is the site where digestive process occurs. In most cases, cancer starts in this layer [8] and then it extends to the others. The thickness of the normal stomach wall can vary between 3 mm and 7 mm [16,17]. Indeed, it is considered that a value greater than 7 mm may indicate the presence of gastric cancer [16,17].

Figure 2.2: The layers of the stomach wall http://www.cancer.org/acs/groups/cid/documents/webcontent/~export/003141- 2~52~dyn_acs_cid_template/228099-3.gif

2.2 Stomach (or Gastric) Cancer

Stomach (or gastric) cancer is the growing of a malignant primary tumor in the lining of the stomach [18]. As an initial stage, before a cancer occurs, pre-cancerous changes usually develop in the inner lining (mucosa) of the stomach. Nonetheless, these early changes rarely cause symptoms, so it is difficult to be spotted [7].

Unfortunately, the exact cause of stomach cancer remains unknown. However, there are some factors that seem to play a key role and include [6,18,19,20]:

o Gender. Stomach cancer appears twice in men than in women. o Age. In almost 95% of all cases, gastric cancer is detected in people in the elderly. o Helicobacter pylori infection. It increases the risk of gastric cancer in the lower part of the stomach by six times [18,20]. o Diet. A diet that contains salted and preserved meat food seems to be responsible for gastric cancer. o Tobacco use. Smokers have twice possibilities to be diagnosed with stomach cancer in cardia than nonsmokers. o Obesity. Being overweight or obese.

o Family history. People with a family history of stomach cancer (first-degree relatives) have a higher risk of growing stomach cancer. o Polyps in the digestive system. Although polyps are non-cancerous masses, they sometimes grow into cancer.

Although the disease rarely causes symptoms at the early stages there are some signs that may occur. These include [6,18]:

o Heartburn or indigestion o Feeling full immediately after eating a small meal o Nausea o Lack of appetite o Unanticipated loss of weight o Stomach ache o Vague discomfort in the abdomen, usually above the navel o Anaemia o Vomiting (with or without blood) o Swelling in the stomach

Nonetheless, these symptoms usually appear at the advanced stage, when cancer has spread in other portion of the body and it is extremely hard to be treated.

2.3 Types of Stomach Cancer

Stomach cancer is classified according to the type of tissue where it originates. There are several types of stomach cancer [8,21] which can be summarized by the following types:

o Adenocarcinoma: The most common type of stomach cancer is called adenocarcinoma and it starts in the innermost lining of the stomach (mucosa). Almost 95% of stomach cancers detected are adenocarcinomas.

o Lymphoma: It is a cancer that can start in any area where lymph tissues exist, such as stomach. It occurs rarely and it accounts approximately 4% of all gastric cancer cases.

o Gastrointestinal stromal tumor (GIST): It is a seldom type of stomach cancer that occurs in the wall of the stomach. GISTs can be cancerous and non-cancerous. They may develop throughout the digestive tract, but they are mostly spotted in the stomach.

o Carcinoid tumor: This type of cancer is estimated that appears in 3% of all gastric cancer incidences and it start to develop in hormone producing cells of the stomach. It can expand to other organs, but it usually does not spread.

2.4 Detection mechanisms of Stomach Cancer

If a patient feels some of the symptoms that mention in Section 2.2, it is necessary to visit a doctor. If the presence of gastric cancer is suspected, a physical exam and other tests will be given to certify the disease. The main tests that are used to detect gastric cancer are listed in the following subsections.

2.4.1 Upper endoscopy It is a common and sensitive test that is used to detect gastric cancer when symptoms may indicate its presence. It is also called gastroscopy and it allows doctor to examine and visualize the inside of the body using a thin and flexible tube with a small camera (Figure 2.3). During this examination, patient takes a sedative to relax and a topical anaesthetic is also administered [22]. If abnormal areas are found, tissue samples are taken and are examined in a lab to inspect the presence of cancer. Endoscopy is also essential in the detection of gastric lymphoma [23]. However, the basic drawback of endoscopy is that gastric cancers in hereditary diffuse stomach cancer syndrome cannot usually be detected [8].

Figure 2.3: Endoscopic Diagnosis https://www.jhmicall.org/Upload/200802291430_43308_000.jpg

2.4.2 Endoscopic Ultrasound It is a method that is used since 1980 and plays a key role in the detection of biliary and gastrointestinal tumors [24]. It allows the visualization of organs, such us stomach using a combination of endoscopy and high-frequency ultrasound. During this procedure a transducer which emits sound waves is placed on the skin in order to produce a black and white image on a screen. Endoscopic ultrasound method presents high accuracy in tumor detection. However, its accuracy for individual categories is low [24]. Moreover, it is technically challenging because some characteristics of the tumor, like location, size and histologically type may affect the performance of this method.

2.4.3 Biopsy When an abnormal area is found on endoscopy or an imaging test, the doctor removes a sample of that area in order to examine if there are cancerous cells (malignant) or non-cancerous (benign). This procedure is called biopsy and it usually takes place in the upper endoscopy method. Biopsies may also be taken from areas of possible cancer spread, such as nearby organs and lymph nodes [8]. This method is important,

24 as it demonstrates the type of cell where the cancer developed. However, sometimes the cancer is difficult to reach and biopsy is not possible to occur.

2.4.4 Barium meal X-ray In this test, the patient should drink a chalky liquid with barium to make the stomach show up clearly on an X-ray (Figure 2.4). This method is less invasive than endoscopy and it can be helpful in some cases. Nonetheless, it's less commonly used to diagnose cancer, because it may not detect some abnormal areas. It cannot also be used to take a sample of tissue for biopsy [8].

Figure 2.4: Barium X-ray showing gastric cancer https://www.jhmicall.org/Upload/200802291433_11650_000.jpg

2.4.5 Abdominal Computed Tomography (CT) The CT scan takes many pictures of the inside of the body as it rotates around the patient. It provides representative images of the inside of human body and it can verify the presence and the location of cancer (Figure 2.5). Furthermore, it depicts nearby organs, such as liver and spleen where cancer may have been spread. Therefore, this test is useful to decide the extent of cancer and the treatment that is needed. Nevertheless, the CT scan cannot perceive the different layers of the gastric wall, so it is impossible to distinguish early from advanced lesions [22]. Moreover, CT scanning does not give any information about tissue confirmation of the grade and the type of gastric cancer [22].

Figure 2.5: CT scan image showing gastric cancer https://www.jhmicall.org/Upload/200802291431_17542_000.jpg

2.4.6 Magnetic Resonance Imaging (MRI) MRI scan uses radiofrequency waves and magnetic fields to create precise images of the inside of the body. It can discern between normal and disease tissues and it is usually used to diagnose a tumor's size and stage. It is a powerful tool that can find metastatic tumor and it is helpful for treatment planning. The main distinction between the other two imaging methods (X-ray, CT scan) is that MRI scan does not use radiation. Nonetheless, it is usually used to diagnose other types of cancers, such as brain cancer [24,25].

2.4.7 Positron Emission Tomography (PET) This test locates the radioactive substance which is administered to patient into a vein and it is a type of sugar related to glucose, known as FDG. A concentration of FDG in the body's tissues shows the presence of cancer, as cancerous cells are growing faster than normal and they consume more sugar. Therefore, they take up the radioactive material. Although PET scan (Figure 2.6) does not provide as detailed images as CT and MRI scan, it shows the whole body of the patient [8]. So it is a helpful method to find the organs where the cancer may have spread. Nonetheless, some types of stomach cancer do not take up glucose, so it is hard to be detected by PET scan [8].

Figure 2.6: Axial PET scan and CT scan illustrate a primary adenocarcinoma of stomach (T arrow) with local lymph node involvement (LN arrow) http://onlinelibrary.wiley.com/store/10.1002/cncr.21074/asset/image_n/nfig001.jp g?v=1&t=iruro4of&s=2c0d8f1ac972eb140024c96bccb2af53d4e726fe

Chapter 3

Pancreatic Cancer

This chapter explains pancreatic cancer. Especially, in Section 3.1 the main features and the structure of the pancreas are analyzed to realize the potential positions where the cancer forms in this organ. In Section 3.2 general information about pancreatic cancer, such as the risk factors and the signs of the disease are shown. Afterwards, the main types of pancreatic cancer are discussed, while in the last subsector we provide information about the existing detection mechanisms of pancreatic cancer.

3.1 Structure of the pancreas

In order to figure out the potential positions where pancreatic cancer can be developed, the normal structure and the function of the pancreas are explained. The pancreas is a tapered organ that lies in the upper left abdomen behind the stomach, near to the small intestine, liver, and spleen [26]. The right side of pancreas is called head and it is positioned next to the small intestine. The central part is called neck or body, while the left part is called tail and it is located near to spleen (Figure 3.1).

Figure 3.1: Anatomy of the pancreas http://www.pancreapedia.org/sites/www.pancreapedia.org/files/image4.jpg

The main role of the pancreas is to secrete digestive enzymes and hormones to control blood sugar and digest the food [27]. In order to achieve it, it has two functions, the exocrine and endocrine functions. More specific, the exocrine function produces digestive enzymes, such as trypsin and chymotrypsin for proteins digestion or amylase for carbohydrates digestion, into the duodenum [27]. On the other hand, the endocrine comprises of the islets of Langerhans and it produces and secretes hormones into the bloodstream [27]. The main hormones that it creates are insulin, glucagon, somatostatin, and pancreatic polypeptide. The exocrine tissues compose 95% of the pancreatic mass and the remaining mass consists of endocrine cells.

3.2 Pancreatic Cancer

Pancreatic cancer remains one of the most lethal diseases, as almost in all cases pancreatic cancer spreads out in other organs and patients die [9,10]. It rarely gives symptoms at early stages, but as prevention can help to reduce pancreatic cancer

29 mortality rates, it is crucial to learn the risk factors that may cause it. These factors are [28,29]:

o Age. The average age of pancreatic cancer is near 70. o Family history. People with family history in pancreatic cancer, like first- degree relatives, may also develop this disease in the future, although it is not clear if there is an inherited syndrome or not. o Tobacco use. The risk of pancreatic cancer is twice in smokers than in nonsmokers. o Obesity. Overweight is also a risk of pancreatic cancer. o Diabetes. People with type 2 diabetes are more likely to develop pancreatic cancer. o Helicobacter pylori infection. It is considered that it raises the risk not only of gastric but also of pancreatic cancer. o Chronic pancreatitis. This factor also seems to increase the possibilities of growing pancreatic cancer.

Pancreatic cancer can be developed on the head, body or tail of the pancreas. Although the symptoms can vary depend on the position where the tumor is, the main signs that may occur in pancreatic cancer are mentioned below [30]:

o Abdominal pain or back pain o Weight loss o Jaundice o Bowel changes o Diabetes o Sickness o Indigestion or heartburn o Fever and shivering

Unfortunately, these signs usually occur in advanced level, when cancer has metastasized in other organs and it is difficult to be treated.

3.3 Types of Pancreatic Cancer

There are several types of pancreatic tumor, which are grouped according to where they occur in this organ. Almost 65% of all cases start growing in the head of the pancreas, 30% in the body and tail and 5% in the whole pancreas [12]. Two main categories are the exocrine and endocrine pancreatic cancer. All different types of pancreatic cancer are discussed in the next subsections.

3.3.1 Exocrine Tumor Exocrine tumor starts growing in the exocrine cells of the pancreas and it is the most common type of pancreatic cancer [12]. It can occur anywhere in the pancreas and it appears in over 95% of all cases of pancreatic cancer. The most known type is the ductal adenocarcinoma. Nevertheless, there are also some rare types that are mentioned below.

o Ductal Adenocarcinoma: This type of tumor starts from cells lining the ducts of the pancreas and it accounts over 75% of all malignant pancreatic cancer. It is aggressive and devastating and it can grow in any position of pancreas, but it frequently appears in the head of it [12].

o Acinar Cell Carcinoma: The acinar cell carcinoma appears rarely and it accounts less than 1-2% of pancreatic cancers [12]. It grows in the acinar cells that produce the pancreatic juices and it tends to develop slower than adenocarcinoma with a better progrnosis.

o Pancreatoblastoma: It is a rare type of pancreatic cancer that occurs mainly in children and it is considered that it is related with some genetic conditions, like Beckwith-Wiedemann syndrome [11,12].

o Mucinous Cystic Neoplasm (MCN): Mucinous Cystic Neoplasm is a cystic tumor that creates a cyst in the body or tail of the pancreas. It is a seldom type of exocrine tumors and it is usually occurs in middle-aged women [12]. Most pancreatic cysts are benign, but there are also some cancerous.

3.3.2 Endocrine Tumor Endocrine tumor accounts less than 5% of the total cases of pancreatic cancer and it starts from the endocrine (hormone producing) cells. It is also known as pancreatic neuroendocrine tumor (PNETS) or islet cell tumor [12]. This kind of tumor usually grows slower than exocrine tumor and it can be cancerous or non-cancerous. The main types of endocrine tumors are the Gastrinomas, Glucagonomas, Insulinomas, Somatostatinomas and VIPomas which are named from the hormone that they produce and are explained in the following subsections.

o Gastrinomas: Gastrinoma is the second most common endocrine tumor that usually grows in the head of the pancreas and in the duodenum. It overproduces gastrin, which is a hormone that controls acid in the stomach and it can be malignant or benign [11,12].

o Glucagonomas: These tumors often develop in the tail of the pancreas and metastasize in other organs, usually in the liver. They overproduce glucagon, a hormone which is useful to increase blood sugar levels in the body. In most cases, they are malignant and they appear frequently in post-menopausal women [12].

o Insulinomas: These tumors are the most common type of endocrine tumors that produce large amount of insulin, a hormone that check the quantity of sugar in the blood. They can develop in any part of the pancreas and they frequently appear in middle age [12]. Fortunately, almost 90% of these tumors are non-cancerous [11].

o Somatostatinomas: Somatostatinomas are usually grown in the head of the pancreas and appear more in women than in men. These tumors overproduce somatostatin, a hormone which controls how the gut works [11]. They are rare tumors and most of them are malignant.

o VIPomas: They produce large amount of vasoactive intestinal polypeptide (VIP), a hormone which controls balance of sugar, salt and water within the gastrointestinal tract [11]. These tumors usually grow in the tail of the pancreas, more often in women than men.

3.4 Detection mechanisms of Pancreatic Cancer

As in stomach cancer, if a patient feels some of the symptoms that mention in Section 3.2, it is needed to visit a doctor. If the presence of pancreatic cancer is suspected, a blood test to check for abnormal levels of substances, such as CA 19-9 and other tests will be given to certify the disease. The main tests that used to detect it are listed in the following subsections with their advantages and limitations.

3.4.1 Ultrasound scan of the abdomen Ultrasound scans use high-frequency sound waves to show internal organs such as the pancreas and produce an image of the inside of the body. It is a painless, quick test and patient does not expose to radiation, but the basic drawback of this method is that it is not so effective at detecting pancreatic cancer at early stages [31].

3.4.2 Endoscopic Ultrasound An endoscopic ultrasound (EUS) is helpful to measure local tumor staging and it is also used to take samples of tissues of the affected area. During this test, the doctor passed a thin, lighted tube through the mouth of the patient to take a picture of the

33 pancreas [31]. It is considered as one of the most sensitive tests for diagnosing tumors in the head of the pancreas in early stages [32].

3.4.3 Endoscopic Retrograde Cholangio-Pancreatography (ERCP) During this procedure, a stent is inserted into the body of the patient and the doctor may takes tissue samples for biopsy. This method presents high accuracy in diagnosing cancer in the head of the pancreas. However, it provides no information about the stage of the cancer [31].

3.4.4 Abdominal Computed Tomography (CT) A CT scan uses a series of x-rays and produces images of the inside of the body [31]. This method is used frequently, because it shows the pancreas and surrounding tissues (Figure 3.2). It can also be used to detect metastatic areas of other organs and to guide a biopsy. Nevertheless, it is not so effective to detect small tumors.

Figure 3.2: CT scan of the upper abdomen (normal pancreas) http://www.pancreapedia.org/sites/www.pancreapedia.org/files/image6.jpg

3.4.5 Magnetic Resonance Imaging (MRI) A magnetic resonance imaging (MRI) scan uses strong magnetic and radio waves instead of X-rays in order to produce an image of the inside of the body (Figure 3.3). This method can also be used to check for metastatic tumors in other parts of the body and it presents similar accuracy as CT scan. It also presents better tissue contrast than CT scan. However, it cannot detect small tumors [13].

Figure 3.3: MRI image (normal pancreas) https://web.stanford.edu/dept/radiology/radiologysite/images/Med%20students %207,%20pancreas/Pancreas,%20normal%20axial%20MR.png

3.4.6 Positron Emission Tomography (PET) During this method, a radioactive substance is injected into a vein and it appears the areas where the cells are more active in the body [13]. A positron emission tomography (PET) scan is useful to determine the location of the cancer and if it has spread to other parts of the body. This method can also be used in patients with suspected pancreatic cancer, in cases where CT or MRI fail to identify small tumors [33].

3.4.7 Laparoscopy It is a surgical procedure done under general anesthetic. During it, a laparoscope will be inserted into the body that allows doctor to see the inside of the abdomen. The main benefit of this method is that it helps to decide if a tumor can be removed by surgery or not and if it has spread in other organs [13].

3.4.8 Biopsy During this test, a doctor removes a small tissue sample in order to examine if it is malignant or benign. This process can take place during EUS, ERCP or laparoscopy [13]. This method is essential, as it illustrates the form of cell where the cancer appears. However, sometimes the cancer is difficult to reach and biopsy is not possible to occur.

Chapter 4

Tumor Classification

There are several systems to classify and identify the stages of cancer. Some of them are common for all types of cancer, while some others depend on the type of cancer and the affected organ. In Section 4.1 the most common classification types of gastric cancer are analysed, while in Section 4.2 the most known classification methods of pancreatic cancer are provided.

4.1 Stomach Tumor Classification

In case of gastric cancer, the most common systems are the Bormann classification which is based on the macroscopic appearance of the tumor and it is explained in Section 4.1.1, the Lauren classification that divides tumors into intestinal and diffuse types and it is analysed in Section 4.2.2, the TNM classification which reflects the depth of tumor infiltration (T), node involvement (N) and the presence of distant metastases (M) and it is presented in Section 4.3.3 and the stages grouping system which is explained in Section 4.4.4.

4.1.1 Bormann Classification

The Japanese Gastric Cancer Association [34] has created a system to classify a gastric tumor based on its macroscopic appearance. The Section 4.1.1.1 classifies stomach cancer at early stages, while the Section 4.1.1.2 is used for advanced gastric cancer identification.

4.1.1.1 Classification of Early Gastric Cancer The early gastric cancer is characterized as remaining to the mucosa or submucosa layer of the stomach wall, regardless of the absence or presence of the cancerous cells in nearby lymph nodes. According to Bormann's classification early gastric cancer is classified into three types based on their macroscopic appearance (Figure 4.1). In particular, tumors can be protruding (type I), superficial (type II), and excavating (type III).The first type indicates a tumor that protrudes above the mucosal surface more than 2.5 mm in height [35,36]. Type IIa is defined as a lesion that is twice as thick as normal mucosa, but less than or equal to 2.5 mm in height. Therefore, if the height of the lesion is less than 2.5 mm it is characterised as type IIa, otherwise as type I. Type IIb is used to describe lesion less than 5 mm and it the most difficult type to be diagnosed endoscopically [37], while type IIc is the most common macroscopic subtype [38]. Finally, type III is defined as a deeply prominent depression.

4.1.1.2 Classification of Advanced Gastric Cancer The appearance of advanced gastric cancer varies from exophytic, ulcerated, infiltrative or combined (Figure 4.2). The Borrmann’s classification system remains one of the most known systems and it classifies advanced gastric carcinomas into type I for polypoid growth, type II for fungating growth, type III for ulcerating growth, and type IV for diffusely infiltrating. Type II usually occurs in the antrum on the lesser curvature, while types I and III in most cases are detected in the corpus, usually on the greater curvature

Figure 4.1: Bormann classification of early Figure 4.2: Bormann classification of gastric cancer advanced gastric cancer http://clinicalgate.com/wp- http://clinicalgate.com/wp- content/uploads/2015/03/c00025_f025-002- content/uploads/2015/03/c00025_f025-005- 97814557074781.jpg 9781455707478.jpg

4.1.2 Lauren Classification

The Lauren classification is widely used to describe how adenocarcinoma tumors look and behave. According to it, gastric adenocarcinomas can be divided in two histological types, diffuse and intestinal [39]. In the first category cells are non- cohesive, poorly differentiated, spread fast to other organs and tend to scatter in the stomach. This type is often developed in younger age than the other type and almost equally in men and women. In the second category cells are cohesive, well- differentiated, grow slowly and form glands. There are indications that this type is usually developed in the elderly more often in men than in women. It has been

39 estimated that almost 55% of gastric cancers are intestinal type, 35% are diffuse type and the remaining 10% is characterised as “unclassified” [40,41].

4.1.3 TNM staging system

The stage of a cancer states its size and if cancer has affected other parts of the body. In TNM system, T (Tumor) represents the depth of the primary tumor into the stomach wall, N (Node) is used to describe if cancer has spread to lymph nodes and M (Metastasis) refers to whether cancer has metastasized to other parts of the body [42,43,44].

4.1.3.1 Tumor (T) The first factor (T) describes the depth and size of the tumor and it is divided in the following four main categories:

o T1 is the earliest stage and it means that either tumor has grown in the innerest layer of the stomach that called mucosa (T1a), or tumor has grown through mucosa and submucosa layers. o T2 describes that tumor has grown through the muscularis propria layer. o T3 means that tumor has grown into the outer layers of the stomach. o T4 is used when either tumor has broken through the serosa, the outer layer of the stomach wall (T4a) or tumor has also spread to the surrounding organs, such as liver and oesophagus (T4b).

4.1.3.2 Node (N) The second factor (N) describes if cancer has developed into lymph nodes and it is divided in the next 4 main categories:

o N0 represents that no lymph node contains cancerous cells. o N1 is used when there are 1 or 2 cancerous cells in regional lymph nodes. o N2 means that cancer has spread to 3 to 6 nearby lymph nodes.

o N3 means there are more than 6 and less than 16 cancer cells in nearby lymph nodes (N3a) or that cancer has spread to more than 16 nearby lymph nodes (N3b).

4.1.3.3 Metastasis (M) Finally, the third factor (M) indicates the absence or presence of distant metastasis and it is divided in two categories. In the first category, there is no metastasis to other organ (M0), while in the second the cancer has spread to other parts of the body (M1).

4.1.4 Stages Grouping

The stages of the stomach cancer are determined using the combination of T,N,M categories that are used in TNM system. In particular, in gastric cancer there are four major stages (I, II, III, IV) that describe the depth and the size of the tumor and some of them are split in small subcategories, as they are presented in Table 4.1 and are explained below [43]:

o Stage I is the earliest stage of the stomach cancer. It is split in two subcategories, Stage IA and Stage IB.  Stage IA describes that the tumor is only limited in the inner layer of the stomach wall, without being spread in lymph nodes or other organs (T1,N0,M0).  Stage IB is used when either the tumor has spread in 1 or 2 lymph nodes near stomach, but not in other organs (T1,N1,M0), or the tumor has developed in the muscularis propria layer of the stomach wall, without affecting lymph nodes or other organs (T2,N0,M0).

o Stage II can be separated in Stage IIA and Stage IIB.  In Stage IIA, the tumor is only in the inner layer of the stomach wall, 3 to 6 nodes contain cancer cells and there is no metastasis (T1,N2,M0) or the tumor is extended in the muscularis propria layer and in 1 or 2 nearby lymph nodes, without affecting other organs (T2,N1,M0) or

cancer has spread in the outer layer of the stomach wall, but it has not developed in lymph nodes or other organs (T3,N0,M0).  In Stage IIB, the tumor is only in the inner layer of the stomach wall, 7 or more nodes contain cancer cells and there is no metastasis (T1,N3,M0) or the tumor is extended in the muscularis propria layer and in between 3 to 6 nearby lymph nodes, without affecting other organs (T2,N2,M0) or cancer has spread in the outer layer of the stomach wall, and it has developed in 1 or 2 lymph nodes, but not in other organs (T3,N1,M0) or the cancer has extended in the serosa layer, without involving lymph nodes or distant organs (T4,N0,M0). o Stage III is divided in Stage IIIA, Stage IIIB and Stage IIIC.  In Stage IIIA, cancer has spread in the muscularis propria layer and in 7 or more nearby lymph nodes, but it has not developed in other organs (T2,N3,M0) or cancer has spread in the outer layer of the stomach wall and in 3 to 6 lymph nodes without involving other organs (T3,N2,M0) or it has developed in the serosa layer and in 1 or 2 lymph nodes, but not in distant organs (T4,N1,M0).  In Stage IIIB, cancer has spread in the subserosa layer of the stomach, in 7 or more nearby lymph nodes, but it has not developed in other organs (T3,N3,M0) or cancer has spread in the serosa layer of the stomach wall and in 3 to 6 lymph nodes without involving other organs (T4,N2,M0) or it has developed in the serosa layer and in 0,1 or 2 lymph nodes, but not in distant organs (T4,N0 or N1,M0).  In Stage IIIC, cancer has spread in the serosa layer and in 7 or more nearby lymph nodes, but it has not been extended in other organs (T4,N3,M0) or it has developed in the serosa layer and in more than 3 lymph nodes, but not in distant organs (T4,N2 or N3,M0). o Stage IV is used for any tumor that has developed outside the stomach, it has metastasized in distant organs and it may or may not affect lymph nodes. It is described as any T, any N and M1 in the TNM system.

Stage Tumor (T) Node (N) Metastasis (M) Stage IA T1 N0 M0 Stage IB T1 N1 M0 T2 N0 M0 T1 N2 M0 Stage IIA T2 N1 M0 T3 N0 M0 T1 N3 M0 Stage IIB T2 N2 M0 T3 N1 M0 T4 N0 M0 T2 N3 M0 Stage IIIA T3 N2 M0 T4 N1 M0 T3 N3 M0 Stage IIIB T4 N2 M0 T4 N0,N1 M0 Stage IIIC T4 N3 M0 T4 N2,N3 M0 Stage IV Any T Any N M1 Table 4.1: Stage grouping system for gastric cancer

4.2 Pancreatic Tumor Classification

In respect to pancreatic tumors, there are four major classification systems. In particular, in Section 4.2.1 pancreatic cancer is divided in four categories based on MD Anderson Cancer Center (MDACC), while in Section 4.2.2 World Health Organization (WHO) classification which divides tumors into intestinal and diffuse types is presented. Moreover, in Section 4.2.3 the TNM classification which reflects the depth of tumor infiltration (T), node involvement (N) and the presence of distant

43 metastases (M) is analysed, while in Section 4.2.4 the stages grouping which categorize tumors into groups is presented.

4.2.1 MD Anderson Cancer Center (MDACC) classification

A simple staging system that is used for treatment purposes by doctors is the MD Anderson system, which puts tumors into four categories based on if they can be removed with surgery or not and where the cancer has spread. This system is usually used to describe pancreatic exocrine tumors rather than neuroendocrine tumors and it is important, because based on it, doctors can recommend the most appropriate treatment method.

4.2.1.1 Resectable This type of cancer can be entirely removed by a surgery. The tumor is only in the pancreas or it has grown beyond it, but without being spread into arteries, veins or other organs [45,46]. Almost 10% to 15% of pancreatic tumors are found in this stage. It can be said that this stage corresponds to stages IA, IB, and IIA in the TNM system.

4.2.1.2 Borderline Resectable In this stage, a tumor may is difficult to be removed, but after chemotherapy or radiation cherapy when the tumor will have shrunk it will be possible to be extracted [45,46]. This stage is used to describe some cancers in stage III of the TNM system.

4.2.1.3 Locally Advanced This type of cancer cannot be removed completely with surgery, because it has grown into blood vessels or in nearby organs, but it has not been spread to distant organs [45,46]. Approximately 35% to 40% of pancreatic cancers are detected in this stage. This stage includes stage IIB and most cancers in stage III of the TNM system.

4.2.1.4 Metastatic In this stage, cancer is not located only in the pancreas, but is has grown into distant organs and it cannot be entirely removed surgically [45,46]. Almost 45% to 55% of cases are diagnosed in this stage.

4.2.2 World Health Organization (WHO) classification

The World Health Organisation (WHO) is used to classify endocrine tumors in three main categories, based on their appearance under microscope. This system is used to measure the process of cells dividing. The major categories are [47]:

o Well-differentiated endocrine tumor that can have benign or uncertain behaviour. In both cases tumor is limited to the pancreas, but in the first case its size is equal or less than 2 cm, while in the second it is larger than 2 cm. In this category, there are a small number of cells actively dividing and the tumor cells look similar to the surrounding tissues. It is usually described as low- grade, because it is not so aggressive.

o Well-differentiated endocrine carcinoma is morphologically similar to well- differentiated endocrine tumor, the tumor cells look similar to the surrounding tissues, but it has a higher number of cells actively dividing. It is usually described as low- or intermediate-grade, according to its biological aggressiveness.

o Poorly-differentiated endocrine carcinoma is used to describe a malignant carcinoma. In this category, tumor cells look very different from the surrounding tissues and it is claimed to be high-grade, as cancer is aggressive and quickly spread in other tissues.

4.2.3 TNM staging system

The American Joint Committee on Cancer (AJCC) suggested the TNM staging system which provides information about the size of the tumor and how far the cancer has spread [46]. In TNM system, T (Tumor) represents the size and location of the primary tumor into the pancreas, N (Node) is used to describe if cancer has spread to lymph nodes and M (Metastasis) refers to whether cancer has metastasized to other distant organs.

4.2.3.1 Tumor (T) According to TNM system, T is used to describe the size and location of the primary tumor and it is divided in the next four main categories:

o T1 is an early stage and it means that tumor is only in the pancreas and it is equal or less than 2 cm. o T2 describes that tumor is only in the pancreas, but it is larger than 2 cm. o T3 means that tumor has developed beyond the pancreas, but without affecting major blood vessels or nerves. o T4 is used when the tumor has expanded outside the pancreas into nearby major arteries or veins.

4.2.3.2 Node (N) The second term (N) is used to determine if cancer has spread into lymph nodes and it is divided in the following two major categories:

o N0 represents that no lymph node contains cancerous cells. o N1 is used when cancer has expanded to nearby lymph nodes.

4.2.3.3 Metastasis (M) The third factor (M) indicates if the cancer has spread to other organs and it is divided in two categories. In the first category, there is no metastasis to other organ (M0),

46 while in the second the cancer has developed to other parts of the body and on distant lymph nodes (M1). The most frequent affected organs are liver, peritoneum and lungs.

4.2.4 Stages Grouping

The combination of T,N,M categories that are used in TNM system provides an overall grouping in stage I,II,III or IV. This system is divided in four categories that are presented in Table 4.2 and are analysed below [46,48]:

o Stage I is an early stage that is used when tumor is only limited in the pancreas. It can be divided in two subgroups, Stage IA and Stage IB, based on the size of the tumor. It is comparable to T1 or T2, N0 and M0 in the TNM system.

o Stage II can be separated in Stage IIA and Stage IIB. In the first case, tumor has spread outside the pancreas, without involving major blood vessels or lymph nodes. It is described as T3, N0 and M0 in the TNM system. In the second case, cancer may or may not have expanded beyond pancreas and lymph nodes are involved. It is equivalent to T1, T2 or T3, N1 and M0 in the TNM system.

o Stage III is used when tumor has spread in nearby arteries and veins outside the pancreas and it may involve lymph nodes. There is no metastasis in other organs and it is equal to T4, N0 or N1 and M0 in the TNM system.

o Stage IV is used for any tumor that has developed outside the pancreas, it has metastasized in distant organs and it may affect lymph nodes. It is similar to Any T, Any N and M1 in the TNM system.

The below table (Table 4.2) summarised the stages with the corresponding values in the TNM system:

Stage Tumor (T) Node (N) Metastasis (M) Stage IA T1 N0 M0 Stage IB T2 N0 M0 Stage IIA T3 N0 M0 Stage IIB T1,T2,T3 N1 M0 Stage III T4 Any N M0 Stage IV Any T Any N M1 Table 4.2: Stage grouping system for pancreatic cancer

Chapter 5

Digital Human Phantoms

This section presents some information about DHP (Digital Human Phantom) images. Firstly, Section 5.1 provides the way of DHP data production and Section 5.2 explains the format of DHP images that are used in this project.

5.1 DHP Production

Computational human phantoms are computer models of the human body that are used to represent the internal structure of the body [49]. Computer-generated phantoms have been developed to represent humans in different ages and anatomies. Therefore, they can be used to quantitatively test and improve medical imaging devices and techniques. [50]. There are several computational human phantoms that are used nowadays. In this project, a digital human phantom that is provided by RIKEN (Saitama, Japan) Bio-research Infrastructure Construction Team under no- disclosure agreement between the University of Manchester and RIKEN will be used. The usage is approved by RIKEN ethical committee.

In particular, Digital Human Phantom is a computer-generated phantom that provides models which represent the structure of human body. These models can be used to perform experiments that would be risky to apply on real subjects [51]. The DHP phantom that is currently used in this project is produced by scanning a healthy normal Japanese man [52,53]. In order to produce the digital human phantom the below steps are followed [52,53]:

1. Firstly, an entire human body is scanned using MRI from head to feet. Each scan shows the cross section of the human body orthogonal to the direction of

the backbone [52,53]. The distance between two successive MRI scans can vary. For instance, the original images obtained by the group had 1 mm resolution. However, these images were resampled to 0.5 mm and are now being used in this project. 2. Using the knowledge of doctors, the DHP images that are generated from the MRI scan are segmented. Thereby, each tissue that is illustrated on an image can be identified. In this project, the size of each pixel is 0.5 mm × 0.5 mm. Therefore, each pixel has an identification tissue number. For instance the number 33 is used for stomach and the number 29 for pancreas. The MRI scanned image is replaced with a stream of integers without the Cartesian coordinate. The file with these integers has a name that characterizes the distance of each MRI scan phantom from the head.

In the below figures two DHP images are depicted. Both of them have passed from the two steps mentioned before and they represent the internal structure of the upper abdomen. Figure 5.1 shows clearly the liver and stomach, while Figure 5.2 illustrates plainly the liver and pancreas. All tissues which are presented in these images have a unique gray-scale color. For instance, stomach is represented from number 33 and pancreas from number 29. In this project, we will deal only with these two organs. Finally, all images have a unique file name which shows the height from the patient’s head.

Figure 5.1: DHP image that includes stomach (v1_01090.pgm)

Figure 5.2: DHP image that includes pancreas (v1_01150.pgm)

5.2 DHP Structure

All the DHP images comprise PGM (Portable Gray Map) data files in the two dimensional space [54]. The PGM format is a widely known grayscale file format and an array of arbitrary integers [55]. The values range is from 0 (black) up to a maximum effective value (white). Each gray value is a number between zero and the maximum value. All PGM files contain only one ASCII decimal value per pixel. In Table 5.1, there is the main structure of a pgm data file. In particular, the first line of the file describes the type of the file and it is usually called as “magic number”. In the pgm files, the magic identifier can be “P2” that corresponds to ASCII characters or “P5” for the binary form of the data. In Table 5.1, the next line is used as comment line. In the pgm files, every line that starts with “#” character contains comments with respect to the limitation of 70 characters per line. The third line consists of the width (first value) and the height (second value) of the images which are separated with whitespace. The next line shows the maximum grayscale value of the image. Finally, the last line contains all the numbers (ASCII decimal values) that represent the values of the pixels.

P2 pgm format (ASCII version) # testImage comment line i j width=i height=j (ASCII decimal values) k maximum effective gray value(ASCII decimal) 0 0 0 0 0 2 2 0 k k 1 1 gray scale values from 0 to k (k>6) 0 6 0 2 0 6 4 2 0 0 4 0 Table 5.1: The main structure of a pgm data file

In Figure 5.3, a sample DHP image is produced. This image consists of four different colors which represent four different values (Table 5.2). Especially, the outermost side has the value 0 (black color), while the innermost part in the center of the image has the value 180 which is the maximum effective value and it appears with white color. This produced image has also other two gray-scale value (25,75). The value 25 is represented with a dark gray color as a line in the second and seventh line of the image, while the value 75 is shown with a light gray color as a line in the third and sixth line of the image.

P2 #test1 8 8 180 0 0 0 0 0 0 0 0 0 25 25 25 25 25 25 0 0 75 75 75 75 75 75 0 0 180 180 180 180 180 180 0 0 180 180 180 180 180 180 0 0 75 75 75 75 75 75 0 0 25 25 25 25 25 25 0 0 0 0 0 0 0 0 0 Figure 5.3: DHP first sample image Table 5.2: Values of first sample image

In Figure 5.4, a second sample DHP image is provided. This image consists of five different colors which represent five different values (Table 5.3). More specific, the outermost side is like a gray square with the value 100 and inside it there is a smaller white square with the value 150 which is the maximum effective value. The center of the image has the value 0 (minimum value) and it is illustrated with black color. Finally, next to the center of the image, there are also two different gray-scale values (50, 75).

P2 #test2 9 5 150 100 100 100 100 100 100 100 100 100 100 150 150 150 150 150 150 150 100 100 150 75 50 0 50 75 150 100 100 150 150 150 150 150 150 150 100 100 100 100 100 100 100 100 100 100

Figure 5.4: DHP second sample image Table 5.3: Values of second sample image

Chapter 6

Design and Implementation

Chapter 6 provides some general information about mathematical models and it illustrates all the steps that were followed during the implementation. In particular, Section 6.1 illustrates the definition of mathematical models and their types and it explains the usefulness of them in several fields, while Section 6.2 presents details about the implementation. Especially, Section 6.2.1 analyzes the methodology that was followed in order to find and save the images that contain stomach and pancreas. Furthermore, in Sections 6.2.2-6.2.4 the mathematical models that were developed and implemented in order to represent some types of gastric and pancreatic tumor are illustrated. Additionally, Section 6.2.5 shows all the steps that were developed in order to introduce cancer in DHP images. Finally, Section 6.2.6 explains the graphical user interface that was created so that the user is able to introduce the type of cancer (pancreatic or gastric), the type of tumor (ellipsoid, spheroid, paraboloid), the position where the tumor will be inserted, the size of the tumor along x-, y- and z-axis and the degrees of tumor’s rotation.

6.1 Mathematical Models

A mathematical model is an abstract model that is used to describe the behaviour of a system, the different aspects of the real world, and their interactions [56,57]. Based on Eykhoff definition a mathematical model is “a representation of the essential aspects of an existing system -or a system to be constructed- which presents knowledge of that system in usable form” [58].

Mathematical models are widely used to describe and study a physical system, so that its inner form and behaviour can be easily comprehensible. These models use several

54 mathematical structures, such as equations, graphs or tree diagrams to represent real world problems. They also provide an abstraction that reduces a problem to its essential characteristics.

The major utility of mathematical modelling is that it can explain and translate a real physical situation to a conceptual mathematical problem (Figure 6.1) to embrace the intended purpose and it is able to solve similar problems methodically [58]. It also takes into consideration the most essential aspects that characterize a physical problem ignoring the non-essential ones. [57].

Figure 6.1: The conversion of a real-world problem into a mathematical problem http://math4teaching.com/wp-content/uploads/2011/04/modeling.jpg

Although, there are various types of models that are in common use, the four main categories in mathematical modelling approaches are the empirical models, simulation models, deterministic and stochastic models. Empirical modelling defines a model using observed relationship among experimental data and they are widely usable to describe trends and for forecasting. Simulation model is a mathematical model which generates a scenario based on a set of rules through a computer [59]. Its main advantage is that it can be used to study complex systems or events that will be hard to be examined in real life. Deterministic modelling uses a set of equations to model the outcome of an event ignoring the random variation. Therefore, it always predicts the same outcome from a given starting point [59,60]. On the other hand, in stochastic modelling events are considered to occur with some probability. As a consequence, a stochastic model gives probabilities when the equations are formulated and it predicts the distribution of possible outcomes [59,60]. There are also other segregations of

55 models such as static or dynamic. Concisely, static models are at steady state and are independent of time, while dynamic models change with respect to time.

Nowadays, mathematical models are increasingly noteworthy and have a key role in various fields. The application of mathematical models is not limited to the technological field, but also in natural, medical and social sciences and engineering disciplines. In particular in biology science mathematical modelling has a great impact and it is the only way to attain quantitative understanding of the real system. Indeed, a wide range of mathematical models and shapes apply to bioscience field and more specific they can be used to approach different types of cancer [61,62,63]. However, according to Howard Emmons “the challenge in mathematical modelling is not to obtain the most extensive descriptive model, but to produce the simplest possible model that incorporates the major features of the phenomenon of interest” [64]. In this work, in the following subsections, we propose some simple static, deterministic mathematical models that can be used to describe several types of pancreatic and gastric cancer. These models that have been translated into algorithms allow us to simulate various tumors with different sizes and shapes in a short time period and cost-effectively.

6.2 Implementation

Before focusing on the objective of this project, the images that will be used should be extracted. For this purpose, a shell script that is presented in Section 6.2.1 is created. For the implementation of the algorithms that are shown in the following subsections MATLAB is preferred rather than other languages, because it is mathematically robust with build-in routines and toolboxes. It is widely used for computer vision, signal and image processing, data visualization and machine learning projects by millions engineers worldwide and it offers high-performance numerical computations [65]. It also contains built-in functionality that is useful for the creation of the Graphical User Interface. In particular, the project was implemented in MATLAB R2012a edition.

6.2.1 Shell script to identify the location of stomach and pancreas into the DHP images

In this project, we focus on the creation and insertion of a tumor inside the stomach and pancreas of a healthy scanning Japanese man. However, the images that were given to us include parts of the whole body (scanning images from head to tail). Before trying to identify the images which contain these organs, the given file that contains the numbers of all tissues was read. According to it the number 33 represents stomach and the number 29 is used for pancreas depiction. In order to find the images that contain stomach and pancreas, a shell script is implemented (Appendix A). The script creates the StomachImages and PancreasImages folders and it saves there the images that contain stomach and pancreas respectively. More specific, StomachImages folder comprises 171 images (v1_01025.pgm-v1_01196.pgm) that contain stomach and PancreasImages folder includes 103 images with pancreas (v1_01107.pgm-v1_01210.pgm).

6.2.2 Mathematical model for the representation of gastric and pancreatic cancer (ellipsoid tumor)

Based on the background research, the early gastric cancer types (I,IIa,IIb), the advanced gastric cancer type I and some types of pancreatic cancer can be represented using an ellipsoid shape. The general form of an ellipsoid [66,67] in Cartesian coordinates is given by:

x2 y2 z2 (1) + + = 1 a2 b2 c2

where a,b,c are the distances from the centre to the surface of an ellipsoid along three vertical axes. Especially, a is the horizontal radius, b is the vertical radius and c gives the “depth radius” of an ellipsoid in the third axis (Figure 6.2).

Figure 6.2: Ellipsoid shape with a,b,c parameters

If b is greater than a, the shape is a prolate ellipsoid of revolution (Figure 6.3). On the other hand, if a is greater than b, the surface is an oblate ellipsoid of revolution (Figure 6.4).

Figure 6.3: A prolate ellipsoid (a=25, b=50) Figure 6.4: An oblate ellipsoid (a=50, b=25)

For this project, a surface (S) of an ellipsoid tumor is described as follow:

S = b2c2(x − a)2 + a2c2(y − b)2 + a2b2(z − c)2 ≤ a2b2c2 (2)

This means that every point P=(x,y,z) will be part of an ellipsoid if and only if it satisfies (2).

6.2.3 Mathematical model for the representation of early gastric cancer type I and pancreatic cancer (spheroid tumor)

Based on the background research, the early gastric cancer type I and some types of pancreatic cancer can be represented using a spheroid shape. The general form of a spheroid [68,69,70] in Cartesian coordinates with z as a symmetry axis is given by:

x2 + y2 z2 (3) + = 1 r2 c2

where r is the radius of the spheroid and c is the distance from the centre point to the pole along the z- axis (Figure 6.5). In case where r is equal to c the shape is a sphere (Figure 6.6).

Figure 6.5: Spheroid shape with r and c Figure 6.6: Sphere shape with parameters parameters r=30 and c=30

For this project, a surface (S) of a spheroid tumor is described as follow:

S = c2(x − r)2 + c2(y − r)2 + r2(z − c)2 ≤ r2c2 (4)

This means that every point P=(x,y,z) will be part of a spheroid if and only if it satisfies (4).

6.2.4 Mathematical model for the representation of gastric cancer (paraboloid tumor)

Based on the background research, the early gastric cancer IIc and the advanced gastric cancer type II can be represented using a shape similar to paraboloid. The general form of an elliptic paraboloid [71,72] in Cartesian coordinates is given by:

x2 y2 푧 (5) + − = 0 a2 b2 푐

Moreover, sections parallel to the xz- and yz-planes are parabolas, while sections parallel to the xy-plane are ellipses or circles if a is equal to b (Figure 6.7). In case where c>0 it opens upward and if c<0 it opens downward. The general form of a hyperbolic paraboloid [71,72] is defined as:

x2 y2 푧 (6) − − = 0 a2 b2 푐

Moreover, sections parallel to the xz- and yz-planes are parabolas, while sections parallel to the xy-plane are hyperbolas (Figure 6.8). The shape of a hyperbolic paraboloid is like a saddle. Both paraboloids (elliptic, hyperbolic) are symmetric in the xz- and yz-planes.

Figure 6.7: An elliptic paraboloid Figure 6.8: A hyperbolic paraboloid

Furthermore, every point of a paraboloid cylinder [71,72] satisfies the equation:

푥2 = 4푐푦 (7) This equation does not depend on z plane and it describes a parabola in the xy-plane that can be translated in the z-plane (Figure 6.9). Finally, an elliptic cylinder [71,72] can be described from the below equation:

푥2 푦2 (8) + = 1 푎2 푏2

As before, equation (8) does not depend on z coordinate and it describes an ellipse in the xy-plane that can be translated along the z dimension (Figure 6.11).

For this project, a surface (S) of a tumor with a shape similar to paraboloid is created using the following form:

S = (b푥2 − 푎푦)2 + (푧2 + 푦2) ≤ 100ab (9)

Where a, b: positive numbers. This means that every point P=(x,y,z) will be part of the paraboloid if and only if it satisfies (9). If we try to analyze the above form and separate the two terms before the inequality, then the term (b푥2 − 푎푦)2 ≤ 100ab is used to create a surface of two parabolic cylinders (Figure 6.9, Figure 6.10), while the term (푧2 + 푦2) ≤ 100ab creates an elliptic cylinder (Figure 6.11).

Figure 6.9: A parabolic Figure 6.10: The term Figure 6.11: An elliptic cylinder (퐛풙ퟐ − 풂풚)ퟐ ≤ ퟏퟎퟎ퐚퐛 cylinder and the term (풛ퟐ + 풚ퟐ) ≤ ퟏퟎퟎ퐚퐛

The combination of these two shapes (Figure 6.10, Figure 6.11) creates the surface S which is described in equation (9) and the paraboloid tumor in 3D space that describes some types of gastric cancer (Figure 6.12, Figure 6.13).

Figure 6.12: A view of the paraboloid Figure 6.13: Another view of the same tumor paraboloid tumor

6.2.5 Implementation of the algorithm for tumor introduction

Before implementing our algorithm the selected images should be read. For this reason, an existing function [73] is used. This function opens an ASCII PGM file and reads the data. It inputs the name of the file and outputs the gray scale data that is read from the file. As it has been already mentioned in subsection 6.2.1, StomachImages folder comprises 171 images (v1_01025.pgm-v1_01196.pgm) that contain stomach and PancreasImages folder includes 103 images with pancreas (v1_01107.pgm- v1_01210.pgm). As the two organs are located close to each other, the images from v1_01107.pgm to v1_01196.pgm contain both of them. Therefore, the total number of unique images is 185. In order to improve the performance of the program, after the first reading of all images, we save all of them (185 images) in a 3-dimensional array with the name array3d. In this way, every time that the user executes the program the command load array3d.mat is executed and function [73] that is used to read (or write) the data is skipped. If the user prefers to test the program with another dataset, s/he should make the appropriate changes in the load_images() function. This function

62 calls function [73] in order to read the new dataset and stores the new values into array3d.mat. For cancer representation in DHP images, the algorithm uses a value that does not represent any tissue. This value is 60 and it represents cancer into images. For every image that is read, the point (x,y,z) that is given to place the center of the tumor is checked to determine whether this position is inside the stomach/pancreas or not. If it is outside, a message explaining that it is not possible to insert the tumor in that position is displayed and the program is terminated. Otherwise, the tumor is inserted at that position. Additionally, depending on the user’s preference the tumor can be rotated. The acceptable values range from -180 to 180. The rotation is accomplished using the imrotate() build-in function. This function receives as input the tumor and the degree of rotation that the user asks and outputs the rotating tumor. If the degree of rotation is a negative value, the rotation is in a clockwise direction (Figure 6.14), while if it is equal to zero, the tumor appears as in Figure 6.15. In case where the degree of rotation is a positive value, the tumor is rotated counterclockwise (Figure 6.16). Finally, if the tumor is ellipsoid or spheroid it can be cropped or extended beyond the border of the stomach. In the case of the paraboloid tumor, it can be developed in the wall of the stomach and only inside it. The user is also able to create tumors with different sizes. In case of ellipsoid or spheroid the parameters a,b and r respectively affect the size of the tumor. Obviously, if the parameter a, b or r is increased, the shape will be bigger. In the case of paraboloid, the first two parameters affect shape’s thickness and aperture. Indeed, if thickness parameter increases the tumor becomes smaller (Figure 6.17, Figure 6.18), while if aperture parameter increases the aperture of the paraboloid closes more (Figure 6.18, Figure 6.19). In all cases, the third parameter (axis c) represents the size of tumor in z-direction and it refers to the total number of images in which the tumor will be introduced. In case of paraboloid, axis c represents exactly the total number of layers. However, in case of spheroid or ellipsoid tumor the total number of layers where cancer will be inputted is equal to double axis c. This happens, because different procedures were followed during the implementation of the tumors. Therefore, if the user prefers to introduce a spheroid or ellipsoid tumor in 30 layers in z-direction, axis c should be defined as 15. However, if s/he wants to insert a paraboloid tumor in 30 layers, axis c should be set 30.

After the introduction of the tumor and in order to create new images with cancer in the stomach or pancreas, an existent function [73] is applied. This function writes grayscale data into an ASCII PGM file.

Figure 6.14: Paraboloid Figure 6.15: Paraboloid Figure 6.16: Paraboloid tumor with degree of tumor with degree of tumor with degree of rotation= -100 rotation= 0 rotation= 50

Figure 6.17: Paraboloid Figure 6.18: Paraboloid Figure 6.19: Paraboloid tumor with (Thickness, tumor with (Thickness, tumor with (Thickness, Aperture)=(6,1) Aperture)=(10,1) Aperture)=(10,3)

6.2.6 Implementation of the Graphical User Interface (GUI)

A graphical user interface was created so that the user will be able to execute the program. The GUI that was implemented consists of two pages. In the first page (Figure 6.20) the user can inspect the images that contain stomach and pancreas, while in the second page s/he is able to insert the type of cancer and the features of the tumor.

o Previous Image, Next Image: The first page of the GUI is essential to inform and familiarize the user with the DHP images that contain stomach and/or pancreas. In particular, the user can examine all the images that contain stomach and/or pancreas in order to select the position where the tumor will be inserted. For this purpose, the buttons Previous Image and Next Image are used to go towards all the layers in stomach and/or pancreas images. When the user decides the image that will constitute the centre layer (centre z) of the tumor, s/he should click on the exact position inside the image (centre x,y) where the tumor will be inputted and immediately the fields below centre (x,y,z) will be filled with the appropriate values based on user’s selection (Figure 6.20).

Figure 6.20: The first page of the GUI with tumor’s position

o Next Page: After the selection of the tumor’s position the user is able to press the Next Page button (Figure 6.20) and move forward the second page of the GUI. The values of centre (x,y,z) of the tumor from the first page are passed as arguments in the second page and they are registered in the appropriate field (Figure 6.21), so that the user is able to avoid memorizing the precise position. However, these values are not restrictive and the user has the ability to change them in the second page of the GUI (Figure 6.21).

Figure 6.21: The second page of the GUI with tumor’s position

The second page of the GUI requires certain inputs and the first fields that should be selected are the type of cancer and the type of tumor. Afterwards, the user should fill in numerical values in the blank spaces. These values describe the position of the tumor inside the DHP images, its size and its characteristics.

o Type of cancer, Type of tumor, whole tumor: Firstly, the user should select between two available types of cancer; namely pancreatic or gastric. As it is clear from Figure 6.22 and Figure 6.23 in the case of pancreatic cancer the type of tumor can be spheroid or ellipsoid, while in gastric cancer the user has also the option to select paraboloid tumor. It is also observed that in cases of spheroid or ellipsoid tumor for pancreatic or gastric cancer the user is able to select the option of whole tumor. However, in case of paraboloid tumor according to the literature survey the tumor can be introduced only inside the

stomach, so the option of whole tumor is disabled, as we can see in Figure 6.24.

Figure 6.22: Pancreatic cancer, Spheroid tumor

Figure 6.23: Pancreatic cancer, Ellipsoid tumor

Figure 6.24: Gastric cancer, Paraboloid tumor

o Parameters: After the selection of type of cancer and tumor the user should set the parameters. The first parameter is the centre (x,y,z) that refers to the

position where the tumor will be introduced. In particular, x and y represents the x and y values in each DHP image, while z represent the centre layer where the tumor will be inserted. In case of spheroid tumor (Figure 6.22) the user is able to introduce the value of radius r and the axis c of the tumor, which represent the number of the layers where the tumor will be added. In case of ellipsoid tumor (Figure 6.23) the user should set the values of axis a, b and c, while in case of paraboloid (Figure 6.24) the user has to insert the thickness and the aperture of the tumor, as also the number of layers (axis c) where the tumor will be introduced. Finally, in all the above cases, the tumor can be rotated from -180 to 180 degrees. o Forbidden Input Values: The program takes also into account the cases, where the user introduces invalid data. For instance, if the user inserts x or/and y value(s) that are outside from the selected organ (pancreas/stomach), then an error message appears and the program is ended (Figure 6.25). The same also happens in case where the user selects valid x and y, but invalid z coordinate. Therefore cancer cannot be inserted into that image, as the image does not contain the selected organ (Figure 6.26).

Figure 6.25: Forbidden values in (x,y), outside pancreas

Figure 6.26: Forbidden value in z coordinate, image without pancreas

Moreover, in all cases the values that are introduced should be numerical and positive. Otherwise, an error message appears again and the program is terminated (Figure 6.27). The only case where negative values are accepted is in the case of degree of rotation which takes values between -180 and 180. In this case, if the user inserts a value outside of this range, an error message appears and the user is informed that values between -180 and 180 should be entered (Figure 6.28).

Figure 6.27: Forbidden value for radius

Figure 6.28: Forbidden value for degree of rotation

o Add button: When the user inputs all the values, the Add button should be pressed. In this way the values that are inserted will be saved in the array that is shown below the input values (Figure 6.29). The user is able to insert as many tumors as s/he wants. The type of cancer and tumor can be the same or different and the tumor can be introduced whole or cropped according to the preferences of the user.

Figure 6.29: Three tumors are introduced using Add button

o Delete button: The program is also capable of deleting tumors. Especially, the user should select the line that is going to be removed and then click on Delete button. For instance in Figure 6.30 the second line is first selected, then the Delete button is pushed and finally the selected line is removed in Figure 6.31.

Figure 6.30: The second tumor is selected in order to be deleted

Figure 6.31: The second tumor has been removed

o Run button: When the user introduces all the values of each tumor and s/he adds it in the array, s/he is able to select the Run button to execute the program. Initially, the program introduces the first tumor that the user inserted and then the others. When the program finishes the results appears (Figure 6.32). The first image is the original image, while the second is the image with cancer. As the tumor can be inserted in more than one layer, the buttons Previous Image and Next Image show all the images in which the tumor is inserted. The third figure displays the tumor(s) that the user inputs. In Figure 6.32 two tumors are shown in the same figure, because they are added in the same layer (same centre z). Otherwise, if the tumors are inserted in different layers, the tumor that is inputted in the lower layer is shown first and the other

tumor is displayed afterwards. Finally, the last image shows the side view of the image with cancer, where the Previous Image and Next Image buttons next to it are used when more than one tumor have been inputted in order to see the side view of each tumor.

Figure 6.32: Results after the execution of the program

o Save button: In Run function we execute the program and we are able to see the images. However, the images with cancer are only displayed in the interface without being saved. Therefore, the images will be available while the interface is opened and they will be disappeared when the interface is closed. If the user prefers to keep the images with cancer s/he should select the Save button, which is used in order to create and save the images with cancer. This function calls the function [73] which writes grayscale data into an ASCII PGM file and save them in the path that has been already defined.

The main reason that we prefer to distinguish the Run from the Save function rather than having one function for both is for time-saving, as the writing function [73], that is called in Save function, is time-consuming. In addition to this, in some cases, the user may just want to see the results without save them

or s/he wants to try for different numbers before s/he finds the final appropriate values. Therefore, using the Run button the user can see the results quickly without wasting time waiting and when the images are suitable s/he may select to save them. o Previous Image, Next Image: After the execution of the program, the resultant images are presented (Figure 6.32, Figure 6.33). As we can see, between the first two figures (Original Image, Image with cancer) there are two buttons (Previous Image, Next Image). These buttons are also next to the fourth figure (Side view of the image) and they are used to go towards all the layers, where the cancer has been inserted. Indeed, between the first two figures when Previous Image or Next Image button is pressed, we go towards the previous or next layer respectively in the Original Image and in the Image with cancer concurrently. Moreover, the Previous Image and Next Image buttons next to the Side view of the image are used when more than one tumor has been inserted and they show the side view of each tumor.

o Waiting bar: While the user is waiting for the results in Run or in Save function, a waiting bar is appeared. This bar is useful, because it shows the time that has been completed (red line) and the remaining time (white space). When the program ends the red line is full and the waiting bar is disappeared (Figure 6.33).

o Previous Page: If the user needs to change the position of the tumor or add another tumor in a different position or organ, but s/he is not certain about the exact place of the tumor, s/he can select the Previous Page button and go back to the first page of the GUI where s/he is able to explore all the layers of stomach and pancreas images (Figure 6.33).

Figure 6.33: Waiting bar during the execution of Save function

Limitation of the Graphical User Interface (GUI)

In the first page of the GUI, if the user selects a position outside the borders of the images, the field centre (x,y,z) will be empty, until s/he clicks inside the image. However, if the user selects a position in a region that represents other organ, such as liver, then the field centre (x,y,z) of the tumor will accept that position, although it is not inside the stomach or pancreas. Nevertheless, in the second page of the GUI when the user tries to execute the program, a message will inform him that it is not possible to insert cancer in this position and the program will be ended, as it has been already mentioned and shown in Figure 6.25.

Chapter 7

Evaluation

Section 7 presents the results of our models in gastric and pancreatic cancer representation. Especially, Section 7.1 shows the evaluation metrics that are considered in order to test the precision and the accomplishment of our algorithm. Additionally, Section 7.2 and 7.3 compares the degree of relevance between the produced and the real medical images in gastric and pancreatic cancer representation respectively. Moreover, Section 7.4 provides details about the efficiency of our implementation and more specific the time that the program needs in order to be executed. Finally, Sections 7.5 and 7.6 demonstrate the effectiveness and the impact of this work in gastric and pancreatic cancer representation and in our research group.

7.1 Evaluation Metrics

After the end of the implementation phase and in order to evaluate the effectiveness and the precision of this project in gastric and pancreatic cancer representation, the criteria that are proposed by OECD [74] and were also used by [53] will be considered. In particular, these criteria are the following:

o Relevance: The images produced by this project are compared with real medical images from patients with gastric or pancreatic cancer in order to evaluate their similarity and the precision of this project in cancer representation.

o Efficiency: The time needed for the execution of the program and the production of the images is calculated for different scenarios (various tumor’s sizes, several layers).

o Effectiveness: The extent in which the objectives are accomplished is estimated. Moreover, we measure which objectives were successful and if there are unsuccessful aims we state the difficulties.

o Impact: The positive changes and the major effects of this work in our group are checked. In particular, we take into consideration how similar with real medical images and how useful the results are for the research team that is currently working on cancer detection mechanisms.

7.2 Relevance in gastric cancer representation

In the following images, real medical images from patients with several types of stomach cancer are presented and are compared with the produced images in order to test how remarkable their similarity is.

7.2.1 Relevance in early gastric cancer types I, IIa, IIb and advanced type I In Figure 7.1, Figure 7.3 and Figure 7.5 real medical images from patients with gastric cancer can be seen, while in Figure 7.2, Figure 7.4 and Figure 7.6 the produced images are displayed. In particular, in Figure 7.2 a cropped ellipsoid tumor is added in position (x,y,z)=(604,215,112) with (a,b,c)=(9,20,4). Before the introduction, the tumor is rotated 45 degrees. In Figure 7.4 a cropped ellipsoid tumor is inputted in position (x,y,z)=(605,220,62) with (a,b,c)=(8,22,4). Before the introduction, it also rotated -25 degrees. Finally, in Figure 7.6 a whole ellipsoid tumor that extends out of stomach borders is inserted in position (x,y,z)=(676,288,48) with (a,b,c)=(10,20,4). It is clear from all the above figures that the produced images are similar to the real images.

Figure 7.1: A 76-year old man with a T1a gastric carcinoma. CT scan illustrates a thickening of the inner layer (arrow) http://www.intechopen.com/source/html/38930/media/image1.png

Figure 7.2: Produced image (v1_01112.pgm) with gastric cancer type T1a

Figure 7.3: A 64-year old woman with a T3 gastric carcinoma. CT scan demonstrates a mass in the lesser curvature (arrow) and a perigastric fat stranding (arrowhead) http://www.intechopen.com/source/html/38930/media/image3.png

Figure 7.4: Produced image (v1_01062.pgm) with gastric cancer type T3

Figure 7.5: A 70–year-old woman with a low grade gastrointestinal stromal tumor. CT scans present submucosal soft tissue mass (arrow) in greater curvature side of stomach. References: Dept. Of Diagnostic Radiology, Hanyang University Kuri Hospital - Kuri City/KR http://posterng.netkey.at/esr/viewing/index.php?module=viewimage&task=&me diafile_id=519061&201311270501.gif

Figure 7.6: Produced image (v1_01048.pgm) with GIST type of stomach cancer

7.2.2 Relevance in early gastric cancer type IIc and advanced type II In Figure 7.7 and Figure 7.9 real medical images from patients with stomach cancer are presented, while in Figure 7.8, Figure 7.10 and Figure 7.11 the produced images are shown. Especially, in Figure 7.8 a paraboloid tumor is inputted in position (x,y,z)=(610,230,81) with (thickness,aperture,c)=(12,2,3). Before the insertion, the tumor is rotated -155 degrees.

In Figure 7.10 and Figure 7.11 a paraboloid tumor with (thickness,aperture,c)=(7,2,10) and 180 degrees of rotation is placed in position (x,y,z)=(706,228,83) in transverse1 and sagittal2 plane respectively. It is obvious from Figure 7.7 to Figure 7.11 that the produced images are comparable and similar with the real images.

Figure 7.7: Axial CT illustrates thickening (arrows) and mucosal enhancement of the lesser curvature of the stomach http://api.ning.com/files/7uq07US2ifQ29fTdUonXMBLpbCxvyEeHm- bPinjt3XuSkaCpLTKaaJyegRfPawjNg8rK9s782FHlH- MMeuwx4KsXMSFvsPOM/gastriccancer.jpg?width=320&height=241

Figure 7.8: Produced image (v1_01081.pgm) with gastric cancer

1 Transverse or horizontal plane is an imaginary plane parallel to the ground which separates the body into superior and inferior parts 2 Sagittal plane is an imaginary plane perpendicular to the ground which divides body into right and left

Figure 7.9: CT transverse scan shows an irregular wall thickening (gastric carcinoma) on the antro-pyloric tract (arrow) http://www.intechopen.com/source/html/16461/media/image4.png

Figure 7.10: Produced image (v1_01083.pgm) with gastric carcinoma

Figure 7.11: Side view of the image with gastric carcinoma

7.3 Relevance in pancreatic cancer representation

In the next images, real medical images from patients with pancreatic cancer are shown and they are compared with the produced images in order to test how relevant and comparable they are.

7.3.1 Relevance in exocrine tumors In Figure 7.12, Figure 7.13 and Figure 7.15 real medical images from patients with pancreatic cancer are shown while in Figure 7.14 and Figure 7.16 the produced images are displayed. Specifically, in Figure 7.14 a whole spheroid tumor is introduced in position (x,y,z)=(582,208,174) with (r,c)=(20,5). The degrees of rotation are equal to zero. In Figure 7.16 two cropped ellipsoid tumors are added. The first one in position (x,y,z)=(530,145,170) with (a,b,c)=(15,23,6) and 15 degrees of rotation. The second tumor is placed in position (x,y,z)=(470,212,170) with (a,b,c)=(15,18,6) and zero degree of rotation. It is clear from Figure 7.12 to Figure 7.16 that the produced images are similar to the real images.

Figure 7.12: a. MRI scan image shows a Figure 7.13: b. CT scan image illustrates a pancreatic tail cyst cystic fluid mass 4cm in diameter on the http://www.serena.unina.it/index.php/jop/a tail of the pancreas in a 68-year-old woman rticle/viewFile/1905/1981/9587 http://www.serena.unina.it/index.php/jop/a rticle/viewFile/1905/1981/9588

Figure 7.14: Produced image (v1_01174.pgm) with a cyst mass in the tail of the pancreas

Figure 7.15: MRI image demonstrates two lesions (red arrows) with 2.3 cm and 1.8 cm in diameter in body of pancreas in a 60-year-old patient http://www.serena.unina.it/index.php/jop/article/viewFile/1327/1578/7039

Figure 7.16: Produced image (v1_01170.pgm) with two tumors inside the body of pancreas

Additionally, Figure 7.17, Figure 7.19 and Figure 7.21 show real medical images, while Figure 7.18, Figure 7.20 and Figure 7.22 display the produced images. In particular, in Figure 7.18 and in Figure 7.20 a cropped spheroid tumor with (r,c)=(20,4) is introduced in position (x,y,z)=(515,160,158) and in position (x,y,z)=(660,270,121) respectively. Furthermore, in Figure 7.22 a cropped ellipsoid tumor with (a,b,c)=(17,22,4) is placed in position (x,y,z)=(460,195,172). It is clear from all the above figures that the similarity of produced and real medical images is remarkable.

Figure 7.17: A CT scan shows a ductal adenocarcinoma (long arrow) in the body of the pancreas and a duct dilatation (short arrow) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3636472/bin/qims-03-02-113- f10.jpg

Figure 7.18: Produced image (v1_01158.pgm) with a ductal adenocarcinoma

Figure 7.19: Pancreatic solid pseudopapillary tumors (arrows in A, B, and C) in the tail of the pancreas http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3636472/bin/qims-03-02-113- f18.jpg

Figure 7.20: Produced image (v1_01121.pgm) with a solid pseudo papillary tumors

Figure 7.21: A CT scan of an acinar cell carcinoma in the head of the pancreas http://www.pubcan.org/images/large/Fig_12-56_A.jpg

Figure 7.22: Produced image (v1_01172.pgm) with an acinar cell carcinoma in the head of the pancreas

7.3.2 Relevance in endocrine tumors Finally, a real medical image from patient with pancreatic cancer is shown in Figure 7.23, while in Figure 7.24 a cropped spheroid tumor with (r,c)=(8,8) which is inputted in position (x,y,z)=(488,180,181) is shown. It is clear from Figure 7.23 and Figure 7.24 that the produced image is relevant to the real medical image.

Figure 7.23: A CT scan illustrates an insulinoma (yellow arrow) in the head of the pancreas http://endocrinediseases.org/neuroendocrine/img/pic_insulinoma_ctscan2.jpg

Figure 7.24: Produced image (v1_01184.pgm) with insulinoma

7.4 Efficiency

In order to check the performance and the execution time of the program, different scenarios are evaluated where some inputs are fixed and others are modified. The execution time is estimated using the tic and toc MATLAB commands for the Run and Save functions, where the Run function refers to the function that is called when the Run button is pressed and the Save function is the one that is called when the Save button is used. Each scenario is executed 100 times and the average time is used as the result in order to minimize the deviation over each execution. It is widely common that the performance of a program depends on the computer’s features, such us central processing unit (CPU) speed, random access memory (RAM) and Revolutions Per Minute (RPM) of a hard disk, etc. In this case, for the following results, the operating system of the machine that is used is Scientific Linux release 7.1 (Nitrogen) with total system memory 7.95 GB. The machine has 4 Intel Core i5-3470 processors running at 3.2GHz. Each processor has 4 cores and 1 thread and a 6.14M cache memory.

In Sections 7.4.1-7.4.3 the efficiency of spheroid, ellipsoid and paraboloid tumors in gastric cancer representation is presented. The results of spheroid and ellipsoid tumors in pancreatic cancer depiction can be found in Appendix B. We preferred to omit them from the main part of the dissertation, because the results are almost identical to the spheroid and ellipsoid results that are produced for gastric cancer representation. Therefore, the selection of the organ does not influence the results.

7.4.1 Efficiency of spheroid tumor From Figure 7.25 to Figure 7.28 two cases are tested. Especially, in the first case a spheroid tumor with zero degree of rotation is introduced in position (x,y,z)=(618,270,66), while in the second case two spheroid tumors with zero degree of rotation are added in position (x,y,z)=(618,270,66) and (x,y,z)=(658,230,66) respectively. However, in Figure 7.25 and Figure 7.27, radius is 10mm for all cases and both tumors, while the numbers of layers varies between 10 and 80 layers. On the other hand, in Figure 7.26 and Figure 7.28, radius takes values between 10mm and 80 mm, while the numbers of layers is constant and equal to 30 layers for both scenarios.

Figure 7.25: Average execution time (sec) of Run function for different layers

Figure 7.26 : Average execution time (sec) of Run function for different values of radius (mm)

It is clear from Figure 7.25 that the average execution time for the Run function is independent of the number of layers and almost steady for all cases, because as the number of layers (c-axis) is increased, the size of the tumor only in z-direction is raised which does not affect significantly the execution time. It is also obvious from Figure 7.26 that the execution time is not affected severely from the size of the tumor for the Run function, unless it extends a lot. More specific, in Figure 7.26 for radius from 10mm to 50mm the average execution time is almost the same (approximately 1.5 seconds for 1 tumor and 2-2.33 seconds for 2 tumors), but for radius equals 60mm it increases to 1.8048 seconds (1 tumor) and to 2.4679 second (2 tumors) and it continues to raise to 2.3465 seconds (1 tumor) and to 3.0231 seconds (2 tumors) for radius equals to 80 mm. The main explanation behind this increase is that when the radius is increased, the size of the tumor in the two dimensions (x-axis, y-axis) is raised. Therefore, when the raise is huge the execution time is increased significantly. Moreover, it is easily noticeable that in the case of 2 tumors, the execution time for Run function (Figure 7.25, Figure 7.26) is higher than in 1 tumor, because the algorithm inserts tumors sequentially.

Figure 7.27: Average execution time (sec) of Save function for different layers

Figure 7.28: Average execution time (sec) of Save function for different values of radius (mm)

As regards on the Save function in case where the radius is constant (10mm) and the number of layers is increased (Figure 7.27), the execution time raises almost linear. That boost happens, because every time that the number of layers is raised, function [73] is called and a new image is created. Nevertheless, as it has been already

90 mentioned, function [73] is the most time-consuming function that our program uses. Therefore, when the number of layers is increased, the execution time is also raised. Finally, it can be seen in Figure 7.28 that when the number of layers is constant (30 layers) and the radius is increased the execution time for the Save button is almost the same for 1 tumor and 2 tumors. Indeed, it is almost the same as in case 3 (30mm) in Figure 7.27 in which 30 layers are used. As a consequence, it is clear that the Save function depends on the number of layers and it is almost independent of the size of the tumor. It is also clear that the execution time in Save function it is almost the same for both scenarios (1 tumor, 2 tumors), because in the second case both tumors are added in the same layer (same centre z) with the same value in axis c. Therefore the number of the images that are created is exactly the same in case of 1 and 2 tumors.

Finally, it is remarkable that the execution time using Run function is much smaller than calling Save function. The main explanation behind this difference is that in Run function data are read from the RAM memory and the time that is needed is eliminated. On the other hand, in Save function data are written, so we access the hard disk and the required time for writing is more than in reading the data.

7.4.2 Efficiency of ellipsoid tumor In Figure 7.29-Figure 7.32 two scenarios are again considered. In the case of 1 tumor, an ellipsoid tumor with zero degree of rotation is added in position (x,y,z)=(618,270,66), while in the scenario of 2 tumors, two ellipsoid tumors with zero degree of rotation are introduced in position (x,y,z)=(618,270,66) and (x,y,z)=(658,230,66) respectively. However, in Figure 7.29 and in Figure 7.31, a and b are 20 mm and 10mm respectively for all cases, while the numbers of layers varies between 10 and 80 layers. On the other hand, in Figure 7.30 and Figure 7.32, a and b take different values according to Table 7.1, while the numbers of layers is constant and equal to 30 layers.

Figure 7.29: Average execution time (sec) of Run function for different layers

Figure 7.30: Average execution time (sec) of Run function for different values of a,b (mm)

Size 1 tumor 2 tumors Average Average Average Average a b Execution Execution Execution Execution (mm) (mm) Time for Run Time for Save Time for Run Time for Save Function (sec) Function (sec) Function (sec) Function (sec) 10 10 1.3635 275.2771 1.9472 277.0242 20 10 1.4171 278.7123 2.0015 274.5911 30 10 1.3217 278.0358 2.0043 276.1984 40 10 1.3662 278.5400 2.0111 276.9554 10 50 1.3576 273.5607 2.0581 281.3100 10 60 1.3126 278.5947 2.0758 283.0197 10 70 1.3476 277.3277 2.1501 281.8546 10 80 1.3339 283.0315 2.1363 281.5816 Table 7.1: Values of a, b and average execution time in gastric ellipsoid tumor

It is clear from Figure 7.29 that the average execution time for the Run function is independent of the number of layers, almost steady and less than 1.4 seconds in the case of 1 tumor, while in the scenario of 2 tumors, it is near to 2 seconds until 50 layers and then it goes up to 2.5531 seconds for 80 layers. It is also obvious from Figure 7.30 that the execution time is not affected from the size of the tumor for the Run function, as it is almost constant and less than 1.42 seconds for all the combinations of a and b that are illustrated in Table 7.1 for 1 tumor and near 2 seconds for all cases for 2 tumors. In this example the size of the tumor in one axis (a which refers to x-dimension or b which refers to y-dimension) is increased, while the other two (a and c or b and c) are constant. So, as the increase is not significant the execution time has not been affected. Furthermore, as in spheroid tumor, in the case of 2 tumors the execution time for Run function (Figure 7.29, Figure 7.30) for ellipsoid tumor is higher than in 1 tumor, because the algorithm inserts the tumors sequentially.

Figure 7.31: Average execution time (sec) of Save function for different layers

Figure 7.32: Average execution time (sec) of Save function for different values of a,b (mm)

With respect to the Save function in case where a and b are constant (20mm,10mm respectively) and the number of layers is increased (Figure 7.31), the execution time raises almost linear, as in spheroid tumor (Figure 7.27). As a consequence, when the number of layers is increased, the execution time is also raised. Finally, it can be seen

94 in Figure 7.32 that when the number of layers is constant (30 layers) and a and b are raised the execution time for the Save button is almost the same for all cases and near to 278 seconds. Thereby, it is clear that the Save function depends on the number of layers and it is almost independent of the size of the tumor. We can also see that as in the case of spheroid tumor, the execution time in Save function for ellipsoid tumor is almost the same for both scenarios (1 tumor, 2 tumors), because in the second case both tumors are added in the same layer (same centre z) with the same c axis value. So, the number of the images that are created in case of 1 and 2 tumors is the same.

7.4.3 Efficiency of paraboloid tumor In Figure 7.33-Figure 7.36 we estimated two cases. In the first scenario, a paraboloid tumor with 30 degrees of rotation is introduced in position (x,y,z)=(618,270,66), while in the case of 2 tumors, two paraboloid tumors with 30 degree of rotation are inputted in position (x,y,z)=(618,270,66) and (x,y,z)=(658,230,66) respectively. However, in Figure 7.33 and Figure 7.35, thickness and aperture are 10 mm and 2 mm respectively for all cases, while the numbers of layers varies between 10 and 80 layers. On the other hand, in Figure 7.34 and Figure 7.36, thickness and aperture take different values according to Table 7.2, while the numbers of layers is constant and equal to 30 layers.

Figure 7.33: Average execution time (sec) of Run function for different layers

Figure 7.34: Average execution time (sec) of Run function for different values of thickness, aperture (mm)

Size 1 tumor 2 tumors Average Average Average Average Thickness Aperture Execution Execution Execution Execution (mm) (mm) Time for Time for Time for Time for Run Save Run Save Function Function Function Function (sec) (sec) (sec) (sec) 6 1 1.4635 275.9639 2.4589 279.8429 8 1 1.4245 279.2189 2.4031 281.4645 10 2 1.3324 275.7469 2.2559 282.4204 12 2 1.3437 277.8051 2.2534 282.3090 14 4 1.3416 277.7930 2.1930 281.6968 16 4 1.3374 276.7486 2.1893 282.2449 18 6 1.3190 278.2449 2.1828 281.6402 20 8 1.3147 278.6019 2.1623 280.4773 Table 7.2: Values of thickness, aperture and average execution time in gastric paraboloid tumor

It is obvious from Figure 7.33 that the average execution time for the Run function increases slightly as the number of layers raises and varies between 1.269 seconds and 1.5164 seconds for 1 tumor, while in case of 2 tumors it varies between 2.0188

96 seconds and 2.7125 seconds. It can also been seen from Figure 7.34 that the execution time is not affected severely from the size of the tumor for the Run function and takes values from 1.3147 seconds for (thickness, aperture)=(20,8) to 1.4635 seconds for (thickness, aperture)=(6,1) in case of 1 tumor, while in case of 2 tumors the execution time is between 2.1623 seconds for (thickness, aperture)=(20,8) and 2.4589 seconds for (thickness, aperture)=(6,1). It has been already mentioned that the increase of the thickness value reduces the size of the tumor. That explains why the highest execution time for Run function is presented for (thickness,aperture)=(6,1) for both scenarios. Furthermore, as in spheroid and ellipsoid tumor, in the case of 2 tumors the execution time for Run function (Figure 7.33, Figure 7.34) for paraboloid tumor is higher than in 1 tumor, as Run function adds tumors sequentially.

Figure 7.35: Average execution time (sec) of Save function for different layers

Figure 7.36: Average execution time (sec) of Save function for different values of thickness, aperture (mm)

It is clear that in the Save function in case where thickness and aperture are steady (10mm,2mm respectively) and the number of layers is increased (Figure 7.35), the execution time raises almost linear, as in spheroid and ellipsoid tumor (Figure 7.27, Figure 7.31). Therefore, when the number of layers is increased, the execution time is also raised. Finally, it is obvious in Figure 7.36 that when the number of layers is constant (30 layers) and thickness and aperture are increased the execution time for the Save button is almost steady and near to 277 seconds (1 tumor) and 281 seconds (2 tumors). Thereby, it is clear that the Save function depends on the number of layers and it is almost independent of the size of the tumor. We can also see that as in the case of spheroid and ellipsoid tumor, the execution time in Save function for paraboloid tumor is almost the same for both scenarios (1 tumor, 2 tumors), because both tumors are added in the same layer (same centre z) with the same value in c axis.

From all the above cases, it can be claimed that the execution time for Run and Save functions present small variations for spheroid, ellipsoid and paraboloid tumor. As a consequence, we can state that the execution time does not depend notably on the shape of the tumor.

7.5 Effectiveness

All the objectives of this project are successful and we are able to produce tumors with different size and shapes that can be used to represent several types of stomach and pancreatic cancer. Indeed, the results seem to be very similar with the real medical images. Therefore, the major aims are achieved and the effectiveness of this work is considered high.

7.6 Impact

After the end of this project, DHP images with gastric and pancreatic cancer are available to be used from the research group. In particular, they are able to examine several cases of gastric and pancreatic cancer and they can test on them the detection algorithms that they develop using microwave imaging.

Chapter 8

Conclusions and Future Plans

Chapter 8 demonstrates a summary of the project and provides some advices for future work. In particular, Section 8.1 includes a recap of this project, while Section 8.2 presents some plans and actions that can be followed as a future work.

8.1 Conclusions

It is widely known that cancer is one of the greatest killers in the world and it still remains an unsolved substantial health problem. Indeed, as time passes more incidences are presented and cancer is becoming increasingly threatening for humans life.

During this project, two different kinds of cancer were studied. In particular, the main types and characteristics of stomach and pancreatic cancer were analysed, as well as the available detection mechanisms to detect them and the classification methods that are used to categorize them were presented. The format of DHP images was also studied and we learn how we can create and modify them. Moreover, mathematical models to describe stomach and pancreatic tumors were developed and implemented and afterwards an algorithm for the insertion of tumors inside the DHP images was also implemented. Furthermore, a graphical user interface was created, so that the user is able to introduce the type of cancer, the type of tumor, its features and its position. The user is also able to insert as many tumors as s/he prefers, s/he can also delete one or more of them and s/he can save the produced images with cancer. Finally, the program was evaluated in order to test how relevant the results were with real medical images and we also calculated and analysed the efficiency, effectiveness and impact of this work.

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Unfortunately, conventional cancer treatments redound mildly on disease course, because cancer is usually detected in an advance level after the spread of the tumor, when the available methods of treatment are limited. Therefore, it is crucial to develop new detection mechanisms that can discern cancer at early stages, when the possibilities of treatment are high. Our research group is currently working on the development of new detection mechanisms as the existent ones seems to fail at early stages detection.

After the end of this project, it can be claimed that our contribution is essential, as the research group is now able to examine various cases of stomach and pancreatic cancer into DHP images and it is able to test their detection algorithms in the DHP images with gastric or pancreatic cancer that our project produces.

8.2 Future Plans

The project succeeds in gastric and pancreatic cancer representation and the research group is now able to use different types of tumors that were developed and implemented. Nevertheless, due to the limited time of the project, only two types of cancer were studied. Therefore, as a future plan, more types of cancer can be studied and more types of tumors with different shapes can be developed. Furthermore, although the required time to execute the program is low, it will be essential to improve and decline the time needed for creating and writing the images, as [73] is a time-consuming function. Finally, it will be crucial to test the program in parallel processing with several cores and calculate the execution time in order to see if it improves or not.

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Appendix A

Shell Script to identify the location of stomach and pancreas into the DHP images

Appendix A clarifies the bash code for the identification of the stomach and pancreas into the digital human phantom images.

#!/bin/bash ######################################################### ### Stomach and Pancreas ### ### Identification ### #########################################################

# We define the directories where we save the images filepathStomach="/home/dimitra/Downloads/StomachImages/" filepathPancreas="/home/dimitra/Downloads/PancreasImages/" stomach_pancreas=ègrep -i '33|29' * | sort -n | uniq ` stomach=ècho "$stomach_pancreas" | grep -i ' 33' | cut -f1 -d ':'` pancreas=ècho "$stomach_pancreas" | grep -i ' 29' | cut -f1 -d ':'` if [ ! -d "$filepathStomach" ]; then mkdir $filepathStomach fi if [ ! -d "$filepathPancreas" ]; then mkdir $filepathPancreas fi if [ -n "$stomach" ]; then cp $stomach $filepathStomach fi if [ -n "$pancreas" ]; then cp $pancreas $filepathPancreas fi

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Appendix B

Efficiency of spheroid tumor in pancreatic cancer representation

In Figure B.1-Figure B.4 two scenarios are tested. In the first case, a spheroid tumor with zero degree of rotation is introduced in position (x,y,z)=(570,220,149), while in the second case two spheroid tumors are added in position (x,y,z)=(570,220,149) and (x,y,z)=(515,160,149) respectively. However, in Figure B.1 and Figure B.3, radius is 10mm for all cases, while the numbers of layers varies between 10 and 80 layers. On the other hand, in Figure B.2 and in Figure B.4, radius takes values between 10mm and 80 mm, while the numbers of layers is constant and equal to 30 layers.

Figure B.1: Average execution time (sec) of Run function for different layers

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Figure B.2: Average execution time (sec) of Run function for different values of radius (mm)

Figure B.3: Average execution time (sec) of Save function for different layers

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Figure B.4: Average execution time (sec) of Save function for different values of radius (mm)

Efficiency of ellipsoid tumor in pancreatic cancer representation

In Figure B.5-Figure B.8 two cases are calculated. Firstly, an ellipsoid tumor with zero degree of rotation is introduced in position (x,y,z)=(570,220,149) and secondly two ellipsoid tumors are added in position (x,y,z)=(570,220,149) and (x,y,z)=(515,160,149) respectively.. However, in Figure B.5 and Figure B.7, a and b are 20 mm and 10mm respectively for all cases, while the numbers of layers varies between 10 and 80 layers. On the other hand, in Figure B.6 and in Figure B.8, a and b take different values according to Table B.1, while the numbers of layers is constant and equal to 30 layers.

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Figure B.5: Average execution time (sec) of Run function for different layers

Figure B.6: Average execution time (sec) of Run function for different values of a,b (mm)

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Size 1 tumor 2 tumors Average Average Average Average a(mm) b(mm) Execution Execution Execution Execution Time for Run Time for Save Time for Run Time for Save Function (sec) Function (sec) Function (sec) Function (sec) 10 10 1.3232 280.5610 2.0043 278.9303 20 10 1.3042 279.3149 1.9929 277.3037 30 10 1.3142 278.6526 1.9927 278.7058 40 10 1.2904 280.3041 2.0070 277.7541 10 50 1.3027 280.3429 2.0000 280.0757 10 60 1.3327 278.9653 2.0302 280.6102 10 70 1.3246 279.7006 2.0691 278.9222 10 80 1.3075 276.5264 2.0652 279.7510 Table B.1: Values of a, b and average execution time in pancreatic ellipsoid tumor

Figure B.7: Average execution time (sec) of Save function for different layers

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Figure B.8: Average execution time (sec) of Save function for different values of a,b (mm)

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