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Proposal Form Date Submitted: Proposal Type: If renewal, current grant: Grant Application Resubmission? Prior App: RFA? RFA Title: TITLE OF PROJECT (Titles exceeding 81 characters, including spaces and punctuation, will be truncated.) APPLICANT NAME HIGHEST DEGREE(S) POSITION TITLE: APPLICANT’S CURRENT INSTITUTION ACADEMIC RANK: DIVISION: MAILING ADDRESS (Street, city, state, postal code, country) DEPARTMENT: E-MAIL ADDRESS: Tel: Fax: PROGRAM ELIGIBILITY INFORMATION: (Responses to selected fields displayed below. For some grant programs this section may be blank.) DATES OF PROPOSED PROJECT (MM/DD/YYYY) PROPOSED BUDGET ( ( From Through SIGNING OFFICIAL FOR Name Name Address Title Address Tel: Fax: Tel: Fax: EIN E-MAIL ADDRESS DUNS HUMAN SUBJECTS No Yes VERTEBRATE ANIMALS No Yes Human Subjects Assurance No. IRB Status: Animal welfare assurance no. IACUC Status: IRB Date: IACUC Date: APPLICANT ASSURANCE: I certify that the statements herein are true, complete and SIGNATURE OF APPLICANT DATE accurate to the best of my knowledge. I am aware that any false, fictitious, or fraudulent (In ink. “Per” signature not acceptable.) statements or claims may subject me to criminal, civil, or administrative penalties. I agree to accept responsibility for the scientific conduct of the project and to provide the required progress reports if a grant is awarded as a result of this application. SIGNING OFFICIAL ASSURANCE: I certify that the statements herein are true, complete SIGNATURE OF SIGNING OFFICIAL DATE and accurate to the best of my knowledge, and accept the obligation to comply with the (In ink. “Per” signature not acceptable.) grantor’s terms and conditions if a grant is awarded as a result of this application. I am aware that any false, fictitious, or fraudulent statements or claims may subject me to criminal, civil, or administrative penalties. ADDITIONAL SIGNATURE (follow guidelines for required signatures): DATE ADDITIONAL SIGNATURE (follow guidelines for required DATE I certify that the statements herein are true, complete and accurate to signatures): I certify that the statements herein are true, the best of my knowledge. complete and accurate to the best of my knowledge. Applicant: Application Contacts Role Role Name Name Institution Institution Title Title Division Division Dept Dept Address Address Tel: Fax: Tel: Fax: E-mail E-mail Role Role Name Name Institution Institution Title Title Division Division Dept Dept Address Address Tel: Fax: Tel: Fax: E-mail E-mail Role Role Name Name Institution Institution Title Title Division Division Dept Dept Address Address Tel: Fax: Tel: Fax: E-mail E-mail Role Role Name Name Institution Institution Title Title Division Division Dept Dept Address Address Tel: Fax: Tel: Fax: E-mail E-mail GENERAL AUDIENCE SUMMARY APPLICANT NAME DATE SUBMITTED TITLE OF PROJECT (Titles exceeding 81 characters, including spaces and punctuation, will be truncated.) This General Audience Summary will become public information; therefore, do not include proprietary/confidential information. SCIENTIFIC ABSTRACT APPLICANT NAME DATE SUBMITTED TITLE OF PROJECT (Titles exceeding 81 characters, including spaces and punctuation, will be truncated.) This Scientific Abstract will become public information; therefore, do not include proprietary/confidential information. A. Title of Research Project Multiscale Shape/Intensity Distributions and Machine Learning for Automated Breast Cancer Histologic Grade Estimation B. General Audience Summary (Lay Abstract) Breast cancer is the most common malignant tumor in women, affecting approximately 1 in 9 women in the United States during their lifetime. Although substantial effort has been made both in the diagnosis and treatment of this disease, one third of breast cancer patients are ex- pected to die from it. Breast cancer is a progressive disease at all of its stages, and early detec- tion, accurate diagnosis and timely treatment can alter the natural course of the disease. Once a tumor is detected, a biopsy sample is taken, and studied by a pathologist (histology) to assess the stage of the disease. Correctly determining the development stage of the tumor will allow doc- tors to prescribe the most effective treatments. Breast cancers are classified according to their histologic grade, which to date is one of the most reliable ways to gauge overall survival. His- tologic grade ranges from I to III, with a grade I having a 5-year survival probability of 95% and a grade III having 5-year survival probability of 50%. Unfortunately different pathologists can give different grades to the same breast cancer sample. This occurs because there are no objec- tive methods for measuring the cellular structures and patterns found in breast cancer tumors. These discrepancies may lead to improper therapy for a particular patient. For example, some patients in the ``better'' prognosis category could actually have an aggressive form of the disease, indicating that chemo and hormone treatments are either unneeded, ineffective, or insufficient in various breast cancer patients. Therefore, unbiased methods for histologic grading are needed in order to remedy possible problems in the current grading procedure and improve prognosis. We propose to develop novel computational technologies that may be used to automatically and objectively estimate the histologic grade of breast cancer tumors. Our approach to auto- mated histologic grading is based on three powerful computing technologies: image processing, shape analysis and machine learning. Our approach involves acquiring digital images of cross- sections of breast cancer tumor specimens. The cancer cells in the image will be identified with image processing. The shape and distribution of the cancer cells will be mathematically modeled using shape analysis. The mathematical model of cancer cell shapes may then be analyzed, via machine learning, to determine what shapes and cell distributions are unique to Grades I, II and III breast carcinomas. Once we have mathematically described the shapes associated with these different grades, we can now identify the grade of an unknown sample by comparing the shapes and distribution of its cancer cells with the pre-classified identifiers. This computational ap- proach provides a standard method for histologic grading that will lead to a more reliable and consistent assessment of breast cancer and ultimately to improved cancer health care delivery. C. Scientific Abstract Background. Breast cancers can be categorized histologically based upon their architectural pat- terns and cellular types. Histologic grading has problems with inter- and intra-observer variabil- ity mainly due to a lack of an objective method for measuring architectural and cellular parame- ters of tumors. The variability is a product of both observer variation and sampling bias. Inaccu- rate histologic characterization can result in inappropriate treatment for a given patient. Compu- tational analysis of breast cancers offers an operator-independent method for histologic grading that should enhance grading reliability. Objective/Hypothesis. We propose to develop novel computational technologies that may be used to automatically and objectively estimate the histologic grade of breast cancer tumors. Our approach to automated histologic grading is based on image processing and shape analysis of imaged histologic sections at multiple physical scales. Our hypothesis is that the structure of the cellular pleomorphisms found in breast cancer tumors can be transformed into distinct high- dimensional shape distributions using geometric measures from stochastic geometry. The result- ing shape distributions will map into well-separated regions of the high-dimensional space de- fined by the distributions. We will augment this space with information derived from the inten- sity variations of the hyperchromatism found in the cancer cell nuclei. Mapping an unknown breast cancer sample into this high-D space and determining, via machine learning, to which re- gion it belongs will allow us to automatically estimate its histologic grade. Specific Aims. The specific aims of this project focus on investigating and evaluating the image processing, shape analysis and machine learning technologies needed to develop a reproducible standard for automated estimation of breast cancer histologic grade. Study Design. We will develop a computational pipeline consisting of 1) accurate and robust automated cancer cell segmentation techniques that use advanced pattern clustering and recogni- tion techniques, 2) a variety of geometric measures from stochastic geometry that transform can- cer cell shapes into shape distributions, 3) creating intensity distributions from the texture varia- tion / nuclear hyperchromatism levels within cancer cells, 4) mapping the shape/intensity distri- butions into a high-dimensional space for separation analysis, 5) machine learning technologies (e.g. Support Vector Machines) that identify which geometric measures and intensity features will provide the most reliable/significant grade classifier, and 6) validating tests that utilize specimens from a breast cancer databank. Potential Outcomes and Benefits. The project will provide an initial implementation of a compu- tational, objective, reproducible method for determining the histologic grade of breast cancer tu- mors. Consistent and effective grading of tumors should provide improved health care delivery by indicating proper treatments for breast cancer patients. D. Background Once a breast cancer
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