This proposal requests K07 support for Dr. Elizabeth Burnside to become an independent investigator and leader in the use of computer models to improve the early detection of breast cancer. This award will allow Dr. Burnside to optimize and validate a computer model called a Bayesian network designed to provide decision-support for radiologists interpreting mammography. This project is a multidisciplinary effort integrating computer science, applied medical informatics, population health science and breast imaging. The University of Wisconsin is an ideal environment for this work because it provides access to renowned researchers in these areas and individuals experienced with the inclusion of diverse populations in women's health research. To achieve these research and career goals, the candidate will follow a career development plan that consists of 1) acquisition of advanced research competencies in artificial intelligence, machine learning algorithms, probability theory, Bayesian reasoning, research ethics, and clinical trial design;2) mentorship from nationally and internationally recognized experts;and 3) innovative research in computer-aided decision support in breast cancer diagnosis. The current state of breast cancer screening reveals a wide variability of practice and a shortage of expert mammographers. Dr. Burnside's model envisions using expert knowledge to improve non- expert performance in mammography. The model complements current computer-assisted detection algorithms by contributing information to crucial post-discovery aspects of mammography: interpretation and decision-making. Using probability theory, the model defines relationships between a patient's demographic risk factors, her mammographic findings, and diseases of the breast to generate a breast cancer risk assessment uniquely tailored to her. If successful, the model will improve the accuracy of decision-making, decrease the human costs of underperformance and variability, and deliver personalized medicine to patients who undergo mammography.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Academic/Teacher Award (ATA) (K07)
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Study Section
Subcommittee G - Education (NCI)
Program Officer
Perkins, Susan N
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University of Wisconsin Madison
Schools of Medicine
United States
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Ayvaci, Mehmet U S; Alagoz, Oguzhan; Chhatwal, Jagpreet et al. (2014) Predicting invasive breast cancer versus DCIS in different age groups. BMC Cancer 14:584
Wu, Yirong; Rubin, Daniel L; Woods, Ryan W et al. (2014) Developing a comprehensive database management system for organization and evaluation of mammography datasets. Cancer Inform 13:53-62
Wu, Yirong; Alagoz, Oguzhan; Ayvaci, Mehmet U S et al. (2013) A comprehensive methodology for determining the most informative mammographic features. J Digit Imaging 26:941-7
Alagoz, Oguzhan; Chhatwal, Jagpreet; Burnside, Elizabeth S (2013) Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis. Decis Anal 10:200-224
Ayvaci, Mehmet U S; Alagoz, Oguzhan; Burnside, Elizabeth S (2012) The Effect of Budgetary Restrictions on Breast Cancer Diagnostic Decisions. Manuf Serv Oper Manag 14:600-617
Burnside, Elizabeth S; Chhatwal, Jagpreet; Alagoz, Oguzhan (2012) What is the optimal threshold at which to recommend breast biopsy? PLoS One 7:e48820
Percha, Bethany; Nassif, Houssam; Lipson, Jafi et al. (2012) Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 19:913-6
Woods, Ryan W; Sisney, Gale S; Salkowski, Lonie R et al. (2011) The mammographic density of a mass is a significant predictor of breast cancer. Radiology 258:417-25
Ferreira, Pedro; Fonseca, Nuno A; Dutra, InĂªs et al. (2011) Predicting Malignancy from Mammography Findings and Surgical Biopsies. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2011:
Woods, Ryan W; Oliphant, Louis; Shinki, Kazuhiko et al. (2010) Validation of results from knowledge discovery: mass density as a predictor of breast cancer. J Digit Imaging 23:554-61

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