The goal of this research is to develop, integrate, and evaluate novel informatics techniques to optimize the mammographic diagnosis of breast cancer in aging women. Women over age 65 who develop breast cancers have greater death rates and poorer outcomes. These women also bear the heaviest burden of """"""""overdiagnosis"""""""" a phenomenon in which screening identifies cancer which may not go on to cause symptoms or death. These sobering statistics are made more urgent by the realization that the number of women >65 is projected to more than double (from 20-40 million) between 2000 and 2050. In this research program, we propose to develop tools that will improve the early diagnosis of invasive breast cancer, minimize unnecessary invasive procedures (decrease false positives) and concomitantly address overdiagnosis. Specifically we aim to 1) develop a probabilistic computer model trained by a novel machine learning algorithm, Prediction using Logical Uplift Modeling (PLUM), that tailors breast cancer risk estimations to aging women;2) use a decision analytic model to determine the optimal breast biopsy threshold using an important pathologic prognostic indicator, cytologic grade in the context of age, and 3) use comparative effectiveness analysis to determine how these personalized risk prediction strategies and optimal thresholds for action will improve on current breast cancer screening policies in women >65. Our multidisciplinary team has a track record (including NIH funding and publications in the medical, engineering, and computer science literature) of innovative research that integrates state-of-the-art informatics algorithms to improve breast cancer diagnosis. Using prior experience and infrastructure, we are building a completely new machine learning algorithm, PLUM, which uses 1) inductive logic programming (ILP) to accurately learn from multi-relational data;2) uplift modeling that uses age as a partition, and 3) rule incorporation into our probabilistic model for accurate risk prediction. We necessarily use a uniquely rich clinical data, a deep understanding of disease processes, and creative integration of these computational tools. New preliminary data presented in this resubmission foreshadow scientific success and clinical translation. If supported, this project will prove that risk prediction and decision analytic tools can accurately assess breast cancer risk, determine an optimal, personalized biopsy threshold, and provide a superior breast cancer screening policy than is currently employed in the US for women over age 65.

Public Health Relevance

Breast cancer screening recommendations are ambiguous for women over the age of 65 and poorer breast cancer outcomes are seen in this population. Accurate and personalized software tools that predict breast cancer risk and prognosis are needed to avoid late diagnosis, morbidity and mortality, as well as minimize false positives and overdiagnosis (cancer which may not go on to cause symptoms or death). Our multidisciplinary team of breast cancer physicians/experts, engineers, and computer scientists propose to build models that can predict breast cancer risk, determine the optimal management of mammography findings based on this risk, and the population impact of these tools for women over 65 in the US.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Redmond, George O
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University of Wisconsin Madison
Schools of Medicine
United States
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DuBenske, Lori L; Schrager, Sarina; McDowell, Helene et al. (2017) Mammography Screening: Gaps in Patient's and Physician's Needs for Shared Decision-Making. Breast J 23:210-214
Wu, Yirong; Abbey, Craig K; Liu, Jie et al. (2016) Discriminatory power of common genetic variants in personalized breast cancer diagnosis. Proc SPIE Int Soc Opt Eng 9787:
Abbey, Craig K; Wu, Yirong; Burnside, Elizabeth S et al. (2016) A Utility/Cost Analysis of Breast Cancer Risk Prediction Algorithms. Proc SPIE Int Soc Opt Eng 9787:
Benndorf, Matthias; Wu, Yirong; Burnside, Elizabeth S (2016) A history of breast cancer and older age allow risk stratification of mammographic BI-RADS 3 ratings in the diagnostic setting. Clin Imaging 40:200-4
Burnside, Elizabeth S; Drukker, Karen; Li, Hui et al. (2016) Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer 122:748-57
Benndorf, Matthias; Burnside, Elizabeth S; Herda, Christoph et al. (2015) External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets. Med Phys 42:4987-96
Liu, Jie; Wu, Yirong; Ong, Irene et al. (2015) Leveraging Interaction between Genetic Variants and Mammographic Findings for Personalized Breast Cancer Diagnosis. AMIA Jt Summits Transl Sci Proc 2015:107-11
Benndorf, Matthias; Kotter, Elmar; Langer, Mathias et al. (2015) Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon. Eur Radiol 25:1768-75
Kuusisto, Finn; Dutra, InĂªs; Elezaby, Mai et al. (2015) Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems. AMIA Jt Summits Transl Sci Proc 2015:87-91
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

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