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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
4R01CA165229-05
Application #
9057470
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Redmond, George O
Project Start
2012-07-09
Project End
2017-04-30
Budget Start
2016-05-01
Budget End
2017-04-30
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Schrager, Sarina; Burnside, Elizabeth (2018) Breast Cancer Screening in Primary Care: A Call for Development and Validation of Patient-Oriented Shared Decision-Making Tools. J Womens Health (Larchmt) :
van Ravesteyn, Nicolien T; van den Broek, Jeroen J; Li, Xiaoxue et al. (2018) Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches. Med Decis Making 38:126S-139S
Feld, Shara I; Fan, Jun; Yuan, Ming et al. (2018) Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. AMIA Jt Summits Transl Sci Proc 2017:81-90
van den Broek, Jeroen J; van Ravesteyn, Nicolien T; Mandelblatt, Jeanne S et al. (2018) Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49. Med Decis Making 38:140S-150S
Trentham-Dietz, Amy; Ergun, Mehmet Ali; Alagoz, Oguzhan et al. (2018) Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 168:229-239
Wu, Yirong; Fan, Jun; Peissig, Peggy et al. (2018) Quantifying predictive capability of electronic health records for the most harmful breast cancer. Proc SPIE Int Soc Opt Eng 10577:
Burnside, Elizabeth S; Vulkan, Daniel; Blanks, Roger G et al. (2018) Association between Screening Mammography Recall Rate and Interval Cancers in the UK Breast Cancer Service Screening Program: A Cohort Study. Radiology 288:47-54
Lee, Cindy S; Sickles, Edward A; Burnside, Elizabeth S (2018) Data-Driven Mammography Screening Practices-Reply. JAMA Oncol 4:588-589
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
Burnside, Elizabeth S; Lee, Sandra J; Bennette, Carrie et al. (2017) Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age. MDM Policy Pract 2:

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