The goal of this research is to develop novel machine learning techniques to integrate physician expertise and machine learned, logical rules in a graphical model that will accurately estimate breast cancer risk after breast biopsy. Our multidisciplinary team has a track record (including NIH funding and publications in the medical and computer science literature) illustrating an innovative research program that merges cutting edge machine learning algorithms including inductive logic programming and statistical relational learning to train graphical models to predict breast cancer risk. However, in contrast to prior work, we are testing a completely new methodology which we call Advice-Based-Learning (ABLe). By developing ABLe, our team aims to establish an innovative, collaborative cycle between machine-learning and physician expertise. We propose to test the hypothesis that this cycle will increase accuracy beyond what either the machine or human can accomplish alone. Specifically, we hypothesize first that a conventionally-trained graphical model trained with conventional machine learning first algorithms can accurately predict the risk of breast cancer after core biopsy and perform better than current clinical practice;a critical aim that is favorably foreshadowed by our new preliminary data but is labor intensive because we must perfect our unique clinical data that accurately represents clinical experience. Second, a graphical model trained using ABLe can incorporate multi-relational data with physician expertise and significantly improve the predictive accuracy over conventionally trained graphical models and current clinical practice. Third, our best graphical model trained with ABLe will accurately estimate the probability of malignancy after breast biopsy on new clinical cases better than physicians alone resulting in a tool that has the potential to improve care. Our clinical application is as compelling as our algorithmic work. Image-guided core needle biopsy of the breast is a common procedure that is imperfect, has high-stakes, and is particularly amenable to improvement with automated decision support. Breast core biopsy, the standard of care for breast cancer diagnosis, can be """"""""non-definitive"""""""" in 5-15% of women undergoing this procedure. This means that between 35,000-105,000 women will require additional biopsies or radiologic follow-up to cement a diagnosis and risk the possibility of missed breast cancers, delays in diagnosis, and unnecessary surgeries. This important problem is emblematic of a plethora of clinical situations where rigorous and accurate risk estimation of rare events provides the opportunity for automated decisions support tools to personalize and strategically target health care interventions to improve decision-making for health-care providers and patients. This award will enable us not only to produce graphical models that provide improved decision support in the breast cancer clinic, but also, and more significantly, to develop a methodology that integrates heterogeneous predictive data and physician knowledge within a graphical model, thereby developing and validating a new algorithmic paradigm for creating accurate, comprehensible, adaptable decision support tools well-suited for clinical translation.

Public Health Relevance

Our multidisciplinary group of breast cancer physicians and computer scientists propose to develop a new paradigm for construction of clinical decision support tools that will integrate machine learning (computers learning from data) and physician expertise in order to perform better than either alone. Our system will be able to accurately estimate the true risk of malignancy after breast core biopsy addressing the challenges of delays in diagnosis and unnecessary surgeries encountered on the road to early breast cancer diagnosis.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM010921-02
Application #
8319628
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2011-08-15
Project End
2015-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
2
Fiscal Year
2012
Total Cost
$317,346
Indirect Cost
$104,196
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
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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
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:
Bozkurt, Selen; Gimenez, Francisco; Burnside, Elizabeth S et al. (2016) Using automatically extracted information from mammography reports for decision-support. J Biomed Inform 62:224-31
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
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
Ferreira, Pedro; Fonseca, Nuno A; Dutra, Inês et al. (2015) Predicting malignancy from mammography findings and image-guided core biopsies. Int J Data Min Bioinform 11:257-76
Kuusisto, Finn; Dutra, Inês; Elezaby, Mai et al. (2015) Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems. AMIA Jt Summits Transl Sci Proc 2015:87-91
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
Tunc, Sait; Alagoz, Oguzhan; Burnside, Elizabeth (2014) Opportunities for Operations Research in Medical Decision Making. IEEE Intell Syst 29:59-62

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