The goal of this project is to determine the benefit of incorporating novel imaging markers of breast tissue composition in guiding decisions for personalized breast cancer screening recommendations. Specifically, we propose to determine the predictive value of a new Breast Complexity Index (BCI), defined as a comprehensive descriptor of breast density and parenchymal texture, in ;) improving breast cancer risk estimation and //) assessing the risk of a false-positive or a false-negative screening exam. Studies have identified breast density as a strong independent risk factor for breast cancer. In addition, clinical trials have shown that mammographic sensitivity and specificity drops significantly in women with dense breasts, when compared to women with fatty breasts, suggesting that increased breast density is also independently associated with cancers missed by mammography (i.e., false negatives) and an increase in false positive recalls. The proposed research plan is designed as a phased imaging biomarker validation study with the Intention to determine the predictive value of BCI in improving individualized assessment in two important aspects of personalized risk: a woman's risk of developing breast cancer and her risk of having unnecessary call-back or mammographically occult disease. Our main hypothesis is that, by integrating quantitative breast density information with parenchymal texture features, BCI can provide a more comprehensive and objective descriptor of breast tissue composition than the commonly used BIRADS density and breast percent density (PD%) estimates and therefore result in more accurate predictions at the individual level. Improving risk assessment in both of these aspects can result in more informed decisions for guiding breast cancer screening recommendations, recognizing that personalized approaches to screening not only intensify surveillance to those most likely to benefit {i.e., high-risk women), but should also reduce intensity or provide alternative approaches to those less likely to benefit. Within this setting we propose compare mammography to breast tomosynthesis, an emerging tomographic breast imaging modality with the potential to significantly reduce false-positive call-backs in the screening setting, especially for women with very complex breast tissue. Our study holds the promise to shift the current paradigm in breast health care delivery by providing improved clinical decision-making tools for guiding personalized breast cancer screening recommendations.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZCA1-SRLB-R (O1))
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University of Pennsylvania
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Seitz, Holli H; Schapira, Marilyn M; Gibson, Laura A et al. (2018) Explaining the effects of a decision intervention on mammography intentions: The roles of worry, fear and perceived susceptibility to breast cancer. Psychol Health 33:682-700
Gastounioti, Aimilia; Oustimov, Andrew; Hsieh, Meng-Kang et al. (2018) Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk. Acad Radiol 25:977-984
McCarthy, Anne Marie; Barlow, William E; Conant, Emily F et al. (2018) Breast Cancer With a Poor Prognosis Diagnosed After Screening Mammography With Negative Results. JAMA Oncol 4:998-1001
Conant, Emily F; Sprague, Brian L; Kontos, Despina (2018) Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic. Radiology 286:401-404
McDonald, Elizabeth S; McCarthy, Anne Marie; Weinstein, Susan P et al. (2017) BI-RADS Category 3 Comparison: Probably Benign Category after Recall from Screening before and after Implementation of Digital Breast Tomosynthesis. Radiology 285:778-787
Conant, Emily F; Keller, Brad M; Pantalone, Lauren et al. (2017) Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures. Radiology 283:673-680
Wood, Marie E; Sprague, Brian L; Oustimov, Andrew et al. (2017) Aspirin use is associated with lower mammographic density in a large screening cohort. Breast Cancer Res Treat 162:419-425
Balasubramanian, Bijal A; Garcia, Michael P; Corley, Douglas A et al. (2017) Racial/ethnic differences in obesity and comorbidities between safety-net- and non safety-net integrated health systems. Medicine (Baltimore) 96:e6326
Weiss, Julie E; Goodrich, Martha; Harris, Kimberly A et al. (2017) Challenges With Identifying Indication for Examination in Breast Imaging as a Key Clinical Attribute in Practice, Research, and Policy. J Am Coll Radiol 14:198-207.e2
Haas, Jennifer S; Barlow, William E; Schapira, Marilyn M et al. (2017) Primary Care Providers' Beliefs and Recommendations and Use of Screening Mammography by their Patients. J Gen Intern Med 32:449-457

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