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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA163313-04
Application #
8715727
Study Section
Special Emphasis Panel (ZCA1-SRLB-R)
Project Start
Project End
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
4
Fiscal Year
2014
Total Cost
$222,588
Indirect Cost
$99,328
Name
University of Pennsylvania
Department
Type
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Onega, Tracy; Beaber, Elisabeth F; Sprague, Brian L et al. (2014) Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level. Cancer 120:2955-64
Keller, Brad M; Chen, Jinbo; Conant, Emily F et al. (2014) Breast density and parenchymal texture measures as potential risk factors for Estrogen-Receptor positive breast cancer. Proc SPIE Int Soc Opt Eng 9035:90351D
McCarthy, Anne Marie; Kontos, Despina; Synnestvedt, Marie et al. (2014) Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program. J Natl Cancer Inst 106:
McCarthy, Anne Marie; Armstrong, Katrina (2014) The role of testing for BRCA1 and BRCA2 mutations in cancer prevention. JAMA Intern Med 174:1023-4
Conant, Emily F (2014) Clinical implementation of digital breast tomosynthesis. Radiol Clin North Am 52:499-518
Keller, Brad M; Nathan, Diane L; Gavenonis, Sara C et al. (2013) Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures. Acad Radiol 20:560-8
Daye, Dania; Keller, Brad; Conant, Emily F et al. (2013) Mammographic parenchymal patterns as an imaging marker of endogenous hormonal exposure: a preliminary study in a high-risk population. Acad Radiol 20:635-46