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15 March 2019 Analysis of mammographic density as a predictor for breast cancer occurrence
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Abstract
We are investigating the association of mammographic density with breast cancer occurrence. With IRB approval, we collected cases of women with screening-detected breast cancer and controls. A total of 2028 patients including 329 cases was collected from the screening cohort in our institution. An experienced MQSA radiologist retrospectively reviewed the earliest available digital mammograms (DMs) and assessed breast density in terms of BI-RADS categories and percent density (PD) estimated by interactive thresholding. Survival models were built based on BI-RADS categories and strata based on PD measures, respectively. Using the pairwise log-rank test, we observed a statistically significant difference at the 5% level between BI-RADS category A and C, category A and D, category B and C, & category B and D. Similarly, we found a significant difference between curves for women with <10% density and with 20-34% density, between women with <10% density and with ≥35% density, and between women with 10-19% density and with ≥35% density. A multivariate Cox proportional hazards model was constructed using backwards variable selection with age, BI-RADS density, PD strata, and PD as independent factors. At the 5% level, the results indicated that age and PD had statistically significant influences on occurrence time. With age serving as a borderline protective factor (regression coefficient < 0, hazard ratio HR=0.99, p=.0506), PD was a risk factor (regression coefficient < 0, hazard ratio HR=1.02, p=.0001) for breast cancer occurrence. Our results showed that breast density plays an important role in the risk and occurrence for breast cancer.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Annika Zdon, Mark A. Helvie, Alex Tsodikov, Heang-Ping Chan, and Jun Wei "Analysis of mammographic density as a predictor for breast cancer occurrence", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095412 (15 March 2019); https://doi.org/10.1117/12.2511649
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