Abstract P1-02-06: Effect of chemoprevention on convolutional neural network-based breast cancer risk model using a mammographic dataset of women with atypical hyperplasia, lobular and ductal carcinoma in situ

Background: Mammographic density (MD) is a strong predictor of breast cancer risk and has been shown to predict response to tamoxifen for chemoprevention. We have developed a novel convolutional neural network (CNN)-derived pixel-wise breast cancer risk model, which is a more accurate predictor of breast cancer risk than MD. Our objective was to investigate how chemoprevention use among women with atypical hyperplasia (AH), lobular or ductal carcinoma in situ (LCIS/DCIS) is associated with change in the CNN breast cancer risk model. Methods: We conducted a retrospective cohort study of patients diagnosed with AH, LCIS, or DCIS at Columbia University Irving Medical Center (CUIMC) in New York, NY between 2007 and 2015. We collected mammograms conducted at baseline (at diagnosis of AH/LCIS/DCIS or start of chemoprevention) and 3-5 years later. Demographic and clinical characteristics, chemoprevention use, and mammography images were extracted from the electronic health record. Briefly, each mammogram was normalized as a map of z-scores and resized to an input image size of 256x256. Then a contracting and expanding fully convolutional CNN architecture was composed entirely of 3x3 convolutions, a total of four strided convolutions instead of pooling layers, and symmetric residual connections. L2 regularization and augmentation methods were implemented to prevent over-fitting. High-risk was defined as an absolute CNN model score >0.5. Change in CNN breast cancer risk model from baseline to follow-up was defined as: 1) stay high risk; 2) stay low risk; 3) increase from low to high risk; 4) decrease from high to low risk. Unordered polytomous regression models were used to assess the association between change in CNN score with chemoprevention use. Results: Among 541 evaluable patients, the mean age was 60 years (SD, 9.56) and 69% were postmenopausal. About a third of women (N=184, 34%) initiated chemoprevention, including 72% on a selective estrogen receptor modulator (SERM), 19% on an aromatase inhibitor (AI), and 9% both. About 57% had high CNN breast cancer risk scores at baseline. Comparing women who initiated chemoprevention to those who did not, more women had a decrease in their CNN breast cancer risk model (33.7% vs. 22.9%) and fewer women had an increase in CNN score (11.4% vs. 20.2%). In multivariate analysis, those who were treated with chemoprevention were less likely to have an increase in CNN risk score (odds ratio=0.51, 95% confidence interval=0.28-0.92). Conclusions: We previously reported that our CNN based algorithm can predict breast cancer risk and we now demonstrate that the CNN breast cancer risk model is modifiable on serial mammograms from high-risk women with proven chemopreventive agents, such as SERMs and AIs. Future studies should focus on whether these CNN mammographic changes are associated with subsequent development of breast cancer and may serve as a pharmacodynamics surrogate endpoint for breast cancer chemoprevention trials. Citation Format: Haley Manley, Simukayi Mutasa, Richard Ha, Katherine Crew. Effect of chemoprevention on convolutional neural network-based breast cancer risk model using a mammographic dataset of women with atypical hyperplasia, lobular and ductal carcinoma in situ [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-02-06.