INTRODUCTION: We investigated whether our convolutional neural network (CNN)-based breast cancer risk model is modifiable by testing it on women who had undergone risk-reducing chemoprevention treatment. MATERIALS AND METHODS: We conducted a retrospective cohort study of patients diagnosed with atypical hyperplasia, lobular carcinoma in situ, or ductal carcinoma in situ at our institution from 2007 to 2015. The clinical characteristics, chemoprevention use, and mammography images were extracted from the electronic health records. We classified two groups according to chemoprevention use. Mammograms were performed at baseline and subsequent follow-up evaluations for input to our CNN risk model. The 2 chemoprevention groups were compared for the risk score change from baseline to follow-up. The change categories included stayed high risk, stayed low risk, increased from low to high risk, and decreased from high to low risk. Unordered polytomous regression models were used for statistical analysis, with P < .05 considered statistically significant. RESULTS: Of 541 patients, 184 (34%) had undergone chemoprevention treatment (group 1) and 357 (66%) had not (group 2). Using our CNN breast cancer risk score, significantly more women in group 1 had shown a decrease in breast cancer risk compared with group 2 (33.7% vs. 22.9%; P < .01). Significantly fewer women in group 1 had an increase in breast cancer risk compared with group 2 (11.4% vs. 20.2%; P < .01). On multivariate analysis, an increase in breast cancer risk predicted by our model correlated negatively with the use of chemoprevention treatment (P = .02). CONCLUSIONS: Our CNN-based breast cancer risk score is modifiable with potential utility in assessing the efficacy of known chemoprevention agents and testing new chemoprevention strategies.