Use of a novel convolutional neural network-based mammographic evaluation to assess response to adjuvant endocrine therapy in women with early-stage breast cancer.
Authors: Julia Elizabeth McGuinness, Vicky Ro, Simukayi Mutasa, Richard Ha, Katherine D. Crew
Journal: Journal of Clinical Oncology (2021)

530 Background: The standard of care for early-stage hormone receptor (HR)-positive breast cancer (BC) is 5-10 years of adjuvant endocrine therapy (ET), which leads to a 50-60% relative risk reduction in BC recurrence. However, 10-40% of patients may relapse up to 20 years (y) after diagnosis, and there is a need for biomarkers of response to ET. We developed a novel, fully-automated convolutional neural network (CNN)-based mammographic evaluation that accurately predicts BC risk, which is being evaluated as a pharmacodynamic response biomarker to adjuvant ET. Methods: We conducted a retrospective cohort study among women with HR-positive stage I-III unilateral BC diagnosed at Columbia University Irving Medical Center from 2007-2017, who received adjuvant ET and had at least 2 mammograms of the contralateral breast (baseline and annual follow-up). Demographics, clinical characteristics, BC treatments, and relapse status were extracted from the electronic health record and New York-Presbyterian Hospital Tumor Registry. We performed CNN analysis of mammograms at baseline (start of ET) and annual follow-up. Our primary endpoint was change in CNN risk score, expressed as a continuous variable (range, 0-1). We used two-sample t-tests to assess for differences in mean CNN scores between patients who relapsed or remained in remission. We evaluated if CNN score at baseline and change from baseline were associated with relapse using logistic regression, with adjustment for known prognostic factors. Results: Among 870 evaluable women, mean age at diagnosis was 59.5y (standard deviation [SD], 12.4); 60.3% had stage I tumors, 72.6% underwent lumpectomy, and 45.8% received chemotherapy. With a median follow-up of 4.9y, there were 68 (7.9%) breast cancer relapses (36 distant, 26 local, 6 new primary). Median number of evaluable mammograms per patient was 5 (range, 2-13). Mean baseline CNN risk scores were significantly higher among women who relapsed compared to those in remission (0.258 vs 0.237, p = 0.022), which remained significant after adjustment for known prognostic factors. There was a significant difference in mean absolute change in CNN risk score from baseline to 1y follow-up between those who relapsed vs. remained in remission (0.001 vs. -0.022, p = 0.027), but this was no longer significant in multivariable analysis. Conclusions: We demonstrated that higher baseline CNN risk score was an independent predictor of BC relapse. A greater decrease in mean CNN risk scores at 1-year follow-up after initiating adjuvant ET was seen among BC patients who remained in remission compared to those who relapsed. Therefore, baseline CNN risk scores may identify patients at high-risk for breast cancer recurrence to target for more intensive adjuvant treatment. Early changes in CNN risk scores may be used to predict response to long-term ET in the adjuvant setting.