Abstract PS5-21: Use of a novel convolutional neural network (CNN)-based mammographic evaluation to assess response to adjuvant endocrine therapy

Background: The standard of care for early-stage hormone receptor (HR)-positive breast cancer is 5-10 years of adjuvant endocrine therapy (ET), which is associated with a 50-60% relative risk reduction in breast cancer recurrence. However, patients remain at risk of recurrence up to 20 years after diagnosis, and there is a need for biomarkers of response to ET. While decrease in mammographic density (MD) is associated with improved disease-free survival (DFS), measurement is subject to variability in radiologists’ interpretations. We developed a novel, fully-automated convolutional neural network (CNN)-based mammographic evaluation that is a more accurate, independent predictor of breast cancer risk than MD. We evaluated the role of the CNN model as a pharmacodynamic biomarker of response to adjuvant ET among women with early-stage HR-positive breast cancer. Methods: We conducted a retrospective cohort study among women with HR-positive, stage I-III unilateral breast cancer diagnosed at Columbia University Irving Medical Center (CUIMC) in New York, NY, from 2007-2017, who received adjuvant ET and had at least two contralateral mammograms (baseline and on ET) in our electronic health record (EHR). Demographics, clinical characteristics, breast cancer treatment (surgery, radiation, systemic therapy), type of ET (aromatase inhibitor [AI], tamoxifen, or both), and breast cancer relapse (distant, local, new breast primary) were extracted from the EHR and New York Presbyterian Hospital (NYPH) Tumor Registry. We performed CNN analysis of contralateral mammograms at baseline (within 1 year of diagnosis and prior to ET) and at 1- and 2-year (y) follow-up on ET. The primary endpoint was change in CNN risk score, expressed as a continuous variable (range, 0-1.00). Paired t-tests were used to assess for differences in CNN scores between baseline and 1y and 2y follow-up. Logistic regression was used to evaluate if CNN scores at baseline and change from baseline were associated with relapse, with adjustment for known prognostic factors. Results: Of 2,559 women diagnosed with stage I-III HR-positive breast cancer at CUIMC from 2007-2017, 465 had serial mammograms available for CNN analysis. Mean age at diagnosis was 61.2y (SD, 12.2y), and 38.1% of women were non-Hispanic white, 12.5% non-Hispanic black, 39.8% Hispanic, and 9.7% other/unknown. At initial diagnosis, 62.2% had stage I tumors, 74.4% received lumpectomy, and 41.7% received chemotherapy. There were 28 (6.0%) breast cancer relapses (15 distant, 10 local, 3 new primary). Women who had relapsed were more likely to be obese (p=0.009), have higher tumor stage (p Conclusions: We demonstrated a significant change in CNN risk scores from baseline to short-term follow-up among women with early-stage breast cancer who received adjuvant ET. Women who relapsed had higher baseline CNN risk scores, but this was not significant. Due to the small number of relapses, change in CNN risk scores was not associated with breast cancer recurrence. The CNN risk model will be evaluated prospectively in adjuvant clinical trials, to further evaluate its role as a predictor of breast cancer relapse and response to adjuvant ET. Citation Format: Julia E McGuinness, Jana Lee, Aishwarya Anuraj, Simukayi Mutasa, Richard S Ha, MD, Katherine D Crew, MDMS. Use of a novel convolutional neural network (CNN)-based mammographic evaluation to assess response to adjuvant endocrine therapy [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-21.