Abstract P3-08-11: The application of machine learning techniques to standardize breast cancer grading and develop multivariate risk outcome models

Background: The 2019 NCCN guidelines and the College of American Pathology (CAP) endorse consistent, unambiguous comprehensive pathology reporting for invasive breast cancer. Challenges surrounding inter-pathologist variability and the lack of quantitative, standardized approaches to histologic grade are significant and critical to patient management. We developed an automated multi-network machine learning platform for histologic grading and examined performance with clinical outcome. Methods: Using the Cancer Genome Atlas (TCGA) breast cancer (BCA) image dataset and clinical data as a training cohort, we evaluated 420 conventional HE 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-11.