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

Abstract

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 H&E images (one slide per patient). An artificial intelligent (AI) / deep-learning based workflow is used to normalize staining differences, identify regions of tumor vs. normal and characterize individual cellular attributes of breast cancer grading systems including overall gland structure, nuclear morphology, and mitotic figures. Individual features were correlated with overall survival using the concordance index (c-index). Support vector models and Kaplan-Meier incidence curves were used to further understand feature importance. Results: Using the AI platform, 88 image features representing BCA gland morphology and cellular-nuclear attributes were generated from 420 patients with 36 events including metastasis and or death with a median overall survival (OS) of 5-years. Three clinical variables were available including AJCC stage, grade and age at diagnosis. Image features were prioritized on C-index <0.5 or >0.5 which reflects increased or reduced risk of poor outcome, respectively. A training model consisting of image features + clinical data produced a C-index of 0.85, Hazards ratio 11, p<0.001. Only 1 clinical feature (stage) and 9 imaging features (all with individual c-indices <0.5) representing nuclear shape, size, number and mitotic figure activity were selected. Conclusions: Our innovative lab-based AI / deep-learning platform produced accurate BCA risk models to predict metastasis and OS through automated H&E BCA grading. Future analyses include a multi-site validation study to confirm these initial training results. Citation Format: Marcel Prastawa, Abishek Sainath Madduri, Brandon Veremis, Alexander Shtabsky, Bahram Marami, Jack Zeineh, Michael Joseph Donovan, Gerardo Fernandez. The application of machine learning techniques to standardize breast cancer grading and develop multivariate risk outcome models [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 P3-08-11.