Abstract PO4-01-10: Multi-modal artificial intelligence models from baseline histopathology predict prognosis in HR+ HER2- early breast cancer

Abstract Background: Prognostic assessment in HR+ HER2- early breast cancer (EBC) remains challenging given relatively low rates of disease progression. Nuanced risk stratification is needed for decisions regarding systemic therapy. Modern artificial intelligence (AI)-based techniques have already provided substantial medical progress, particularly in prostate cancer. Here, we leverage multi-modal artificial intelligence (MMAI) trained using digital histopathology and clinical data to evaluate whether the MMAI technology can be expanded to other disease indications by applying the technology to the WSG PlanB and ADAPT trials in HR+ HER2- EBC. Methods: Pre-treatment breast biopsy and surgical hematoxylin and eosin (H hazard ratios were reported per standard deviation increase of the model score. Results: A total of 5539 patients from the two trials with H 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-01-10.