Abstract PS3-04-04: Ai predicts response to neoadjuvant therapy in breast cancer across diverse cohorts

Abstract Background: Neoadjuvant therapy (NAT) is standard of care for many early-stage breast cancer patients. Traditional clinical characteristics such as ER and HER2 receptor status provide useful prognostic information, but lack sufficient precision when used in isolation. Basic histological features, such as tumor infiltrating lymphocytes, are also associated with NAT response. With the advent of computational pathology and machine learning, it is now possible to extract complex spatial and morphological predictive patterns from digitized slides that are not apparent to the human eye. Integrating histopathologic features with clinical variables holds promise for building robust predictive models that can guide personalized treatment planning, reduce overtreatment, and improve survival outcomes in breast cancer patients undergoing neoadjuvant therapy. Ataraxis Breast Neo (ATX-N) is a multi-modal artificial intelligence test that integrates morphological features extracted from standard H 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-04-04.