Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides
Ingrid Garberis, V. Gaury, Charlie Saillard, Damien Drubay, K. Elgui, Benoît Schmauch, A. Jaeger, Loïc Herpin, J. Linhart, M. Sapateiro, F. Bernigole, Aurélie Kamoun, Alexandre Filiot, Oussama Tchita, Rémy Dubois, M. Auffret, Lionel Guillou, Imad Bousaid, Mikael Azoulay, Jérôme Lemonnier, Meriem Sefta, Sibille Everhard, A. Sarrazin, J-F Reboud, F. Brulport, J. Dachary, Barbara Pistilli, Suzette Delaloge, Pierre Courtiol, Fabrice André, V. Aubert, Magali Lacroix‐Triki
Abstract
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p < 0.05). Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. After dichotomization, combining RlapsRisk BC with clinico-pathological factors increases cumulative sensitivity (0.69 vs 0.63) and dynamic specificity (0.80 vs 0.76) compared to clinical factors alone. Expert analysis of high-impact regions identified by the model highlights well-established morphological features, supporting its interpretability and biological relevance. Early breast cancer is often responsive to treatment, however, long-term prognosis is variable. Here, the authors utilise a deep learning model to predict metastasis free survival using digitised tumour slides.