Valentin Gaury (Senior Data Scientist) and colleagues at Owkin, in collaboration with Dr. Ingrid Garberis and researchers from Gustave Roussy, Paris-Saclay University (Villejuif, France), have recently published a paper on the deep learning assessment of metastatic relapse risk using digitized breast cancer histological slides.

Following this recent publication, Pathology News will host a webinar where Valentin Gaury will join expert pathologist Magali Lacroix-Triki to present new RlapsRisk® BC findings, compare its performance with current prognostic tools, and discuss its potential impact on breast cancer care.

Learn more on the Pathology News event page or secure your spot by registering here.

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.