Elena Diana Chiru

Abstract Treatment selection in breast cancer is guided by risk assessment using molecular subtypes and clinicopathological characteristics. However, current approaches lack the precision required for optimal clinical decision-making. To address this, we use data from 8161 patients to develop and evaluate an AI test integrating digital pathology with clinical data. The AI test provides a robust method for predicting disease-free interval (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p < 0.001]).

Background The phase 3 NATALEE clinical trial, which defined risk of recurrence (ROR) criteria, showed that ribociclib improves invasive disease-free survival among patients with stage II to III moderate and high-risk hormone receptor–positive (HR+)/ HER2-negative (HER2–) early breast cancer. As these ROR criteria rely exclusively on clinical features, we sought to determine whether incorporating pathology data enhances risk stratification.