Multi-modal AI for comprehensive breast cancer prognostication

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