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 < 0.001]). In a direct comparison, the AI test displays numerically higher discrimination (C-index: 0.67 [0.61–0.74]) than the standard-of-care 21-gene assay (C-index: 0.61 [0.49–0.73]). Across molecular subtypes, the AI test demonstrates robust prognostic performance, including in triple negative breast cancer (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no guideline-recommended assays currently exist. These findings highlight the potential of AI-based pathology tests as a promising tool for improved risk stratification across all major subtypes, with implications for clinical decision-making. Selecting appropriate treatment for breast cancer is guided by molecular subtypes and clinical characteristics. Here, the authors show that their AI-based approach, which integrates digital pathology images and clinical data, demonstrates robust accuracy in predicting the risk of cancer recurrence across major molecular breast cancer subtypes, including triple negative breast cancer.