Multi-modal AI for comprehensive breast cancer prognostication.
Publication Title
Nat Commun
Document Type
Article
Publication Date
5-20-2026
Keywords
washington; renton; ai
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]). 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.
Area of Special Interest
Cancer
Area of Special Interest
Women & Children
Specialty/Research Institute
Oncology
DOI
10.1038/s41467-026-73088-y
