ArticleNature communications2026
Multi-modal AI for comprehensive breast cancer prognostication.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Oncotype DX: Clinical Utility, Evidence, and Future Trends in Personalized Breast Cancer Management.International journal of cancer · 2026Review
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.medRxiv : the preprint server for health sciences · 2026Article
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Accessible Clinical Tool for Prognosis and Chemotherapy Prediction in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer in Diverse and Low-Resource Settings.JCO global oncology · 2026Article
- Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study.The Lancet. Oncology · 2026Article
- Analytical Validation of Multimodal AI Test Predicting Breast Cancer Recurrence Risk (Ataraxis Breast RISK).Diagnostics (Basel, Switzerland) · 2026Article
- Breast cancer classification based on microcalcifications using dual branch vision transformer fusion.Scientific reports · 2025Article
- External validation of precisebreast, a digital prognostic test for predicting breast cancer recurrence, in an early-stage cohort from the Netherlands.Breast cancer research : BCR · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
51 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.