ArticleNature communications2025
Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Understanding and targeting the tumour matrisome.Nature reviews. Clinical oncology · 2026Review
- Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy-Resection Gap.Journal of clinical medicine · 2026Review
- Development and Validation of a Multimodal-Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer.Cancer research communications · 2026Article
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- Multi-modal AI for comprehensive breast cancer prognostication.Nature communications · 2026Article
- Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.medRxiv : the preprint server for health sciences · 2026Article
- What's new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration.Journal of pathology and translational medicine · 2026Article
- Analytical Validation of Multimodal AI Test Predicting Breast Cancer Recurrence Risk (Ataraxis Breast RISK).Diagnostics (Basel, Switzerland) · 2026Article
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Computational Pathology with Topological signatures and Visual Word Encoding.Research square · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Review
- AI-driven immunotherapy: synergizing with radiotherapy to reconfigure the tumor microenvironment and treatment landscape.Frontiers in pharmacology · 2025Review
- Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer.ESMO open · 2024Review
Corrections and comments
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Authors and funding
32 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 - ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p < 0.05). Applying a 5% MFS event probability threshold stratifies patients into low- and high-risk groups. After dichotomization, combining RlapsRisk BC with clinico-pathological factors increases cumulative sensitivity (0.69 vs 0.63) and dynamic specificity (0.80 vs 0.76) compared to clinical factors alone. Expert analysis of high-impact regions identified by the model highlights well-established morphological features, supporting its interpretability and biological relevance.
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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.