ArticleNature communications2025
Multimodal histopathologic models stratify hormone receptor-positive early breast cancer.
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 30 papers.
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Who cites it
30 citing papers in PubMed.
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Deep learning-based classification of gh-secreting pituitary neuroendocrine tumors granulation patterns using H&E-stained histopathology.BMC endocrine disorders · 2026Article
- Review
- 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
- 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
- Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.NPJ breast cancer · 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
- [EJNMMI research · 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
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.NPJ digital medicine · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Cell type annotation using large language models (LLMs) and CytoAnalyst.Bioinformatics advances · 2026Article
- Identification of NETs and inflammation-related prognostic genes in breast cancer and PCR experimental validation.Frontiers in genetics · 2026Article
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The 19th St. Gallen international breast cancer conference 'primary therapy of patients with early breast cancer. Evidence, controversies, consensus': key moments and breakthroughs.Ecancermedicalscience · 2026Article
- Integrated local and global Ki67 profiling for prognostic stratification and therapeutic guidance in high-grade neuroendocrine neoplasms: a multicenter validation study.International journal of surgery (London, England) · 2025Article
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
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Authors and funding
28 authors.
Funding
Abstract
The Oncotype DX® Recurrence Score (RS) is an assay for hormone receptor-positive early breast cancer with extensively validated predictive and prognostic value. However, its cost and lag time have limited global adoption, and previous attempts to estimate it using clinicopathologic variables have had limited success. To address this, we assembled 6172 cases across three institutions and developed Orpheus, a multimodal deep learning tool to infer the RS from H&E whole-slide images. Our model identifies TAILORx high-risk cases (RS > 25) with an area under the curve (AUC) of 0.89, compared to a leading clinicopathologic nomogram with 0.73. Furthermore, in patients with RS ≤ 25, Orpheus ascertains risk of metastatic recurrence more accurately than the RS itself (0.75 vs 0.49 mean time-dependent AUC). These findings have the potential to guide adjuvant therapy for high-risk cases and tailor surveillance for patients at elevated metastatic recurrence risk.
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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.