ArticleNature communications2025
A large model for non-invasive and personalized management of breast cancer from multiparametric MRI.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- Tina: A diffusion neural network for generating personalized AI models from text prompts.Patterns (New York, N.Y.) · 2026Article
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- Multi-task deep learning assists detection and diagnosis of gliomas and brain metastases.NPJ digital medicine · 2026Article
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- The current and future landscape of AI foundation models for cancer management.Nature communications · 2026Article
- Multiparameter concept-based interpretable model for early breast cancer diagnosis and structured reporting: a multi-center, multi-reader, radiologist-in-the-loop study.BMC medicine · 2026Article
- Artificial intelligence in breast cancer: applications and advancements.Cancer biology & medicine · 2026Review
- Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.Research (Washington, D.C.) · 2026Article
- The role of AI-driven decision support in immunotherapy for predicting pathological complete response in triple-negative breast cancer.Frontiers in oncology · 2026Article
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
- A deep learning model based on multiphase DCE-MRI for preoperative prediction of Ki-67 expression in breast cancer.Frontiers in oncology · 2026Article
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Breast Magnetic Resonance Imaging (MRI) demonstrates the highest sensitivity for breast cancer detection among imaging modalities and is standard practice for high-risk women. Interpreting the multi-sequence MRI is time-consuming and prone to subjective variation. We develop a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, leveraging breast MRI scans from 5205 female patients in China for model development and validation. MOME matches four senior radiologists' performance in identifying breast cancer and outperforms a junior radiologist. The model is able to reduce unnecessary biopsies in Breast Imaging-Reporting and Data System (BI-RADS) 4 patients, classify triple-negative breast cancer, and predict pathological complete response to neoadjuvant chemotherapy. MOME further supports inference with missing modalities and provides decision explanations by highlighting lesions and measuring modality contributions. To summarize, MOME exemplifies an accurate and robust multimodal model for noninvasive, personalized management of breast cancer patients via multiparametric MRI.
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