A large model for non-invasive and personalized management of breast cancer from multiparametric MRI
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.
Authors
- Pranav Rajpurkar (ORCID: https://orcid.org/0000-0002-8030-3727)
- Varut Vardhanabhuti (ORCID: https://orcid.org/0000-0001-6677-3194)
- Luyang Luo (ORCID: https://orcid.org/0000-0002-7485-4151)
- Mingxiang Wu (ORCID: https://orcid.org/0000-0002-5297-438X)
- Xin Yi (ORCID: https://orcid.org/0000-0002-1639-6324)
- Chiu‐Wing Winnie Chu (ORCID: https://orcid.org/0000-0003-4962-4132)
- Juan Zhou (ORCID: https://orcid.org/0000-0002-3573-4560)
- Hao Chen (ORCID: https://orcid.org/0000-0002-8400-3780)
- Qiong Wang (ORCID: https://orcid.org/0000-0001-5673-5063)
- Mei Li
- Zhenhui Li (ORCID: https://orcid.org/0009-0009-3613-9403)
Institutions
- Harvard University (US)
- Chinese University of Hong Kong (HK)
- Chinese Academy of Sciences (CN)
- Hong Kong University of Science and Technology (HK)
- Kunming Medical University (CN)
- Chinese PLA General Hospital (CN)
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- ShenZhen People’s Hospital (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Wuhan General Hospital of Guangzhou (CN)
- Southern Medical University (CN)
- University of Hong Kong (HK)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1038/s41467-025-58798-z
- Citations
- 8
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 10.88
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- Innovation and Technology Commission
- Kunming Medical University
- Shenzhen Municipal Science and Technology Innovation Council