A large model for non-invasive and personalized management of breast cancer from multiparametric MRI
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58798-z |
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| _version_ | 1849699144288960512 |
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| author | Luyang Luo Mingxiang Wu Mei Li Yi Xin Qiong Wang Varut Vardhanabhuti Winnie CW Chu Zhenhui Li Juan Zhou Pranav Rajpurkar Hao Chen |
| author_facet | Luyang Luo Mingxiang Wu Mei Li Yi Xin Qiong Wang Varut Vardhanabhuti Winnie CW Chu Zhenhui Li Juan Zhou Pranav Rajpurkar Hao Chen |
| author_sort | Luyang Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8b1317e31b8046b881fd3eb0c16631da |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8b1317e31b8046b881fd3eb0c16631da2025-08-20T03:18:42ZengNature PortfolioNature Communications2041-17232025-04-0116111410.1038/s41467-025-58798-zA large model for non-invasive and personalized management of breast cancer from multiparametric MRILuyang Luo0Mingxiang Wu1Mei Li2Yi Xin3Qiong Wang4Varut Vardhanabhuti5Winnie CW Chu6Zhenhui Li7Juan Zhou8Pranav Rajpurkar9Hao Chen10Department of Computer Science and Technology, The Hong Kong University of Science and TechnologyDepartment of Radiology, Shenzhen People’s HospitalDepartment of Radiology, PLA Middle Military Command General HospitalDepartment of Computer Science and Technology, The Hong Kong University of Science and TechnologyShenzhen Institute of Advanced Technology, Chinese Academy of SciencesDepartment of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong KongDepartment of Imaging and Interventional Radiology, The Chinese University of Hong KongDepartment of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer CenterDepartment of Radiology, 5th Medical Center of Chinese PLA General HospitalDepartment of Biomedical Informatics, Harvard UniversityDepartment of Computer Science and Technology, The Hong Kong University of Science and TechnologyAbstract 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.https://doi.org/10.1038/s41467-025-58798-z |
| spellingShingle | Luyang Luo Mingxiang Wu Mei Li Yi Xin Qiong Wang Varut Vardhanabhuti Winnie CW Chu Zhenhui Li Juan Zhou Pranav Rajpurkar Hao Chen A large model for non-invasive and personalized management of breast cancer from multiparametric MRI Nature Communications |
| title | A large model for non-invasive and personalized management of breast cancer from multiparametric MRI |
| title_full | A large model for non-invasive and personalized management of breast cancer from multiparametric MRI |
| title_fullStr | A large model for non-invasive and personalized management of breast cancer from multiparametric MRI |
| title_full_unstemmed | A large model for non-invasive and personalized management of breast cancer from multiparametric MRI |
| title_short | A large model for non-invasive and personalized management of breast cancer from multiparametric MRI |
| title_sort | large model for non invasive and personalized management of breast cancer from multiparametric mri |
| url | https://doi.org/10.1038/s41467-025-58798-z |
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