A large model for non-invasive and personalized management of breast cancer from multiparametric MRI

Luyang Luo, Mingxiang Wu, Mei Li, Yi Xin, Qiong Wang, Varut Vardhanabhuti, Winnie CW Chu, Zhenhui Li (), Juan Zhou (), Pranav Rajpurkar and Hao Chen ()
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Luyang Luo: The Hong Kong University of Science and Technology
Mingxiang Wu: Shenzhen People’s Hospital
Mei Li: PLA Middle Military Command General Hospital
Yi Xin: The Hong Kong University of Science and Technology
Qiong Wang: Chinese Academy of Sciences
Varut Vardhanabhuti: Li Ka Shing Faculty of Medicine, The University of Hong Kong
Winnie CW Chu: The Chinese University of Hong Kong
Zhenhui Li: the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center
Juan Zhou: 5th Medical Center of Chinese PLA General Hospital
Pranav Rajpurkar: Harvard University
Hao Chen: The Hong Kong University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58798-z

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