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

Author

Listed:
  • Luyang Luo

    (The Hong Kong University of Science and Technology
    Harvard University)

  • 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
    Southern Medical University)

  • Pranav Rajpurkar

    (Harvard University)

  • Hao Chen

    (The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology
    The Hong Kong University of Science and Technology)

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.

Suggested Citation

  • Luyang Luo & Mingxiang Wu & Mei Li & Yi Xin & Qiong Wang & Varut Vardhanabhuti & Winnie CW Chu & Zhenhui Li & Juan Zhou & Pranav Rajpurkar & Hao Chen, 2025. "A large model for non-invasive and personalized management of breast cancer from multiparametric MRI," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58798-z
    DOI: 10.1038/s41467-025-58798-z
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    References listed on IDEAS

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    1. Michael Moor & Oishi Banerjee & Zahra Shakeri Hossein Abad & Harlan M. Krumholz & Jure Leskovec & Eric J. Topol & Pranav Rajpurkar, 2023. "Foundation models for generalist medical artificial intelligence," Nature, Nature, vol. 616(7956), pages 259-265, April.
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    3. Hanwen Xu & Naoto Usuyama & Jaspreet Bagga & Sheng Zhang & Rajesh Rao & Tristan Naumann & Cliff Wong & Zelalem Gero & Javier González & Yu Gu & Yanbo Xu & Mu Wei & Wenhui Wang & Shuming Ma & Furu Wei , 2024. "A whole-slide foundation model for digital pathology from real-world data," Nature, Nature, vol. 630(8015), pages 181-188, June.
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