A large model for non-invasive and personalized management of breast cancer from multiparametric MRI
Luyang Luo, Mingxiang Wu, Mei Li, Xin Yi, Qiong Wang, Varut Vardhanabhuti, Chiu‐Wing Winnie Chu, Zhenhui Li, Juan Zhou, Pranav Rajpurkar, Hao Chen
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. Artificial intelligence-based studies for breast cancer magnetic resonance imaging (MRI) predominantly rely on single sequences and have limited validation. Here, the authors develop a mixture-of-modality-experts model (MOME) that integrates multiparametric breast cancer MRI information within a unified structure, which shows reliable performance in a large cohort.