Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model

• URL: http://arxiv.org/abs/2408.12606v2
• Date: Mon, 2 Sep 2024 00:52:01 GMT
• Title: Towards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model
• Authors: Luyang Luo, Mingxiang Wu, Mei Li, Yi Xin, Qiong Wang, Varut Vardhanabhuti, Winnie CW Chu, Zhenhui Li, Juan Zhou, Pranav Rajpurkar, Hao Chen,
• Abstract summary: We report a mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure. MOME demonstrated accurate and robust identification of breast cancer. It could reduce the need for biopsies in BI-RADS 4 patients with a ratio of 7.3%, classify triple-negative breast cancer with an AUROC of 0.709, and predict pathological complete response to neoadjuvant chemotherapy with an AUROC of 0.694.
• Score: 19.252851972152957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast magnetic resonance imaging (MRI) is the imaging technique with the highest sensitivity for detecting breast cancer and is routinely used for women at high risk. Despite the comprehensive multiparametric protocol of breast MRI, existing artificial intelligence-based studies predominantly rely on single sequences and have limited validation. Here we report a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, offering a noninvasive method for personalized breast cancer management. We have curated the largest multiparametric breast MRI dataset, involving 5,205 patients from three hospitals in the north, southeast, and southwest of China, for the development and extensive evaluation of our model. MOME demonstrated accurate and robust identification of breast cancer. It achieved comparable performance for malignancy recognition to that of four senior radiologists and significantly outperformed a junior radiologist, with 0.913 AUROC, 0.948 AUPRC, 0.905 F1 score, and 0.723 MCC. Our findings suggest that MOME could reduce the need for biopsies in BI-RADS 4 patients with a ratio of 7.3%, classify triple-negative breast cancer with an AUROC of 0.709, and predict pathological complete response to neoadjuvant chemotherapy with an AUROC of 0.694. The model further supports scalable and interpretable inference, adapting to missing modalities and providing decision explanations by highlighting lesions and measuring modality contributions. MOME exemplifies a discriminative, robust, scalable, and interpretable multimodal model, paving the way for noninvasive, personalized management of breast cancer patients based on multiparametric breast imaging data.

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