A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI

Authors：Zhou, Xin-Xiang; Zhang, Lan; Cui, Quan-Xiang; Li, Hui; Sang, Xi-Qiao; Zhang, Hong-Xia; Zhu, Yue-Min; Kuai, Zi-Xiang^*

Source：Journal of Magnetic Resonance Imaging, 2023.

DOI：10.1002/jmri.28895

• breast cancer
• molecular subtype
• deep learning
• diffusion-weighted
• dynamic contrast-enhanced
• magnetic resonance imaging

Background: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease. Purpose: To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI. Study Type: Prospective. Subjects: 486 female breast cancer patients (training/validation/test: 64%/16%/20%). Field Strength/Sequence: 3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases). Assessment: The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI. Statistical Tests: Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant. Results: The CDFR-DNN ( accuracies, 0.79 similar to 0.80; AUCs, 0.93 similar to 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 similar to 0.78; AUCs, 0.92 similar to 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal ( P = 0.065 similar to 1.000) to DCE-MRI (accuracies, 0.79 similar to 0.80; AUCs, 0.93 similar to 0.95). The predictive performance of the ME- DNN on MP-MRI ( accuracies, 0.85 similar to 0.87; AUCs, 0.96 similar to 0.97) was superior to those of both the CDFR- DNN and NCDFR- DNN on either DW-MRI or DCE-MRI. Data Conclusion: The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction.

• Institution
  1; 哈尔滨医科大学