Xin‐Xiang Zhou, Lan Zhang, Quan‐Xiang Cui, Hui Li, Xi‐Qiao Sang, Hong‐Xia Zhang, Yue‐Min Zhu, Zi‐Xiang Kuai

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

  • Radiology, Nuclear Medicine and imaging
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BackgroundDynamic 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.PurposeTo 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 TypeProspective.Subjects486 female breast cancer patients (training/validation/test: 64%/16%/20%).Field Strength/Sequence3.0 T/DW‐MRI (13 b‐values) and DCE‐MRI (one precontrast and five postcontrast phases).AssessmentThe 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 TestsModel 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.ResultsThe CDFR‐DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR‐DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW‐MRI. Utilizing the CDFR‐DNN, DW‐MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE‐MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME‐DNN on MP‐MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR‐DNN and NCDFR‐DNN on either DW‐MRI or DCE‐MRI.Data ConclusionThe 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.Level of Evidence2Technical Efficacy Stage1

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