Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method

Purpose: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images. Methods: In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (n = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (n = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models. Results: In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859-0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632-0. 814, 0.641-0.788, 0.621-0.709, 0.620-0.701and 0.611-0.785. Conclusion: The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.