Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics

Abstract

Background: Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear. Purpose: To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics. Study Type: Prospective. Population: A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%). Field Strength/Sequence: A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values). Assessment: After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER−, PR+ vs. PR−, HER2+ vs. HER2−, Ki-67+ vs. Ki-67−, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression. Statistical Tests: Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant. Results: With few exceptions, no significant differences (P = 0.062–0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62–0.87) and NME-DWI (AUC = 0.62–0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68–0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62–0.93) outperformed other three models (AUC = 0.62–0.90). Data Conclusion: NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI. Evidence Level: 2. Technical Efficacy: Stage 2.