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.