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- Title
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.
- Authors
Zhang, Lan; Zhou, Xin‐Xiang; Liu, Lu; Liu, Ao‐Yu; Zhao, Wen‐Juan; Zhang, Hong‐Xia; Zhu, Yue‐Min; Kuai, Zi‐Xiang
- 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.
- Subjects
CONTRAST-enhanced magnetic resonance imaging; DIFFUSION magnetic resonance imaging; PROGNOSIS; RADIOMICS; FISHER discriminant analysis
- Publication
Journal of Magnetic Resonance Imaging, 2023, Vol 58, Issue 5, p1590
- ISSN
1053-1807
- Publication type
Article
- DOI
10.1002/jmri.28611