Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Radiology, Center for Regeneration and Aging Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
- School of Control and Computer Engineering, North China Electric Power University, Beijing, China.
- Department of Radiology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang, China.
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China.
- Hangzhou Shuxi Intelligent Technology Co., LTD, Hangzhou, China.
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China. [email protected].
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China. [email protected].
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China. [email protected].
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China. [email protected].
Abstract
Breast cancer treatments are often tailored to recurrence risk to improve outcomes, but reliable risk stratification methods are lacking. To predict preoperative breast cancer recurrence risk through a tridimensional synergy model integrating breast MRI, radiomics, and deep learning. Retrospective. 428 female patients were randomly divided into training (n = 299, 52.5 ± 12.2 years) and internal validation (n = 129, 53.2 ± 11.5 years) sets, with an external validation set of 196 patients (51.3 ± 11.2 years). Multiparametric 3T MRI included fat-suppressed T2-weighted (T2WI) spin-echo, axial diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) with one pre- and five post-contrast axial acquisitions. We compared recurrence-free survival (RFS) prediction among DLR, DLC, and DLRC models, selected the optimal DLRC to stratify patients by risk, assessed RFS differences, and validated predictions, confirming effective risk stratification. Continuous corrected chi-squared tests, one-way ANOVA, log-rank test. A two-tailed P< 0.05 was considered statistically significant. The DL model showed predictive capability for 3-year RFS, with AUCs of 0.75 (95% CI, 0.65-0.84) in the training set, 0.74 (95% CI, 0.56-0.92) in the internal validation set, and 0.65 (95% CI, 0.53-0.78) in the external validation set. The DLR model achieved improved AUCs of 0.82 (95% CI: 0.74-0.90), 0.83 (95% CI: 0.73-0.93), and 0.67 (95% CI: 0.55-0.80) for predicting 3-year recurrence-free survival (RFS) in the training, internal validation, and external validation sets, respectively. The DLC model outperformed the DL model alone. The DLRC model achieved the best performance in the training and internal validation sets. Its predictive ability remained discernible but was attenuated in the external validation set, with AUCs of 0.95 (95% CI: 0.91-0.99), 0.89 (95% CI: 0.79-0.99), and 0.79 (95% CI: 0.67-0.91) across the respective datasets. The DLRC model effectively predicted and stratified breast cancer recurrence risk by integrating deep learning, radiomics, and clinicopathological features. 3. Stage 5.