Deep learning radiomics based on preoperative multiparametric MRI in predicting breast cancer recurrence risk
TL;DR
The DLRC model effectively predicted and stratified breast cancer recurrence risk by integrating deep learning, radiomics, and clinicopathological features and achieved the best performance in the training and internal validation sets.
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.