MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), and Transformer-derived deep learning features (Swin Transformer, SwinT) to predict post-radiotherapy radiation-induced carotid artery injury (RICAI) in nasopharyngeal carcinoma (NPC).

MATERIALS AND

METHODS

In this multicenter retrospective study, 500 NPC patients from four hospitals were allocated to training (n = 274), internal testing (n = 118), and external testing cohorts (n = 108). Rad features were extracted from MRI-defined carotid artery regions of interest, SRad features from K-means-derived subregions, and deep features from a SwinT backbone. Single-source and fusion models were developed. Discrimination (AUC), classification (ACC/SEN/SPE), calibration (Brier score and calibration curves), reclassification (NRI/IDI), and interpretability (SHAP) were assessed.

RESULTS

RICAI was observed in 48.5%, 48.3%, and 54.6% of the training, internal testing, and external testing cohorts, respectively. Among single-source models, SwinT and SRad showed comparable performance, with Rad slightly inferior; all outperformed the clinical model. The fused Rad-SRad-SwinT achieved the best performance, with AUCs of 0.814 (95% CI: 0.737-0.891) in internal testing and 0.871 (95% CI: 0.794-0.932) in external testing, alongside favorable classification in external testing (ACC 0.815, SEN 0.763, SPE 0.878) and good calibration (Brier score 0.148). NRI/IDI analyses indicated significantly improved reclassification versus single-source models. SHAP analyses demonstrated that SwinT-derived features contributed most to model decisions, followed by SRad and Rad, supporting complementary gains from deep semantic representation and subregional heterogeneity quantification.

CONCLUSION

Integrating multilevel radiomics with Transformer-derived deep learning features enhances prediction of RICAI after NPC radiotherapy and shows promise as a noninvasive risk-stratification tool.