MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.
Authors
Affiliations (7)
Affiliations (7)
- The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Medical Physics Research Center, Nanjing Medical University, Changzhou 213003, China; Key Laboratory of Medical Physics in Changzhou, Changzhou 213003, China.
- Department of Radiotherapy, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China.
- Department of Radiotherapy, Su Bei People's Hospital, Yangzhou 225001, China.
- Department of Radiotherapy, Cancer Hospital of Jiangsu Province, Nanjing 210009, China.
- The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Medical Physics Research Center, Nanjing Medical University, Changzhou 213003, China; Key Laboratory of Medical Physics in Changzhou, Changzhou 213003, China; School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
- The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Changzhou 213003, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213003, China; Medical Physics Research Center, Nanjing Medical University, Changzhou 213003, China; Key Laboratory of Medical Physics in Changzhou, Changzhou 213003, China. Electronic address: [email protected].
Abstract
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). 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. 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. 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.