Incremental diagnostic value of tumor habitat radiomics for risk stratification in thymic epithelial tumors.

Purpose: To determine the incremental diagnostic value of habitat radiomics for risk stratification of thymic epithelial tumors (TETs) based on contrast-enhanced CT (CECT). Methods: This retrospective study included 220 patients with pathologically confirmed TETs (82 high-risk [B2/B3/thymic carcinoma] and 138 low-risk [A/AB/B1]) who underwent preoperative CECT. Tumors were segmented into 3 subregions (habitats) using k-means clustering, and radiomic features were extracted from both whole-tumor and subregions. After feature selection (variance threshold, reproducibility evaluation, XGBoost-based importance ranking, and recursive feature elimination), three machine learning models were developed (1): a conventional radiomics model (2), a habitat radiomics model, and (3) a combined model integrating both feature sets. Model performance was evaluated using ROC analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration metrics, and decision curve analysis (DCA). Results: The combined model demonstrated superior discrimination (AUC: 0.900) compared to the conventional (AUC: 0.819) and habitat (AUC: 0.734) radiomics models in the independent test set. Although DeLong's test showed no statistically significant difference (p=0.161), the performance of combined model demonstrated incremental diagnostic value (NRI: 0.286; IDI: 0.209). Calibration and DCA confirmed its robustness and higher net benefit across decision thresholds. While the models' training performance might suggest overfitting, their test results demonstrate generalizability. Conclusions: The habitat radiomics approach enables accurate risk stratification prediction in TETs and demonstrates potential as a clinically valuable tool to augment the performance of conventional radiomics models in routine practice.