Feed

To investigate the CT heterogeneity and dose distribution pattern on the occurrence of radiation pneumonitis (RP), this study retrospectively analyzed 251 lung cancer patients. Based on dose values, each patient's CT and Dose images were divided into 8 block and 6 ring regions based on the intersection of specific CT structure and dose region with specific dose value. 1158 radiomics features were extracted from each modality, characterizing shape, density, voxel intensity, and texture features of each region. Dose-Volume Histogram (DVH) parameters were also calculated. Seven machine learning models were used to predict patients' RP status, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (K-NN), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and Categorical Boosting (CatBoost) models. The results showed that multi-modal dosimetry and radiomics features are more effective in prediction than DHV parameters. For the block regions, areas with dose values ≥ 30 Gy performed the best, with the highest AUC of 0.886, and for the ring regions, areas with dose values between 40~50 Gy achieved the highest AUC of 0.977. In summary, finely divided ring regions had better and more stable predictive performance than block regions, which provides a basis for personalized treatment plans and with the potential to improve treatment outcomes.Clinical Relevance- CT heterogeneity and dose distribution patterns in the ring region with 40~50 Gy are more relevant to the occurrence of RP, physicians should pay more attention to this region in treatment planning.