Dual-radiomics based on SHapley additive explanations for predicting hematologic toxicity in concurrent chemoradiotherapy patients

BACKGROUND: This study investigates the application of a machine learning model that integrates radiomic features and dosiomic features to predict hematologic toxicity (HT) in patients with advanced cervical cancer undergoing concurrent chemoradiotherapy (CCRT). Two integration methods based on SHapley Additive exPlanations (SHAP) values for dual-radiomic features were compared. METHODS: Clinical information, planning CT images, and dose distribution files from 205 patients with advanced cervical cancer treated with CCRT were retrospectively collected. Patients were categorized by HT severity, with 80% of the data used for training and 20% for testing. Radiomic features and dosiomic features were extracted from the same regions of interest, and SHAP-based feature selection was employed. Extreme gradient boosting models were developed using two feature selection schemes: single-step and multi-step. Sensitivity, specificity, and area under the curve (AUC) values on the test set were used to evaluate model performance. RESULTS: For the single-step feature selection scheme, the best hybrid model achieved an AUC of 0.79, sensitivity of 0.67, and specificity of 0.72. For the multi-step feature selection scheme, the best hybrid model achieved an AUC of 0.81, sensitivity of 0.75, and specificity of 0.83. Furthermore, the hybrid models outperformed those using radiomic or dosiomic features alone. CONCLUSIONS: Combining radiomic and dosiomic features improves classification performance in predicting HT in patients with advanced cervical cancer undergoing CCRT, with the multi-step SHAP-based feature selection scheme offering additional advantages. These models hold promise for optimizing patient treatment strategies.