Abstract
PURPOSES: To develop a combined model integrating multi-channel deep learning features, radiomics features, and clinical variables for noninvasive pathological grading of clear cell renal cell carcinoma (ccRCC). MATERIAL AND METHODS: A retrospective study was conducted on 496 patients with pathologically confirmed ccRCC who underwent preoperative triple-phase contrast-enhanced CT. Multi-channel deep learning features were extracted from three ROI settings (conventional, tumor-only, and 5-mm expansion) by stacking arterial, medullary and excretory phases. These were fused with arterial-phase radiomics features and clinical data to construct and compare predictive models. RESULTS: In the ResNet50 model, the expanded 5mm ROI slice model had an AUC of 0.791 in the training cohort and 0.780 in the testing cohort, indicating that the model could effectively predict the pathological grading of ccRCC. By combining deep learning features with radiomics and clinical features, the integrated model achieved AUCs of 0.855 in the training cohort and 0.849 in the testing cohort, significantly outperforming the individual radiomics and clinical models. Decision curve analysis (DCA) further showed that the clinical-imaging combined model provided a higher net benefit. CONCLUSION: Multi-channel, multiphase CT fusion, when integrated with radiomics and clinical features, can significantly enhance predictive accuracy for ccRCC grading, providing a promising and interpretable noninvasive tool to support individualized treatment planning.