Objective：To evaluate the predictive value of preoperative enhanced CT radiomic feature models in comparison with clinical - pathological feature models for disease -free survival（DFS）in patients with non -metastatic grades 2-3 clear cell renal cell carcinoma（ccRCC）after surgery. Methods：A retrospective analysis was conducted on 315 patients with non -metastatic ccRCC who underwent surgical treatment and were pathologically graded as grades 2-3 between January 2013 and December 2020. Preoperative enhanced CT images，clinical-pathological data，and follow-up information were collected. The region of interest（ROI）of the lesion was delineated，and radiomic features were extracted using Python. Patients’radiomic scores were calculated using the least absolute shrinkage and selection operator（LASSO）and Cox regression analysis. Clinical - pathological feature models，radiomic models （corticomedullary phase，parenchymal phase，corticomedullary+parenchymal phase），and combined radiomic and clinical-pathological feature models were constructed to predict DFS. Results：When predicting DFS in non - metastatic grades 2-3 ccRCC patients，the combined radiomic model of corticomedullary + parenchymal phase demonstrated superior predictive efficacy（C - index：training set 0.848，validation set 0.754）compared to single-phase radiomic models（corticomedullary phase C-index：training set 0.832，validation set 0.701；parenchymal phase C - index：training set 0.842，validation set 0.720）. However，the combined model incorporating both radiomic and clinical-pathological features exhibited the highest predictive efficacy（C-index：training set 0.857，validation set 0.832）. Conclusion：The model constructed based on radiomics features extracted from preoperative enhanced CT corticomedullary phase + parenchymal phase images combined with clinicopathological features is helpful in predicting postoperative DFS in patients with non-metastatic grades 2-3 ccRCC.