BACKGROUND: Nuclear grading of clear cell renal cell carcinoma (ccRCC) plays a crucial role in diagnosing and managing the disease. OBJECTIVE: To develop and validate a CT-based Delta-Radiomics model for preoperative assessment of nuclear grading in renal clear cell carcinoma. MATERIALS AND METHODS: This retrospective analysis included surgical cases of 146 ccRCC patients from two medical centers from December 2018 to December 2023, with 117 patients from Hospital and 29 from the *Hospital Affiliated to University of **. Radiomic features were extracted from whole-abdomen CT images, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used for feature selection. The Multi-Layer Perceptron (MLP) approach was employed to construct five predictive models (RAD_NE, RAD_AP, RAD_VP, RAD_Delta1, RAD_Delta2). The models were evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity, while clinical utility was assessed through Decision Curve Analysis (DCA). RESULTS: A total of 1,834 radiomic features were extracted from the three phases of the CT images for each model. The models demonstrated strong classification performance, with AUC values ranging from 0.837 to 0.911 in the training set and 0.608 to 0.869 in the test set. The Rad_Delta1 and Rad_Delta2 models demonstrated advantages in predicting ccRCC pathological grading.The AUC value of the Rad_Delta1 is 0.911in the training set and 0.771 in the external verifcation set.The AUC value of the Rad_Delta2 is 0.881 in the training set and0.608 in the external verifcation set. DCA curves confirmed the clinical applicability of these models. CONCLUSION: CT-based delta-radiomics shows potential in predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).