Objective To investigate whether a model constructed based on multi-scale 3D-CT features can provide a robust prediction of renal cell carcinoma(RCC)pathological subtypes and WHO/ISUP grade.Methods A retrospective analysis of 507 patients with postoperative pathologically confirmed RCC from four medical centers were divided into a train-ing set(centers 1-3,346 cases),an internal validation set(centers 1-3,87 cases),and an external test set(center 4,74 cases).The prediction model constructed in this study contained the following modules:a kidney-tumor semantic segmentation model constructed by 3D-UNet,a multi-scale features extractor based on region of interest(ROI),and two classifiers constructed by the XGBoost algorithm.In addition,we used a strategy of five-fold cross-validation and multi-cen-ter external testing to validate and test the stability and generalization ability of the models.Finally,we also explored the contribution of each feature to the model decision by calculating the amount of SHAP for a single feature.Results The accuracy and efficacy of the multi-phase CT model were superior compared to the single-phase CT model for both RCC path-ological subtype and WHO/ISUP grade prediction.In the predicted RCC pathological subtype model,the AUROC for the five-fold cross-validation were 0.86,0.85,0.88,0.88,and 0.89,respectively,and the AUROC for the internal valida-tion set and external test set of the optimal model were 0.89 and 0.75,respectively.While for the WHO/ISUP grade pre-diction model,the AUROC for the five-fold cross-validation were 0.81,0.82,0.79,0.7 and 0.81,respectively,and the AUROC for the internal validation set and external test set of the optimal model were 0.89 and 0.75,respectively.In terms of model solvability analysis,the first-order statistics and gray-scale matrix features were the first and second features of the RCC organizational subtype prediction models,respectively;while the first-order statistics in the WHO/ISUP hierarchical prediction model played the most important role in the WHO/ISUP grade prediction model.Conclusion The model con-structed based on multi-scale 3D-CT features can provide a strong prediction for preoperative assessment of RCC pathologi-cal subtypes and WHO/ISUP grade.
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