Enhanced Computed Tomography-Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma.

Objective The aim of the study was to explore the value of enhanced computed tomography (CT)-based radiomics signature combined with clinical features in evaluating nuclear grading of clear cell renal cell carcinoma (ccRCC). Methods One hundred one patients with ccRCC were classified into low- and high-grade group, and the data were divided into training set and verification set. Radiomics signatures were constructed in the training set in enhanced 3 stages and the combination of them. The predictive nomogram was constructed. The classification efficiency and the clinical practicability of the integrated radiomics model were evaluated. Results The classification efficiency of enhanced 3-stage integrated histology model was higher than that of each single-phase model. The predictive nomogram incorporated the best radiomics signature, and the independent clinical risk factors showed good performance. A decision curve analysis curve shows that the net benefit of the combined model. Conclusions It is feasible to evaluate the nuclear grading of ccRCC based on enhanced CT radiomics signature combined with clinical features.