• Qiong Li, Yu-Jia Liu, Di Dong, Xu Bai, Qing-Bo Huang, Ai-Tao Guo, Hui-Yi Ye, Jie Tian, and Hai-Yi Wang.
• Department of Radiology, Tianjin Nankai Hospital (Tianjin Hospital of Integrated Traditional Chinese and Western Medicine), Tianjin, China.
• J Magn Reson Imaging. 2020 Nov 1; 52 (5): 1557-1566.

BackgroundNuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).PurposeTo develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.Study TypeRetrospective.PopulationIn all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.Field Strength/SequencePretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T2 WI, contrast-enhanced T1 WI, and diffusion weighted imaging.AssessmentThree prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.Statistical TestsThe least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.ResultsThe radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).Data ConclusionMultiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.Level Of Evidence3 TECHNICAL EFFICACY STAGE: 2.© 2020 International Society for Magnetic Resonance in Medicine.

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