MR Imaging Radiomics Analyses for Prediction of High Grade Histology and Necrosis in Clear Cell Renal Cell Carcinoma: Preliminary Experience - Dwivedi, Durgesh K. (Clin Genitourin Cancer, 2020)
INTRODUCTION: Percutaneous renal mass biopsies can accurately diagnose clear cell renal cell carcinoma (ccRCC), however their reliability to determine nuclear grade in larger, heterogeneous tumors is limited. To assess the ability of radiomics analyses of magnetic resonance imaging (MRI) to predict high grade (HG) histology in ccRCC. PATIENTS AND METHODS: 70 patients with a renal mass underwent 3T MRI before surgery between 8/2012 and 8/2017. Tumor length, first order statistics, and Haralick texture features were calculated on T2-weighted (T2W) and dynamic contrast enhanced (DCE) MRI after manual tumor segmentation. After variable clustering algorithm was applied, tumor length, wash-out and all cluster features were evaluated univariably by receiver operating characteristic (ROC) curves. Three logistic regression models were constructed to assess predictability of HG ccRCC and cross-validated. RESULTS: At univariate analysis, area under the curve (AUC) of length, DCE texture cluster 1 and cluster 3 for diagnosis of HG ccRCC were 0.7 (95% CI, 0.58-0.82, false discovery rate (FDR) p-value = 0.008), 0.72 (95% confidence interval (CI), 0.59-0.84, FDR p-value = 0.004) and 0.75 (95% CI, 0.63-0.87, FDR p-value = 0.0009), respectively. At multivariable analysis, AUC for model 1 (tumor length only), model 2 (length + DCE clusters 3 and 4), and model 3 (DCE cluster 1 and 3) for diagnosis of HG ccRCC were 0.67 (95% CI, 0.54-0.79), 0.82 (95% CI, 0.71-0.92), and 0.81 (95% CI, 0.70-0.91), respectively. CONCLUSION: Radiomics analysis of MRI images was superior to tumor size for the prediction of high-grade histology in ccRCC in our cohort.