Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma? - Wang, Xu; Song, Ge; Jiang, Haitao; Zheng, Linfeng; Pang, Peipei; Xu, Jingjing (Abdominal imaging, 2021)
Objective The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT). Materials and methods Pathologically confirmed subjects ( n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade ( n = 53) and high grade ( n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model. Results Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set ( P > 0.05). Conclusion This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.