Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma.

Abstract

To investigate the feasibility of using CT texture analysis (CTTA) to differentiate between low- versus high-grade urothelial carcinoma. A total of 105 patients with high-grade urothelial carcinoma (HGUC, n=106) and low-grade urothelial carcinoma (LGUC, n=18) were included in this retrospective study. Both unenhanced and enhanced CT images representing the largest cross-sectional area of the tumor were chosen for CTTA performed using TexRAD software. Comparison of texture parameters, mean gray-level intensity (Mean), standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were made for the objective. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve was calculated for texture parameters that were significantly different (P<0.05) for the purpose. Sensitivity (Se), specificity (Sp), positive predictive value, negative predictive value, and accuracy were calculated using the cut-off value of texture parameter with the highest AUC. Compared to HGUC, LGUC had significantly lower Mean (P=0.001), Entropy (P=0.002), and MPP (P<0.001) on unenhanced and enhanced images and lower SD (P=0.048) on enhanced images. There was no significant difference in skewness or kurtosis at any texture scale on unenhanced and enhanced images. A MPP<24.13 at fine texture scale on unenhanced images identified LGUC from HGUC with the highest AUC of 0.779±0.065 (Se=72.2%, Sp=84.9%, PPV=44.8%, NPV=94.7%, and accuracy=83.1%). CTTA proved to be a feasible tool for differentiating LGUC from HGUC. MPP quantified from fine texture scale on unenhanced images was the optimal diagnostic parameter for estimating histologic grade of urothelial carcinoma.