Angiomyolipoma with Minimal Fat: Differentiation From Clear Cell Renal Cell Carcinoma and Papillary Renal Cell Carcinoma by Texture Analysis on CT Images

Rationale and Objectives: To retrospectively evaluate the diagnostic performance of texture analysis (TA) for the discrimination of angio-myolipoma (AML) with minimal fat, clear cell renal cell cancer (ccRCC), and papillary renal cell cancer (pRCC) on computed tomography (CT) images and to determine the scanning phase, which contains the strongest discriminative power. Materials and Methods: Patients with pathologically proved AMLs (n = 18) lacking visible macroscopic fat at CT and patients with pathologically proved ccRCCs (n = 18) and pRCCs (n = 14) were included. All patients underwent CT scan with three phases (precontrast phase [PCP], corticomedullary phase [CMP], and nephrographic phase [NP]). The selected images were analyzed and classified with TA software (MaZda). Texture classification was performed for 1) minimal fat AML versus ccRCC, 2) minimal fat AML versus pRCC, and 3) ccRCC versus pRCC. The classification results were arbitrarily divided into several levels according to the misclassification rates: excellent (misclassification rates <= 10%), good (10%< misclassification rates <= 20%), moderate (20%< misclassification rates <= 30%), fair (30%< misclassification rates <= 40%), and poor (misclassification rates >= 40%). Results: Excellent classification results (error of 0.00%-9.30%) were obtained with nonlinear discriminant analysis for all the three groups, no matter which phase was used. On comparison of the three scanning phases, we observed a trend toward better lesion classification with PCP for minimal fat AML versus ccRCC, CMP, and NP images for ccRCC versus pRCC and found similar discriminative power for minimal fat AML versus pRCC. Conclusions: TA might be a reliable quantitative method for the discrimination of minimal fat AML, ccRCC, and pRCC.