A noninvasive comprehensive model based on medium sample size had good diagnostic performance in distinguishing renal fat-poor angiomyolipoma from homogeneous clear cell renal cell carcinoma.

Abstract

Purpose: To determine the diagnostic value of a comprehensive model based on unenhanced computed tomography (CT) images for distinguishing fat-poor angiomyolipoma (fp-AML) from homogeneous clear cell renal cell carcinoma (hm-ccRCC).

Methods: We retrospectively reviewed 27 patients with fp-AML and 63 with hm-ccRCC. Demographic data and conventional CT features of the lesions were recorded (including sex, age, symptoms, lesion location, shape, boundary, unenhanced CT attenuation and so on). Whole tumor regions of interest were drawn on all slices to obtain histogram parameters (including minimum, maximum, mean, percentile, standard deviation, variance, coefficient of variation, skewness, kurtosis, and entropy) by two radiologists. Chi-square test, Mann-Whitney U test, or independent samples t-test were used to compare demographic data, CT features, and histogram parameters. Multivariate logistic regression analyses were used to screen for independent predictors distinguishing fp-AML from hm-ccRCC. Receiver operating characteristic curves were constructed to evaluate the diagnostic performances of the models.

Results: Age, sex, tumor boundary, unenhanced CT attenuation, maximum tumor diameter, and tumor volume significantly differed between patients with fp-AML and those with hm-ccRCC (P < 0.05). The minimum, mean, first percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, and Perc.99 of the Fp-AML group were higher than those of the hm-ccRCC group (P < 0.05). Coefficient of variance, skewness, and kurtosis were lower than those in the hm-ccRCC group (all P < 0.05). Age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25 were independent predictors for distinguishing fp-AML from hm-ccRCC (all P < 0.05). The comprehensive model, incorporating age, maximum tumor diameter, unenhanced CT attenuation, and Perc.25, showed the best diagnostic performance (AUC = 0.979).

Conclusion: The comprehensive model based on unenhanced CT imaging can accurately distinguish fp-AML from hm-ccRCC and may assist clinicians in tailoring precise therapy, while also helping to improve the diagnosis and management of renal tumors, leading to the selection of effective treatment options.