Journal of the Korean Society of Radiology · 2026
Utility of Tumor Sphericity on CT for Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma.
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Song Jaein, Park Jung Jae
Аннотация
PURPOSE: To assess the utility of tumor sphericity on CT as a quantitative shape parameter for distinguishing fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC) in small renal masses (SRMs, maximum diameter ≤4 cm).
MATERIALS AND METHODS: We retrospectively evaluated 238 solitary sporadic SRMs that were pathologically confirmed as either AML or RCC and showed no visible fat on unenhanced CT. Tumor volume and sphericity were obtained through volumetric analysis using dedicated software. To distinguish fat-poor AML from RCC, clinical and imaging findings were compared, and receiver operating characteristic (ROC) curve analysis was performed. Binary logistic regression analysis was conducted to identify variables independently associated with fat-poor AML.
RESULTS: Among the 238 SRMs, 22 were diagnosed as AMLs and 216 as RCCs, comprising 173 clear cell, 25 papillary, and 18 chromophobe subtypes. Fat-poor AMLs were significantly more prevalent in female and had lower tumor volumes than RCCs (both p < 0.001). The tumor sphericity was significantly lower in fat-poor AMLs than in RCCs (0.755 ± 0.042 vs. 0.824 ± 0.026, p < 0.001). For distinguishing fat-poor AML from RCC, the area under the ROC curve (AUC) for sphericity was 0.909 (optimal cutoff, 0.801; sensitivity, 86.4%; specificity, 84.7%). Female sex, lower tumor volume, and lower sphericity were independent predictors of fat-poor AML (all p < 0.005). The AUC for the multivariate model was 0.965 (sensitivity, 90.9%; specificity, 92.6%).
CONCLUSION: Tumor sphericity serves as a quantitative shape parameter for distinguishing fat-poor AML from RCC in SRMs.
MATERIALS AND METHODS: We retrospectively evaluated 238 solitary sporadic SRMs that were pathologically confirmed as either AML or RCC and showed no visible fat on unenhanced CT. Tumor volume and sphericity were obtained through volumetric analysis using dedicated software. To distinguish fat-poor AML from RCC, clinical and imaging findings were compared, and receiver operating characteristic (ROC) curve analysis was performed. Binary logistic regression analysis was conducted to identify variables independently associated with fat-poor AML.
RESULTS: Among the 238 SRMs, 22 were diagnosed as AMLs and 216 as RCCs, comprising 173 clear cell, 25 papillary, and 18 chromophobe subtypes. Fat-poor AMLs were significantly more prevalent in female and had lower tumor volumes than RCCs (both p < 0.001). The tumor sphericity was significantly lower in fat-poor AMLs than in RCCs (0.755 ± 0.042 vs. 0.824 ± 0.026, p < 0.001). For distinguishing fat-poor AML from RCC, the area under the ROC curve (AUC) for sphericity was 0.909 (optimal cutoff, 0.801; sensitivity, 86.4%; specificity, 84.7%). Female sex, lower tumor volume, and lower sphericity were independent predictors of fat-poor AML (all p < 0.005). The AUC for the multivariate model was 0.965 (sensitivity, 90.9%; specificity, 92.6%).
CONCLUSION: Tumor sphericity serves as a quantitative shape parameter for distinguishing fat-poor AML from RCC in SRMs.