ArticleQuantitative imaging in medicine and surgery2022
Role of computed tomography features in the differential diagnosis of chromophobe renal cell carcinoma from oncocytoma and angiomyolipoma without visible fat.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Usefulness of the internal-to-external circle area ratio in contrast-enhanced CT to differentiate small (< 3 cm) fat-poor angiomyolipoma from renal cell carcinoma.BMC medical imaging · 2025Article
- Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- Potential of quantitative T1 mapping to serve as a novel prognostic predictor of clear cell renal cell carcinoma after nephrectomy.Quantitative imaging in medicine and surgery · 2024Article
- The clinical significance of computed tomography texture features of renal cell carcinoma in predicting pathological T1-3 staging.Quantitative imaging in medicine and surgery · 2023Article
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6 authors at 3 institutions in 2 countries.
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Abstract
Background: Chromophobe renal cell carcinoma (chRCC) is often confused with oncocytoma and angiomyolipoma without visible fat (AML.wovf). The aim of this study was to determine computed tomography (CT) features predictive of chRCC to distinguish it from oncocytoma and AML.wovf. Methods: This multicenter study enrolled 38 patients with chRCC, 32 with oncocytoma, and 43 with AML.wovf of the kidney. The clinical and imaging features of all cases were reviewed retrospectively, and associations between the features and histopathology were analyzed using univariate analysis, followed by multinomial logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to evaluate logistic regression models and determine optimal cut-off values for numeric data. Results: Univariate analysis revealed significant differences between chRCC and oncocytoma in tumor ratios of lesion to renal cortex net enhancement (RLRCNE) on both corticomedullary and nephrographic phase images (P<0.001 for both) and calcification (P=0.035). On multinomial logistic regression analysis, only corticomedullary RLRCNE remained an independent predictor for the differential diagnosis of chRCC from oncocytoma (P<0.001), with an optimal cut-off value of 0.53. Comparing chRCC and AML.wovf, univariate analysis revealed significant differences in age (P=0.003), segmental enhancement inversion (SEI) (P=0.006), corticomedullary RLRCNE (P<0.001), unenhanced ratio of lesion to renal cortex attenuation (RLRCA; P<0.001), size (P<0.001), enhancement pattern over time (P=0.017), angle (P=0.014), and central scar (P<0.001). Only unenhanced RLRCA (P<0.001), size (P=0.003), and enhancement pattern over time (P=0.002) remained as independent predictors on multinomial logistic regression analysis, with optimal cut-off values of 1.13 and 30.9 mm for RLRCA and size, respectively. On ROC curve analysis of the logistic regression models, the areas under curve (AUC) were 0.888 and 0.963 for chRCC versus oncocytoma and AML.wovf, respectively. Conclusions: Corticomedullary RLRCNE on CT images was an independent predictor for the differential diagnosis of chRCC from oncocytoma. Unenhanced RLRCA, size, and enhancement pattern over time on CT had predictive value for discriminating chRCC from AML.wovf.
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