ArticleDiagnostic and interventional radiology (Ankara, Turkey)2022
CT and clinical features for distinguishing endophytic clear cell renal cell carcinoma from urothelial carcinoma.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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3 citing papers in PubMed.
- Feasibility of Spectral Computed Tomography Color Overlay to Augment Tumor Visibility for Renal Cryoablation.Cardiovascular and interventional radiology · 2026Article
- Article
- CT findings and clinical characteristics in distinguishing renal urothelial carcinoma mimicking renal cell carcinoma from clear cell renal cell carcinoma.BMC urology · 2024Article
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PURPOSE We aimed to characterize the clinical and multiphase computed tomography (CT) features of the distinguishing endophytic clear cell renal cell carcinoma (ECCRCC) from endophytic renal urothelial carcinoma (ERUC). METHODS Data from 44 patients (35 men and 9 women) with ECCRCC and 21 patients (17 men and 4 women) with ERUC were retrospectively assessed. The mean patient age was 55 years (48.25- 59.50 years) and 68 years (63.00-73.00 years), respectively. Univariate and multivariate logistic regression analyses were performed to determine independent predictors for ECCRCC and to construct a predictive model that comprised clinical and CT characteristics for the differential diagnosis of ECCRCC and ERUC. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The independent predictors of ECCRCC were heterogeneous enhancement (odds ratio [OR]=0.027, P=.005), hematuria (OR for gross hematuria=53.995, P=.003; OR for microscopic hematuria=31.126, P = .027), and an infiltrative growth pattern (OR=24.301, P = .022). The AUC of the predictive model was 0.938 (P < .001, sensitivity=84.10%, specificity=95.20%), which had a better diagnostic performance than heterogeneous enhancement (AUC=0.766, P=.001, sensitivity=81.82%, specificity=71.43%), hematuria (AUC=0.786, P < .001, sensitivity=81.82%, specificity=66.67%), and infiltrative growth pattern (AUC=0.748, P=.001, sensitivity=90.48%, specificity=59.09%). CONCLUSION The independent predictors, as well as the predictive model of CT and clinical characteristics, may assist in the differential diagnosis of ECCRCC and ERUC and provide useful information for clinical decision-making.
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