E-ISSN: 1305-3612
CT and clinical features for distinguishing endophytic clear cell renal cell carcinoma from urothelial carcinoma
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Abdominal Imaging - Original Article
VOLUME: 28 ISSUE: 5
P: 410-417
September 2022

CT and clinical features for distinguishing endophytic clear cell renal cell carcinoma from urothelial carcinoma

Diagn Interv Radiol 2022;28(5):410-417
DOI: 10.5152/dir.2022.211248
Xin Chen 1
Yidi Chen 2
Yiwu Leia 1
Fuling Huanga 1
Cheng Tanga 1
Liling Longa 1
1. Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
2. Department of Radiology, West China Hospital, Sichuan University, Sichuan, China
No information available.
No information available
Received Date: 25.12.2022
Accepted Date: 05.10.2022
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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.