Abstract
Background: Distinguishing exophytic renal urothelial carcinoma (ERUC) from exophytic renal clear-cell carcinoma (ERCCC) with collecting system invasion may be difficult as they involve similar locations and collecting system invasion.
Objective: The study aimed to characterize the clinical data and computed tomography (CT) features that can aid in differentiating ERUC from ERCCC.
Methods: Data from 17 patients with ERUC and 222 patients with ERCCC were retrospectively assessed. CT and clinical features exhibiting significant differences in t-tests/Mann-Whitney U-test and chi-square tests/Fisher’s exact tests were analyzed using receiver operating characteristic (ROC) curves. Variables with an area under the curve (AUC) <0.7 were excluded. Univariate logistic regression analysis was used to analyze the associations of CT and clinical features with ERUC or ERCCC. Variables with odds ratio (OR) values being close to 1 in univariate logistic regression were excluded from multivariate logistic regression. A predictive model was then constructed based on the predictors (p<0 in multivariate logistic regression). Differential diagnostic performance was assessed with AUC values.
Results: Multivariate logistic regression analysis identified preserving reniform contour (OR: 45.27, 95% confidence interval [CI]: 4.982–411.39) and infiltrative growth pattern (OR: 21.741, 95% CI: 1.898–249.049) as independent predictors that can be used to distinguish ERUC from ERCCC. AUC values for preserving reniform contour, infiltrative growth pattern, and Model-1 were 0.907 (95% CI: 0.817-0.998), 0.837 (95% CI: 0.729-0.946), and 0.947 (95% CI: 0.874–1), respectively.
Conclusion: The independent predictors and predictive model may play an important role in preoperative differentiation between ERUC and ERCCC.
Keywords: Exophytic, urothelial carcinoma, renal clear-cell carcinoma, collecting system invasion, computed tomography, differential diagnosis, independent predictor, predictive model.
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