Collateral vessels on preoperative enhanced computed tomography for predicting pathological grade of clear cell renal cell carcinoma: A retrospective study

Objectives: To retrospectively evaluate the association between the presence of collateral vessels and grade of clear cell renal cell carcinoma (ccRCC) and whether the presence of collateral vessels could serve as a predictor to differentiate high- and low-grade ccRCC.Materials and Methods: From May 2018 to September 2022, a total of 160 ccRCC patients with pathological diagnosis were enrolled in this study. Patients were divided into a high-grade group and a low-grade group according to World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system. The significant variables were extracted based on the univariate analyses using Student t test, Mann-Whitney U test, Chi-square test or Fisher's exact test. Multivariate logistic regression analyses were performed to determine independent factors among extracted variables. We calculated the sensitivity, specificity and their 95% confidence intervals (CI) of collateral vessels for predicting high WHO/ISUP grade to quantify its predictive performance. Furthermore, to investigate the additional predictive contribution of collateral vessels, a primary model and a control model were constructed to predict WHO/ISUP grade. The primary model included all extracted significant variables and the control model included significant variables except collateral vessels.Results: The proportion of ccRCC patients with collateral vessels was significantly larger in high-grade ccRCC than those in low-grade ccRCC (87.5 % vs. 26.8 %, P < 0.001). Multivariate logistic regression analyses showed that the presence of collateral vessels was an independent predictor for high WHO/ISUP grade (P < 0.001). The sensitivity and specificity of the presence of collateral vessels for differentiating high- and low-grade ccRCC were 87.5 % (95 % CI 0.753-0.941) and 73.2 % (95 % CI 0.643-0.806) respectively. Including collateral vessels in predictive model improves predictive performance for WHO/ISUP grade, increasing the area under the curve (AUC) value from 0.889 to 0.914.Conclusion: The presence of collateral vessels has high sensitivity and specificity for differentiating high- and low-grade ccRCC and can improve the predictive performance for high WHO/ISUP grade.