Prognostic value of systemic immune-inflammation index in non-metastatic clear cell renal cell carcinoma with tumor thrombus

Abstract

This study aims to determine the prognostic value of SII for non-metastatic clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT). We retrospectively collected and analyzed 328 non-metastatic ccRCC patients with VTT who underwent radical nephrectomy and thrombectomy from 3 tertiary centers in China between 2011 to 2021. Kaplan-Meier analyses and Cox proportional hazard analyses were used to determine its prognostic value for overall survival (OS) and disease free survival (DFS). The Harrell concordance index (C-index), receiver operating characteristic curve (ROC) analysis, and decision curve analysis (DCA) were used to evaluate its role in the improvement of prognostic accuracy of the existing models. Nomogram models containing the SII were then developed and evaluated by R. Patients were divided into low-SII and high-SII groups based on the SII optimal cut-off value 912 calculated by the Youden index in all patients. Higher SII was correlated with more symptoms, longer surgical time, higher WHO/ISUP grade, and longer tumor diameter. Kaplan-Meier analyses revealed significant differences in OS and DFS between two groups. Multivariate analyses revealed that SII was an independent prognostic factor for OS (HR:2.220, p=0.002) and DFS (HR:1.846, p=0.002). Compared with other indicators, SII had a superior accuracy (c-index=0.630 for OS and 0.595 for DFS). It also improved the performance of models for predicting OS and DFS (all p <0.01). Based on the results of LASSO Cox regression analysis, we constructed a nomogram to predict OS and it performed well on both the training cohort (AUC=0.805) and the validation cohort (AUC=0.795). Risk stratification based on nomogram can distinguish patients with different risks (all p <0.001). Preoperative SII is an independent predictive factor for OS and DFS of non-metastatic ccRCC patients with VTT. It can be used to improve the performance of current risk models.