To establish an effective dynamic nomogram and a novel risk classification system to predict overall survival (OS) for Renal Cell Carcinoma with venous tumor thrombus (RCC-VTT). 318 patients were enrolled and randomly divided into a training set and a validation set in a 7:3 ratio. LASSO regression analysis and multivariate Cox regression analysis were employed to identify significant prognostic factors. Based on these factors, a nomogram model was developed and evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). Survival differences were assessed using Kaplan–Meier curves and the log-rank test. Eight survival predictors were identified: Mayo Clinic Stage, Histology, N Stage, M Stage, Renal Sinus Invasion, Sarcomatoid Feature, Hemoglobin, and Estimated Glomerular Filtration Rate. The C-indexes for the training and validation sets were 0.77 (95% CI: 0.72–0.82) and 0.75 (95% CI: 0.68–0.82), respectively. The AUCs for the training and validation sets were 0.869 (95% CI: 0.805–0.933) and 0.854 (95% CI: 0.770–0.937) for the 5-yr predictions, respectively. DCA further confirmed the clinical utility of the model. Additionally, the nomogram-based classification system stratified patients into distinct risk subgroups for OS (P < 0.0001). We developed a dynamic nomogram and novel risk classification system for RCC-VTT. This tool has the potential to personalize treatment strategies.
Zhou et al. (Mon,) studied this question.