Background:The applicability of the nomogram approach to the prognosis of CCRC patients following radical nephrectomy has yet been established or tested. In this study, we utilized data from a large publicly available US cancer database to obtain a large sample size of data on CCRC patients, and used this dataset to evaluate the independent prognostic risk factors for cause-specific survival (CSS) in patients with non-metastatic CCRC. The data were used to construct a new novel prognostic nomogram for CCRC following radical nephrectomy in order to provide a clinically useful predictive tool for estimating a patient's survival probability.This study aimed to establish a novel prognostic nomogram for patients with non-metastatic Chromophobe Cell Renal Carcinoma (CCRC) after radical nephrectomy.Methods:A total of 1040 CCRC patients with non-metastatic cancer who had undergone radical nephrectomy, were identified in SEER (2004 through 2014). A novel nomogram was constructed based on the data and variables associated with cause specific survival time were included in the model using the Cox Proportional-Hazards Model. The nomogram was cross-validated against a subset of the data (n = 520 patients) from 9 randomly selected cancer registries in SEER, by calculation of Harell’s Concordance index (C-index) and the calibration curve for the time-related probability of survival. Results:Multivariate analysis of the training dataset (n = 1040 patients) revealed age at diagnosis, tumor size and tumor grade as independent factors associated with cause specific survival (CSS), and these were selected for inclusion in the nomogram. The calibration curve for the time-limited probability of survival showed good agreement between the predictions of the nomogram and actual observation. The C-index of the nomogram for predicting survival was 0.81 (95% CI 0.75 - 0.87), which was statistically higher than the C-index values produced by the 6/7th AJCC systems (0.75, 95% CI 0.67 - 0.83). The nomogram prediction of survival based on the validation dataset was also superior to that of the 6/7th AJCC staging systems (C-index 0.83 vs. 0.71; P = 0.038 ). Conclusion:The nomogram prediction model we built has a higher C index value, and has better sensitivity and predictive value. A prospective study with a larger sample is needed to verify our findings.