Nomogram for Predicting Survival in Non-Metastatic Renal Cell Carcinoma

Abstract

Objectives: Renal cell carcinoma (RCC) is a prevalent and increasingly diagnosed malignancy. This study aimed to create a nomogram prognostic model for cancer-specific survival (CSS) in patients with non-metastatic primary renal cell carcinoma (nmRCC).Patients and Methods: Data from patients diagnosed with renal carcinoma (RC) between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients meeting the inclusion criteria were randomly divided into a training group (70%) and a validation group (30%). Significant independent prognostic factors were identified using univariate and multivariate Cox regression analyses in the training cohort. A nomogram was developed based on these factors to predict 1-, 3-, and 5-year CSS in nmRCC patients. The model's performance was evaluated using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), integrated discriminant improvement (IDI), and decision curve analysis (DCA). The nomogram's performance was compared with the AJCC staging system, and a risk stratification system was validated using Kaplan-Meier survival analysis.Results: The study included 26,372 patients, divided into a training set (N=18,460) and a validation set (N=7,912). Cox regression analyses in the training set identified age, marital status, tumor histology, AJCC stage, tumor size, histological grade, surgical approach, radiotherapy, and chemotherapy as independent prognostic factors. The C-index for the nomogram was 0.833 in the training set and 0.836 in the validation set. The nomogram's area under the curve (AUC) for 1-, 3-, and 5-year predictions in the training set were 0.858, 0.872, and 0.855, respectively, surpassing the AJCC staging system's AUCs. Similar results were observed in the validation set. Calibration curves demonstrated the model's accuracy, while NRI and IDI analyses indicated significant improvements over the AJCC model. DCA suggested the nomogram's potential clinical utility, and the risk stratification system effectively distinguished patients with varying survival risks.Conclusion: The developed nomogram prediction model accurately predicts 1-, 3-, and 5-year CSS in nmRCC patients, offering high accuracy and discriminatory ability. This model can aid physicians and patients in clinical decision-making and proactive risk factor monitoring.