Development and Validation of a Prognostic Nomogram to Predict Overall Survival of Elderly Patients with Gastric Cancer

Background Gastric cancer (GC) is one of the most common malignant tumors of digestive tract origin in China. The proportion of elderly patients with gastric cancer (GC) gradually increases as the population ages. We aimed to develop a prognostic nomogram for prediction of elderly (≥ 75 years old) GC patients in overall survival (OS). Patients and Methods Patients with GC from 2005 to 2014 were selected from the Surveillance, Epidemiology, and End Result (SEER) database and randomly assigned to development and validation sets. The variables for establishing nomogram were confirmed by univariate and multivariate Cox proportional hazard analysis based on the development set. The predictive accuracy and discriminative ability of the model was evaluated using the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves, while its clinical utility was assessed using decision curve analysis (DCA) and Kaplan-Meier curve. Results A total of 1445 patients were included in this study. The nomogram was developed including histologic grade, AJCC stage T, N, M and surgery according to the univariate and multivariate cox regression analysis, the area under the time-dependent receiver operating characteristic curve (AUC) and Occam’s Law of Razor. The C-index of the nomogram was higher than the TNM system in the training cohort (0.710 vs 0.652, p < 0.001), which was also confirmed in the validation cohort (0.701 vs 0.643, p < 0.001); and high AUCs were noted in both development and validation sets. The nomogram showed good discrimination and calibration in both development and validation sets. The DCA curves showed that the nomogram had better clinical utility compared to the AJCC stage model. In addition, participants could be divided into three disparate risk groups (low, moderate, high) by the nomogram. Conclusion This study established a prognostic nomogram that improved the performance of the AJCC staging system with incorporation of risk factors to better predict the short-term survival in elderly GC patients.