Prognostic model for the prediction of cancer-specific survival in elderly patients with stage I-III gastric cancer.

Elderly patients with gastric cancer (GC) exhibit unique physiological conditions and population characteristics. However, no efficient predictive tools have been developed for this patient subgroup. We extracted data on elderly patients diagnosed with stage I-III GC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, and applied Cox regression analysis to examine factors associated with cancer-specific survival (CSS). A prognostic model was developed and validated to predict CSS. We assessed the performance of the prognostic model and stratified patients based on their prognostic scores. Notably, 11 independent prognostic factors, including age, race, grade, the tumor-node-metastasis (TNM) stage, T-stage, N-stage, operation, tumor size, regional nodes, radiation, and chemotherapy, associated with CSS were identified using multivariate Cox regression. A nomogram was constructed based on these predictors. The C-index score of the nomogram was 0.802 (95% (confidence interval) [CI]: 0.7939-0.8114), which is superior to the American Joint Commission on Cancer (AJCC) TNM staging prediction ability in the training cohort (C-index: 0.589; 95% CI: 0.5780-0.6017). Based on the receiver operating characteristic (ROC) and calibration curve, the predicted value of the nomogram demonstrated a satisfactory accuracy with the actual observation value. Additionally, decision curve analysis (DCA) showed that the nomogram had a more ideal clinical net benefit than TNM staging. Survival analysis of the different risk groups confirmed the noteworthy clinical and statistical utility of the nomogram in prognosis stratification. This retrospective study reports the successful creation and validation of a nomogram for predicting CSS at 1-, 3- and 5-years in elderly patients with stage I-III GC. This nomogram critically guides personalized prognostic assessments and may contribute to clinical decision-making and consultation for postoperative survival.