Objective To construct and validate a Nomogram prediction model for overall survival (OS) in middle-aged and elderly patients with stage Ⅱ to Ⅲ gastric cancer.
Methods The clinical, pathological, and follow-up data of middle-aged and elderly patients with stage Ⅱ to Ⅲ gastric cancer in the Affiliated Hospital of Yangzhou University, Northern Jiangsu People's Hospital, and Yangzhou City Hospital of Traditional Chinese Medicine from March 1, 2012 to December 1, 2022 were retrospectively analyzed. Based on univariate and multivariate Cox regression analyses, the independent risk factors for OS in middle-aged and elderly patients with stage Ⅱ to Ⅲ gastric cancer were identified, and a Nomogram prediction model was further constructed and validated. The diagnostic performance of the model was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and the clinical effect of the model was assessed by decision curve analysis (DCA).
Results A total of 382 patients were included. A total of 282 cases were as training sets and 100 cases were as validation sets. Univariate and multivariate Cox regression analyses indicated that family history of gastric cancer, vascular invasion, nerve invasion, T stage, and N stage were independent risk factors for OS in middle-aged and elderly patients with stage Ⅱ to Ⅲ gastric cancer (P < 0.05). A prognostic Nomogram was constructed based on these variables, and the concordance index of the model in the training and validation sets was 0.667 (95% CI, 0.601 to 0.726) and 0.708 (95%CI, 0.622 to 0.766) respectively. The ROC curve indicated that the model had good predictive accuracy. The calibration curve showed that the predicted value of the model was in good agreement with the actual value. DCA demonstrated that the model had good clinical application and potential values.
Conclusion The Nomogram model for 1-, 3- and 5-year OS in middle-aged and elderly patients with stage Ⅱ to Ⅲ gastric cancer constructed based on real-world big data in this study has an ideal predictive effect, which can help clinicians effectively assess patients' prognosis.
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