Construction and validation of nomograms to predict overall survival and cancer-specific survival in patients with gastroesophageal junction cancer of no distant metastasis

Abstract

Abstract Background Gastroesophageal junction (GEJ) cancer is a distinctive type because of its site of incidence, our study aimed to explore the factors affecting the overall survival (OS) and cancer-specific survival (CSS) in patients with GEJ cancer of no distant metastasis and to construct nomogram model to predict the prognosis. Methods Patients with GEJ cancer of no distant metastases were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomized into two cohorts in a 7:3 ratio. Univariate and multivariate Cox regression analyses were performed to determine the OS and CSS risk factors, and nomograms were constructed utilizing these factors. The C-index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to validate the reliability of the model. Results 3,831 GEJ cancer patients without distant metastases were enrolled in the study; 2,686 of these patients were in the training cohort and 1,145 were in the validation cohort. In the training cohort, the result of the multivariate Cox proportional hazards regression model demonstrated that age, histological grading, T-stage, N-stage, and primary site surgery were independent factors for OS. As for CSS, in addition to the appeal factors, the number of tumors was also an independent influencing factor, The C-index of OS and CSS predicted by nomogram models were 0.681 (95% CI: 0.668–0.694) and 0.707 (95% CI: 0.693–0.721). Based on the result of calibration curve and ROC, the nomogram model was able to predict the prognosis of GEJ cancer without distant metastases with accuracy. Up to a certain point, DCA showed a good net advantage of the model in predicting patient survival over a wide range. Conclusion The nomogram prediction model had been validated to have good predictive and clinical application value, which can accurately predict survival rates and inform individualized treatment decisions in patients with GEJ cancer without distant metastases.