A model established using marital status and other factors from the Surveillance, Epidemiology, and End Results database for early-stage gastric cancer

Abstract Background Currently, the postoperative prognosis of early-stage gastric cancer (GC) is difficult to accurately predict. Therefore, this study aimed to combine the basic data, clinical indicators, and treatment information of patients to establish a predictive model for early-stage GC based on a new scoring system. Methods A total of 3647 patients with early stage GC from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. A Kaplan-Meier survival analysis was used to compare differences in prognosis between different marital status, as an innovative prognostic indicator. Univariate and multivariate analyses were used to screen available prediction factors and then build a nomogram using the Cox proportional hazard regression model. Calibration curves and C-index were used to verify the prediction model. Results The univariate analysis showed that age at diagnosis, sex, histology, stage_T and N GCs, surgery in the primary site, lymph node dissection, chemotherapy, radiation, tumor size, and marital status were significant prognostic factors of GC. The multivariate analysis revealed that age at diagnosis, sex, histology, stage_T, surgery, tumor size, and marital status were independent prognostic factors of overall survival. Both the C-index and calibration curves confirmed that the nomogram had a great predictive effect on patient prognosis in training and testing sets. Conclusion We established an accurate prediction model for the postoperative prognosis of patients with early-stage GC based on various clinical indicators and treatment information. This nomogram can effectively help patients with early-stage GC in the future.