A novel nomogram and risk classification system for predicting overall survival in mucinous gastric adenocarcinoma：a population-based study

Abstract

Abstract Purpose Gastric mucinous adenocarcinoma (MGC) is an uncommon and special malignant tumor. There is currently no research has been conducted on MGC patient survival risk factors. Hence, the objective of this study was to develop and validate a prognostic prediction model for predicting survival outcomes in MGC patients. Methods A total of 586 patients diagnosed with MGC between 2004 and 2015 were extracted from the SEER database. Using univariate and multivariate Cox proportional regression models, independent prognostic factors for overall survival (OS) in MGC patients were identified. Based on these factors, a prognostic model for MGC was established. The prediction accuracy and reliability of the novel model were evaluated utilizing concordance-index (C-index), calibration curve, receiver-operator-curve (ROC) and clinicaldecision-curve-analysis (DCA). Results Multivariate Cox regression analysis revealed that age at diagnosis, marital status, pT stage, M, surgery status, radiotherapy and chemotherapy are independent influencing factors of OS in patients with MGC. The model C-index and the area-under-the-curve (AUC) values indicted a high level of differentiation. The calibration curve also demonstrates that the predicted values and actual observed values are in good correlation. Additionally, the DCA curve demonstrates that the nomogram is vastly superior to the 8th edition of the TNM staging system, has superior predictive performance, and is more clinically applicable. Conclusions The prediction model constructed and validated for the first time has exceptional prediction performance, can accurately estimate the OS of MGC patients, has a certain reference value for clinical patients, and is beneficial to the management of clinical patients.