Comprehensive survival nomograms for locally advanced gastric cancer: a large population-based real-world study.

Abstract

This study aimed to construct and verify nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for locally advanced gastric cancer (LAGC) based on a therapeutic selection, demographic factors, and pathological features. The data used for the analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed based on the Cox regression model. The entire cohort comprised 21,757 patients with histologically confirmed LAGC, and was randomly distributed into training and verification groups at a ratio of 2:1 for building the prognostic predictive model. According to the multivariate analysis, 13 variables [i.e., age, marital status, race, tumor location, pathological grade, histological type, T and N stage, surgery, radiotherapy, chemotherapy, tumor size, and regional nodes examined (RNE)] were confirmed as independent predictors for both OS and CSS. All of the significant variables were used to create the nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), the C-index, and calibration curves were applied to identify the discriminating superiority of the nomograms. The nomograms for OS and CSS in LAGC were built and validated based on the therapeutic selection and pathological and demographic variables using a national database. This study aims at helping clinicians make better clinical decisions and encouraging patients receive treatment actively.