A novel nomogram for predicting survival of patients with poorly differentiated gastric adenocarcinoma

BACKGROUND: Poorly differentiated gastric adenocarcinoma (PDGA) is a common adenocarcinoma with less glandular structure in gastric cancer. To date, the factors affecting its prognosis remain unclear. In this study, we establish a novel prognostic nomogram for PDGA. METHODS: We screened the Surveillance, Epidemiology, and End Results (SEER) database and downloaded data from PDGA patients who underwent surgery between 2010 and 2015. We explored their clinicopathological characteristics and important prognostic factors such as overall survival (OS), using univariate and multivariate Cox proportional hazards regression analyses, then constructed a prognostic nomogram using the resulting significant variables to predict the OS. We verified performance of the nomogram externally using a separate Chinese set, and further compared its ability as well as the 8(th) edition of the American Joint Committee on Cancer (AJCC) staging system to predict prognosis. RESULTS: A total of 3,887 patients in the SEER database met our inclusion criteria and were therefore included in the analysis. Multivariate analysis showed that age, sex, tumor size, prime site of tumor, T stage, N stage, and M stage were all independent prognostic factors for PDGA. These factors allowed successful establishment of a nomogram model with high predictive power, based on external verification using a Chinese set comprising 632 PDGA patients. The nomogram showed a better discrimination advantage than the 8(th) edition of the AJCC staging system in predicting OS (C-index of nomogram vs. AJCC staging for SEER set: 0.707 vs. 0.663; Chinese set: 0.788 vs. 0.713). CONCLUSIONS: The nomogram, established herein, was more accurate in predicting the 1-, 3-, and 5-year OS of PDGA patients than the traditional AJCC TNA staging system. Successful establishment of a PDGA prognostic nomogram is a further step towards individualized and precise treatment of gastric cancer.