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Gastric neuroendocrine neoplasms: updated epidemiology and externally validated prognostic nomogram

Year: 2025

Type: article

Abstract: BACKGROUND: Gastric neuroendocrine neoplasms (G-NENs) are rare but increasingly diagnosed tumors with marked heterogeneity in prognosis. Existing prognostic tools, such as the American Joint Committee on Cancer (AJCC) staging system, lack integration of key clinical variables and are based on outdated datasets. Updated real-world data and individualized risk models are needed to improve prognostic accuracy and guide treatment decisions. METHODS: A retrospective analysis of 1641 G-NEN patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2018) was conducted. Independent prognostic factors were identified through univariate and multivariate Cox regression and used to construct a nomogram. The model was validated in an internal SEER cohort (n = 493) and an external cohort from a Chinese cancer center (n = 108). Predictive performance was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). RESULTS: The incidence of G-NENs rose from 0.435 to 7.033 per 1 000 000 persons over the past 46 years. Multivariate analysis identified age, sex, tumor size, grade, T stage, N stage, M stage, and surgery as independent prognostic factors. The nomogram outperformed the AJCC system, with C-index values of 0.86 (training), 0.86 (internal validation), and 0.72 (external validation). Risk stratification effectively differentiated low- and high-risk patients, and chemotherapy significantly improved survival in the high-risk group. CONCLUSIONS: The incidence of G-NENs has increased 16-fold in the past 46 years. The nomogram provides more precise survival predictions than the AJCC staging system and can effectively guide clinical decisions.

Source: International Journal of Surgery

DOI: 10.1097/js9.0000000000003637

Authors: Chongfei Zhao, Kailai Yin, Mengli Zi, Zijie Wang, Li Yuan, Xiangdong Cheng