淋巴结比率（LNR）被证明在许多肿瘤的预后中发挥着至关重要的作用。然而，有关LNR对胃神经内分泌肿瘤（NEN）术后患者预后价值的研究有限。探讨LNR对术后胃神经内分泌肿瘤（NEN）患者的预后价值，并结合LNR建立预后模型。共监测286例患者。 、流行病学和最终结果数据库按8:2的比例分为训练集和验证集。来自中国苏州大学第一附属医院的 92 名患者被指定为测试集。采用Cox回归分析探讨胃NEN患者LNR与疾病特异性生存（DSS）之间的关系。应用随机生存森林（RSF）算法和Cox比例风险（CoxPH）分析分别建立预测DSS的模型，并与第8版美国癌症联合委员会（AJCC）肿瘤-淋巴结-转移（TNM）分期进行比较。分析表明，LNR是胃NEN术后患者的独立预后因素，较高的LNR伴随着较高的死亡风险。 RSF 模型在预测 DSS 方面表现出最佳性能，测试集中的 C 指数为 0.769 [95% 置信区间 (CI)：0.691-0.846] 优于 CoxPH 模型（0.744，95%CI：0.665-0.822）以及第 8 版 AJCC TNM 分期（0.723，95%CI：0.613-0.833）。校准曲线和决策曲线分析（DCA）表明RSF模型具有良好的校准和临床效益。此外，RSF模型可以有效地进行危险分层和个体预后预测。术后胃NEN患者的LNR较高表明DSS较低。 RSF模型在测试集中优于CoxPH模型和第8版AJCC TNM分期，在临床实践中显示出潜力。©作者2024。百事登出版集团有限公司出版。保留所有权利。

Lymph node ratio (LNR) was demonstrated to play a crucial role in the prognosis of many tumors. However, research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm (NEN) patients was limited.To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.A total of 286 patients from the Surveillance, Epidemiology, and End Results database were divided into the training set and validation set at a ratio of 8:2. 92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set. Cox regression analysis was used to explore the relationship between LNR and disease-specific survival (DSS) of gastric NEN patients. Random survival forest (RSF) algorithm and Cox proportional hazards (CoxPH) analysis were applied to develop models to predict DSS respectively, and compared with the 8th edition American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging.Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death. The RSF model exhibited the best performance in predicting DSS, with the C-index in the test set being 0.769 [95% confidence interval (CI): 0.691-0.846] outperforming the CoxPH model (0.744, 95%CI: 0.665-0.822) and the 8th edition AJCC TNM staging (0.723, 95%CI: 0.613-0.833). The calibration curves and decision curve analysis (DCA) demonstrated the RSF model had good calibration and clinical benefits. Furthermore, the RSF model could perform risk stratification and individual prognosis prediction effectively.A higher LNR indicated a lower DSS in postoperative gastric NEN patients. The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set, showing potential in clinical practice.©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.