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小肠神经内分泌肿瘤患者的发病率、生存率和预后列线图:一项 SEER 基于人群的研究。

Incidence, survival, and prognostic nomogram of patients with small intestinal neuroendocrine tumors: A SEER population-based study.

机构信息

Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.

出版信息

Medicine (Baltimore). 2024 Sep 13;103(37):e39616. doi: 10.1097/MD.0000000000039616.

Abstract

Small intestinal neuroendocrine tumors (SI-NETs) are a group of rare and significantly heterogeneous tumors with limited research currently available. This study aimed to investigate the incidence, survival, and prognostic factors of SI-NETs. We selected data from the surveillance, epidemiology, and end results (SEER) database between 2000 and 2019 and evaluated the incidence trend of SI-NETs during this period. We utilized the Kaplan-Meier method to examine the association between clinical variables and survival rates. Based on the multivariable Cox regression analysis results, we developed a nomogram to predict the 1-, 2-, and 3-year cancer-specific survival (CSS) of SI-NETs patients. We evaluated the consistency, accuracy, and clinical utility of the nomogram by drawing calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) curves. The incidence of SI-NETs showed an upward trend in recent years. Age, grade, T stage, M stage, and primary tumor surgery were independent risk factors for CSS in SI-NETs patients. The nomogram model based on these risk factors showed high accuracy and clinical benefit. SI-NETs are rare tumors with an increasing incidence rate. The nomogram model is expected to be an effective tool for personalized prognosis prediction in SI-NETs patients, which may benefit clinical decision-making.

摘要

小肠神经内分泌肿瘤(SI-NETs)是一组罕见且具有显著异质性的肿瘤,目前相关研究有限。本研究旨在探讨 SI-NETs 的发病率、生存率和预后因素。我们从 2000 年至 2019 年的监测、流行病学和最终结果(SEER)数据库中选择数据,并评估了这期间 SI-NETs 的发病率趋势。我们利用 Kaplan-Meier 方法来检验临床变量与生存率之间的关联。基于多变量 Cox 回归分析结果,我们开发了一个列线图来预测 SI-NETs 患者的 1 年、2 年和 3 年癌症特异性生存率(CSS)。我们通过绘制校准曲线、接收者操作特征(ROC)曲线和决策曲线分析(DCA)曲线来评估该列线图的一致性、准确性和临床实用性。近年来,SI-NETs 的发病率呈上升趋势。年龄、分级、T 分期、M 分期和原发肿瘤手术是 SI-NETs 患者 CSS 的独立危险因素。基于这些危险因素的列线图模型显示出较高的准确性和临床获益。SI-NETs 是一种罕见的肿瘤,发病率呈上升趋势。该列线图模型有望成为 SI-NETs 患者个性化预后预测的有效工具,从而可能有益于临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1311/11404879/59d094b606ca/medi-103-e39616-g001.jpg

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