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基于人群的研究:用于预测胰腺神经内分泌肿瘤患者总生存和肿瘤特异性生存的预后列线图。

Prognostic Nomograms to Predict Overall Survival and Cancer-Specific Survival of Patients With Pancreatic Neuroendocrine Tumors: A Population-Based Study.

出版信息

Pancreas. 2021 Mar 1;50(3):414-422. doi: 10.1097/MPA.0000000000001779.

DOI:10.1097/MPA.0000000000001779
PMID:33835974
Abstract

OBJECTIVE

The objective of this research was to construct and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) in patients with pancreatic neuroendocrine tumors (pNETs).

METHODS

We extracted 3787 patients with pNETs from the Surveillance, Epidemiology and End Results database. Nomograms for estimating 3- and 5-year OS and CSS were first established. Then, we used Harrell's Concordance Index, calibration plots, and the area under receiver operating characteristic curve to evaluate the nomograms. The Kaplan-Meier curve was plotted to evaluate the different survival outcomes.

RESULTS

In the multivariate analysis, age, grade, functional status, American Joint Committee on Cancer stage, and surgery were associated with OS and CSS. The established nomograms had good discriminative ability, with a Harrell's Concordance Index of 0.830 for OS and 0.855 for CSS. The calibration plots also revealed good agreement. The area under receiver operating characteristic curve values of the nomograms predicting 3- and 5-year OS and CSS rates were 0.836, 0.816 and 0.859, 0.841, respectively. In addition, Kaplan-Meier curve indicated that patients with higher risk had worse survival outcomes.

CONCLUSIONS

We have proposed and validated the nomograms predicting OS and CSS of pNETs. They can be convenient individualized tools to facilitate clinical decision making.

摘要

目的

本研究旨在构建和验证预测胰腺神经内分泌肿瘤(pNET)患者总生存(OS)和癌症特异性生存(CSS)的预后列线图。

方法

我们从监测、流行病学和最终结果数据库中提取了 3787 名 pNET 患者。首先建立了用于估计 3 年和 5 年 OS 和 CSS 的列线图。然后,我们使用 Harrell 的一致性指数、校准图和接收者操作特征曲线下面积来评估列线图。通过绘制 Kaplan-Meier 曲线来评估不同的生存结果。

结果

在多变量分析中,年龄、分级、功能状态、美国癌症联合委员会分期和手术与 OS 和 CSS 相关。建立的列线图具有良好的判别能力,OS 的 Harrell 一致性指数为 0.830,CSS 为 0.855。校准图也显示出良好的一致性。列线图预测 3 年和 5 年 OS 和 CSS 率的受试者工作特征曲线下面积值分别为 0.836、0.816 和 0.859、0.841。此外,Kaplan-Meier 曲线表明,风险较高的患者生存结局较差。

结论

我们提出并验证了预测 pNET OS 和 CSS 的列线图。它们可以是方便的个体化工具,有助于临床决策。

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Front Oncol. 2022 Apr 27;12:899759. doi: 10.3389/fonc.2022.899759. eCollection 2022.