A nomogram for cancer-specific survival of lung adenocarcinoma patients: A SEER based analysis

IF 1.4 Q3 SURGERY

Surgery open science Pub Date : 2024-10-18 DOI:10.1016/j.sopen.2024.10.003

Hong Guo Doctor of Medicine (M.D.) , Guole Nie Doctor of Medicine (M.D.) , Xin Zhao Master of Medicine (M.M.) , Jialu Liu Master of Medicine (M.M.) , Kaihua Yu Master of Medicine (M.M.) , Yulan Li Doctor of Medicine (M.D.)

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引用次数: 0

Abstract

Background

Non-small cell lung cancer (NSCLC) accounts for 85 % of lung cancer cases. Among NSCLC subtypes, lung adenocarcinoma (LUAD) stands as the most prevalent. Regrettably, LUAD continues to exhibit a notably unfavorable overall prognosis. This study's primary aim was to develop and validate prognostic tools capable of predicting the likelihood of cancer-specific survival (CSS) in patients with LUAD.

Methods

We retrospectively collected 21,099 patients diagnosed with LUAD between 2010 and 2015, and 8290 patients diagnosed between 2004 and 2009 from SEER database. The cohort of 21,099 patients served as the prognostic group for the exploration of LUAD-related prognostic risk factors. The cohort of 8290 patients was designated for external validation. We created a training set and an internal validation set in the prognostic group for the development and internal validation of CSS nomograms. CSS predictors were identified through the least absolute shrinkage and selection operator (Lasso) regression analysis. Prognostic model was constructed via Cox hazard regression analysis, presented in the form of both static and dynamic network-based nomograms.

Results

Several independent prognostic factors were incorporated into the construction of nomogram. The nomogram accurately predicted CSS at 1, 3, and 5 years, with respective AUC values of 0.769, 0.761, and 0.748 for the training group, and 0.741, 0.752, and 0.740 for the testing group. The study demonstrated a strong agreement between anticipated and actual CSS values, supported by decision curve analysis (DCA) and time-dependent calibrated curves. High-risk patients based on the nomogram exhibiting significantly lower survival rates compared to their low-risk counterparts according to Kaplan-Meier (K-M) curves. The nomogram demonstrates excellent predictive power in the external validation cohort.

Conclusions

A dependable and user-friendly nomogram has been developed, available in both static and online dynamic calculator formats, to facilitate healthcare professionals in accurately estimating the likelihood of CSS for patients diagnosed LUAD.

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肺腺癌患者癌症特异性生存期提名图：基于 SEER 的分析

背景非小细胞肺癌（NSCLC）占肺癌病例的 85%。在 NSCLC 亚型中，肺腺癌（LUAD）最为常见。令人遗憾的是，肺腺癌的总体预后仍然不容乐观。本研究的主要目的是开发并验证能够预测LUAD患者癌症特异性生存（CSS）可能性的预后工具。方法我们从SEER数据库中回顾性地收集了2010年至2015年间确诊的21099例LUAD患者，以及2004年至2009年间确诊的8290例患者。21,099例患者作为预后组，探讨与LUAD相关的预后风险因素。8290例患者队列被指定为外部验证组。我们在预后组中创建了一个训练集和一个内部验证集，用于开发和内部验证 CSS 直方图。通过最小绝对收缩和选择算子（Lasso）回归分析确定了 CSS 预测因子。通过 Cox 危险回归分析建立了预后模型，并以基于静态和动态网络的提名图形式呈现。提名图能准确预测 1、3 和 5 年后的 CSS，训练组的 AUC 值分别为 0.769、0.761 和 0.748，测试组的 AUC 值分别为 0.741、0.752 和 0.740。研究结果表明，预期 CSS 值与实际 CSS 值之间具有很高的一致性，决策曲线分析 (DCA) 和随时间变化的校准曲线也证明了这一点。根据 Kaplan-Meier (K-M) 曲线，基于提名图的高风险患者的生存率明显低于低风险患者。结论我们开发出了一种可靠且用户友好的提名图，它既有静态计算器格式，也有在线动态计算器格式，可帮助医护人员准确估计确诊为 LUAD 患者的 CSS 可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Surgery open science Surgery

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

66 days