基于监测、流行病学和最终结果（SEER）数据库及外部验证队列的转移性非小细胞肺癌动态生存列线图的开发与验证

Development and validation of a dynamic survival nomogram for metastatic non-small cell lung cancer based on the SEER database and an external validation cohort.

作者信息

Wang Qing, Wang Yansu, Wang Xinyu, Nakamura Yusuke, Hydbring Per, Yamauchi Yoshikane, Zhao Xiaojing, Cao Min

机构信息

Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Department of Radiotherapy, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.

出版信息

Transl Lung Cancer Res. 2022 Aug;11(8):1678-1691. doi: 10.21037/tlcr-22-544.

DOI:10.21037/tlcr-22-544

PMID:36090634

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9459611/

Abstract

BACKGROUND

Limited efficacy and poor prognosis are common in patients with metastatic non-small cell lung cancer (NSCLC). An accurate and useful nomogram helps the clinician predict the prognosis of the patients. However, there has been no previous report on the nomogram specially for predicting the overall survival (OS) of metastatic NSCLC patients.

METHODS

A total of 18,343 patients diagnosed with metastatic NSCLC in the Surveillance, Epidemiology, and End Results (SEER) database were included and divided into the training cohort (n=12,840) and the internal validation cohort (n=5,503), and 242 patients in Renji Hospital were additionally enrolled as the external validation cohort. Demographical, clinical, and OS data were collected. A Cox proportional hazards regression model was used to develop a nomogram based on the training cohort. To validate the nomogram, we applied C-indexes, calibration curves, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve.

RESULTS

The multivariate Cox regression model found that there were a total of 16 independent risk factors for OS of the patients (all 16 factors showed P<0.001), which were integrated into the nomogram with a C-index of 0.702 [95% confidence interval (CI): 0.684-0.720]. The nomogram also exhibited good prognostic value in the internal validation cohort (C-index =0.699, 95% CI: 0.673-0.725) and external validation cohort (C-index =0.695, 95% CI: 0.653-0.737). The ROC and Kaplan-Meier survival curve analyses demonstrated a high discriminative ability. High-risk patients had significantly less favorable OS than low-risk patients in the SEER population and external validation cohort (both P<0.001). The DCA analysis showed that the nomogram provided better prognosis prediction than the tumor-node-metastasis (TNM) staging system.

CONCLUSIONS

We constructed and validated a dynamic nomogram with 16 variables based on a large-scale population of SEER database to predict the prognosis of metastatic NSCLC patients. The nomogram is expected to provide higher predictive ability and accuracy than the TNM staging system.

摘要

背景

转移性非小细胞肺癌（NSCLC）患者中疗效有限和预后较差的情况很常见。准确且实用的列线图有助于临床医生预测患者的预后。然而，此前尚无专门用于预测转移性NSCLC患者总生存期（OS）的列线图报告。

方法

纳入监测、流行病学和最终结果（SEER）数据库中18343例诊断为转移性NSCLC的患者，分为训练队列（n = 12840）和内部验证队列（n = 5503），另外纳入上海交通大学医学院附属仁济医院的242例患者作为外部验证队列。收集人口统计学、临床和OS数据。基于训练队列，使用Cox比例风险回归模型构建列线图。为验证该列线图，我们应用了C指数、校准曲线、受试者工作特征（ROC）曲线、决策曲线分析（DCA）和Kaplan-Meier生存曲线。

结果

多变量Cox回归模型发现患者OS共有16个独立危险因素（所有16个因素均显示P < 0.001），这些因素被整合到列线图中，C指数为0.702 [95%置信区间（CI）：0.684 - 0.720]。该列线图在内部验证队列（C指数 = 0.699，95% CI：0.673 - 0.725）和外部验证队列（C指数 = 0.695，95% CI：0.653 - 0.737）中也显示出良好的预后价值。ROC和Kaplan-Meier生存曲线分析显示出较高的判别能力。在SEER人群和外部验证队列中，高危患者的OS明显低于低危患者（均P < 0.001）。DCA分析表明，该列线图比肿瘤-淋巴结-转移（TNM）分期系统能提供更好的预后预测。