Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non–Small-Cell Lung Cancer

列线图 医学 肿瘤科 肺癌 内科学 队列 一致性 TNM分期系统 癌症 癌症登记处 登台系统
作者
Wenhua Liang,Li Zhang,Gening Jiang,Qun Wang,Lunxu Liu,Deruo Liu,Zheng Wang,Zhihua Zhu,Qiuhua Deng,Xinguo Xiong,Wenlong Shao,Xiaoshun Shi,Jianxing He
出处
期刊:Journal of Clinical Oncology [Lippincott Williams & Wilkins]
卷期号:33 (8): 861-869 被引量:582
标识
DOI:10.1200/jco.2014.56.6661
摘要

A nomogram is a useful and convenient tool for individualized cancer prognoses. We sought to develop a clinical nomogram for predicting survival of patients with resected non-small-cell lung cancer (NSCLC).On the basis of data from a multi-institutional registry of 6,111 patients with resected NSCLC in China, we identified and integrated significant prognostic factors for survival to build a nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort of 2,148 patients from the International Association for the Study of Lung Cancer (IASLC) database. The predictive accuracy and discriminative ability were measured by concordance index (C-index) and risk group stratification.A total of 5,261 patients were included for analysis. Six independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The C-index of the nomogram was higher than that of the seventh edition American Joint Committee on Cancer TNM staging system for predicting OS (primary cohort, 0.71 v 0.68, respectively; P < .01; IASLC cohort, 0.67 v 0.64, respectively; P = .06). The stratification into different risk groups allowed significant distinction between survival curves within respective TNM categories.We established and validated a novel nomogram that can provide individual prediction of OS for patients with resected NSCLC. This practical prognostic model may help clinicians in decision making and design of clinical studies.
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