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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer
Wenhua Liang1, Li Zhang1, Gening Jiang1
1Wenhua Liang, Qiuhua Deng, Xinguo Xiong, Wenlong Shao, Xiaoshun Shi, and Jianxing He, The First Affiliated Hospital of Guangzhou Medical University; Wenhua Liang, Qiuhua Deng, Xinguo Xiong, Wenlong Shao, Xiaoshun Shi, and Jianxing He, Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease; Wenhua Liang, Li Zhang, and Zhihua Zhu, Cancer Center of Sun Yat-Sen University, Guangzhou; Gening Jiang, Shanghai Pulmonary Hospital of Tongji University; Qun Wang, Shanghai Zhongshan Hospital of Fudan University, Shanghai; Lunxu Liu, West China Hospital, Sichuan University, Chengdu; Deruo Liu, China and Japan Friendship Hospital, Beijing; and Zheng Wang, Shenzhen People's Hospital, Shenzhen, People's Republic of China.
Purpose:
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).
Patients And Methods:
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.
Results:
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.
Conclusion:
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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