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Construction of a nomogram predicting the overall survival of patients with distantly metastatic non-small-cell lung cancer

Authors Deng JQ, Ren ZP, Wen JX, Wang B, Hou XB, Xue ZQ, Chu XY

Received 14 August 2018

Accepted for publication 9 October 2018

Published 22 November 2018 Volume 2018:10 Pages 6143—6156

DOI https://doi.org/10.2147/CMAR.S183878

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Andrew Yee

Peer reviewer comments 3

Editor who approved publication: Dr Chien-Feng Li


Jianqing Deng, Zhipeng Ren, Jiaxin Wen, Bo Wang, Xiaobin Hou, Zhiqiang Xue, Xiangyang Chu

Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, People’s Republic of China

Purpose: This study aimed to establish a nomogram to predict the overall survival (OS) of the general non-small-cell lung cancer (NSCLC) patients with distant metastasis.
Patients and methods: We investigated Surveillance, Epidemiology, and End Results database for NSCLC patients with distant metastasis diagnosed between 2010 and 2014. Statistically significant prognostic factors were identified using uni- and multivariable Cox regression analyses. A nomogram incorporating these prognostic factors was developed and evaluated by the Harrell’s concordance index (C-index), calibration plots, and risk group stratifications.
Results:
We finally included 18,209 patients for analysis. These patients were divided into two groups, 14,567 cases for the training cohort and 3,642 for the validation cohort. Marital status, sex, race, age, histology, T stage, N stage, histological differentiation, bone metastasis, brain metastasis, liver metastasis, with M1a disease, surgery of primary cancer, and chemotherapy were identified as the prognostic factors of the OS and integrated to construct the nomogram. The nomogram had a C-index of 0.704 (95% CI: 0.699–0.709) in the training set and 0.699 (95% CI: 0.689–0.709) in the validation set. The calibration curves for 1- and 2-year OS in the training and validation sets showed acceptable agreement between the predicted and observed survival. Also, the nomogram was capable of stratifying patients into different risk groups within the patients who presented with bone, liver, or brain metastasis, as well as in each T, N stage, respectively.
Conclusion: A nomogram was established and validated to predict individual prognosis for the general patients with distantly metastatic NSCLC. Global prospective data with the latest TNM classification and more comprehensive prognostic factors are needed to improve this model.

Keywords: nomogram, metastatic lung cancer, SEER, prognosis, overall survival, prediction

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