A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study
Joint Authors
Wang, Haiyong
Shang, Xiaoling
Yu, Haining
Lin, Jiamao
Li, Zhenxiang
Zhao, Chenglong
Sun, Jian
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Objective.
In this study, we aimed to establish a novel nomogram model which was better than the current American Joint Committee on Cancer (AJCC) stage to predict survival for non-small-cell lung cancer (NSCLC) patients who underwent surgery.
Patients and Methods.
19617 patients with initially diagnosed NSCLC were screened from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015.
These patients were randomly divided into two groups including the training cohort and the validation cohort.
The Cox proportional hazard model was used to analyze the influence of different variables on overall survival (OS).
Then, using R software version 3.4.3, we constructed a nomogram and a risk classification system combined with some clinical parameters.
We visualized the regression equation by nomogram after obtaining the regression coefficient in multivariate analysis.
The concordance index (C-index) and calibration curve were used to perform the validation of nomogram.
Receiver operating characteristic (ROC) curves were used to evaluate the clinical utility of the nomogram.
Results.
Univariate and multivariate analyses demonstrated that seven factors including age, sex, stage, histology, surgery, and positive lymph nodes (all, P<0.001) were independent predictors of OS.
Among them, stage (C-index = 0.615), positive lymph nodes (C-index = 0.574), histology (C-index = 0.566), age (C-index = 0.563), and sex (C-index = 0.562) had a relatively strong ability to predict the OS.
Based on these factors, we established and validated the predictive model by nomogram.
The calibration curves showed good consistency between the actual OS and predicted OS.
And the decision curves showed great clinical usefulness of the nomogram.
Then, we built a risk classification system and divided NSCLC patients into two groups including high-risk group and low-risk group.
The Kaplan–Meier curves revealed that OS in the two groups was accurately differentiated in the training cohort (P<0.001).
And then, we validated this result in the validation cohort which also showed that patients in the high-risk group had worse survival than those in the low-risk group.
Conclusion.
The results proved that the nomogram model had better performance to predict survival for NSCLC patients who underwent surgery than AJCC stage.
These tools may be helpful for clinicians to evaluate prognostic indicators of patients undergoing operation.
American Psychological Association (APA)
Shang, Xiaoling& Yu, Haining& Lin, Jiamao& Li, Zhenxiang& Zhao, Chenglong& Sun, Jian…[et al.]. 2020. A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study. Journal of Oncology،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1189091
Modern Language Association (MLA)
Shang, Xiaoling…[et al.]. A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study. Journal of Oncology No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1189091
American Medical Association (AMA)
Shang, Xiaoling& Yu, Haining& Lin, Jiamao& Li, Zhenxiang& Zhao, Chenglong& Sun, Jian…[et al.]. A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study. Journal of Oncology. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1189091
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189091