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A nomogram to predict prognosis in patients undergoing sublobar resection for stage IA non-small-cell lung cancer

Authors Yang H, Li X, Shi J, Fu H, Yang H, Liang Z, Xiong H, Wang H

Received 3 August 2018

Accepted for publication 12 October 2018

Published 4 December 2018 Volume 2018:10 Pages 6611—6626

DOI https://doi.org/10.2147/CMAR.S182458

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Amy Norman

Peer reviewer comments 3

Editor who approved publication: Dr Rituraj Purohit


Heli Yang,1 Xiangdong Li,2 Jialun Shi,2 Hao Fu,1 Hao Yang,2 Zhen Liang,1 Hongchao Xiong,1 Hui Wang3

1Department of Thoracic Surgery I, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Haidian, Beijing, People’s Republic of China; 2Department of Cardiothoracic Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, People’s Republic of China; 3Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People’s Republic of China

Introduction: This study aimed to develop a practical nomogram to predict prognosis in patients who are undergoing sublobar resection for stage IA non-small-cell lung cancer (NSCLC). Data from Surveillance, Epidemiology, and End Results (SEER) databases were used to construct the nomogram.
Methods: Data from patients undergoing sublobar resection for stage IA NSCLC diagnosed between 2004 and 2014 were extracted from the SEER database. Factors that may predict the outcome were identified using the Kaplan–Meier method and the Cox proportional-hazards model. A nomogram was constructed to predict the 3- and 5-year overall survival (OS) and lung cancer-specific survival (LCSS) rates of these patients. The predictive accuracy of the nomogram was measured using the concordance index (C-index) and calibration curve.
Results: A total of 4,866 patients were selected for this study. Using univariate and multivariate analyses, eight independent prognostic factors associated with OS were identified, including sex (P<0.001), age (P<0.001), race (P=0.043), marital status (P=0.009), pathology (P=0.004), differentiation (P<0.001), tumor size (P<0.001), and surgery (P=0.001), and five independent prognostic factors associated with LCSS were also identified, including sex (P<0.001), age (P<0.001), differentiation (P<0.001), tumor size (P<0.001), and surgery (P=0.011). A nomogram was established based on these results and validated using the internal bootstrap resampling method. The C-index of the established nomogram for OS and LCSS was 0.649 (95% CI: 0.635–0.663) and 0.640 (95% CI: 0.622–0.658), respectively. The calibration curves for probability of 3-, and 5-year OS and LCSS rates demonstrated good agreement between the nomogram prediction and actual observation.
Conclusion: This innovative nomogram delivered a relatively accurate individual prognostic prediction for patients undergoing sublobar resection for stage IA NSCLC.

Keywords:
sublobar resection, stage IA, non-small-cell lung cancer, prognostic factors, nomogram

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