Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer
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Hai-Fan Xiao1,2, Bai-Hua Zhang3, Xian-Zhen Liao2, Shi-Peng Yan2, Song-Lin Zhu2, Feng Zhou1 and Yi-Kai Zhou1
1State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
2The Department of Cancer Prevention, Hunan Cancer Hospital, Changsha 410006, China
3The Department of Thoracic Surgery, Hunan Cancer Hospital, Changsha 410006, China
Yi-Kai Zhou, email: [email protected]
Keywords: external validation, nomograms, non-small-cell lung cancer, small-cell lung cancer, treatment regimen
Received: March 20, 2017 Accepted: June 18, 2017 Published: August 02, 2017
Purpose: This study aimed to construct two prognostic nomograms to predict survival in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) using a novel set of clinical parameters.
Patients and Methods: Two nomograms were developed, using a retrospective analysis of 5384 NSCLC and 647 SCLC patients seen during a 10-year period at Xiang Ya Affiliated Cancer Hospital (Changsha, China). The patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to identify the prognostic factors needed to establish nomograms for the training cohort. The model was internally validated via bootstrap resampling and externally certified using the validation cohort. Predictive accuracy and discriminatory capability were estimated using concordance index (C-index), calibration curves, and risk group stratification.
Results: The largest contributor to overall survival (OS) prognosis in the NSCLC nomogram was the therapeutic regimen and diagnostic method parameters, and in the SCLC nomogram was the therapeutic regimen and health insurance plan parameters. Calibration curves for the nomogram prediction and the actual observation were in optimal agreement for the 3-year OS and acceptable agreement for the 5-year OS in both training datasets. The C-index was higher for the NSCLC cohort nomogram than for the TNM staging system (0.67 vs. 0.64, P = 0.01) and higher for the SCLC nomogram than for the clinical staging system (limited vs. extensive) (0.60 vs. 0.53, P = 0.12).
Conclusion: Treatment regimen parameter made the largest contribution to OS prognosis in both nomograms, and these nomograms might provide clinicians and patients a simple tool that improves their ability to accurately estimate survival based on individual patient parameters rather than using an averaged predefined treatment regimen.
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