Prognostic nomogram predicts overall survival in pulmonary large cell neuroendocrine carcinoma
Autoři:
Yanqi He aff001; Han Liu aff002; Shuai Wang aff003; Yu Chen aff004
Působiště autorů:
Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
aff001; Department of Respiratory Medicine, the First Hospital of Jilin University, Changchun, China
aff002; Department of Vascular Surgery, the First Hospital of Jilin University, Changchun, China
aff003; Department of Cardiology, Hospital of The University of Electronic Science and Technology of China and Sichuan Provincial People's Hospital, Chengdu, China
aff004
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223275
Souhrn
Background
Large cell neuroendocrine carcinoma (LCNEC) is a rare and typically aggressive malignancy with poor prognosis. This study developed a nomogram model to predict the overall survival (OS) of patients with LCNEC.
Methods
LCNEC patients were identified from the Surveillance, Epidemiology, and End Results database between 2004–2014. Univariate and multivariate Cox regression models were used to determine demographic and clinicopathological features associated with OS. A nomogram model was generated to predict OS and its performance was assessed by Harrell’s concordance index (C-index), calibration plots, and subgroup analysis by risk scores.
Results
Of 3048 eligible patients with LCNEC, 2138 were randomly grouped into the training set and 910 into the validation set. Age at diagnosis, gender, tumor stage, N stage, tumor size, and surgery of primary site were independent prognostic factors of OS. C-index values of the nomogram were 0.75 (95% CI, 0.74–0.76) and 0.76 (95% CI, 0.74–0.77) in the training and validation sets, respectively. In both cohorts, the calibration plots showed good concordance between the predicted and observed OS at 3 and 5 years. Kaplan-Meier curves revealed significant differences in OS in patients stratified by nomogram-based risk score, and patients with a higher-than-median risk score had poorer OS.
Conclusion
This is the first nomogram developed and validated in a large population-based cohort for predicting OS in patients with LCNEC, and it shows favorable discrimination and calibration abilities. Use of this proposed nomogram has the potential to improve prediction of survival risk, and lead to individualized clinical decisions for LCNEC.
Klíčová slova:
Cancer detection and diagnosis – Histology – Lung and intrathoracic tumors – Prognosis – Surgical and invasive medical procedures – Surgical oncology – Secondary lung tumors
Zdroje
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PLOS One
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