Prognostic nomogram predicts overall survival in pulmonary large cell neuroendocrine carcinoma

English version

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
prolekare.web.journal.doi_sk: 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 – Surgical and invasive medical procedures – Lung and intrathoracic tumors – Surgical oncology – Prognosis – Histology – Secondary lung tumors

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