A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database

Gang Lin; Kang Qi; Bing Liu; Haibo Liu; Jian Li

doi:10.21037/tlcr-19-517b

A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database

Transl Lung Cancer Res. 2021 Feb;10(2):622-635. doi: 10.21037/tlcr-19-517b.

Authors

Gang Lin¹, Kang Qi¹, Bing Liu¹, Haibo Liu¹, Jian Li¹

Affiliation

¹ Department of Thoracic Surgery, Peking University First Affiliated Hospital, Peking University, Beijing, China.

Abstract

Background: Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.

Methods: LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8^th edition) by using C-index and a decision curve analysis.

Results: Eight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the testing dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients.

Conclusions: We developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.

Keywords: Large cell lung cancer (LCLC); nomogram; prognosis; prognostic model.