A clinical nomogram for predicting cancer-specific survival in pulmonary large-cell neuroendocrine carcinoma patients: A population-based study

Abstract

Purpose: This study was designed to construct and validate a nomogram that was available for predicting cancer-specific survival (CSS) in patients with pulmonary large-cell neuroen-docrine carcinoma (LCNEC). Patients and Methods: Using the US Surveillance, Epidemiology, and End Results (SEER) database, we identified patients pathologically diagnosed as LCNEC from 1975 to 2016. Univariate and multivariate Cox regression was conducted to assess prognostic factors of CSS. A novel nomogram model was constructed and validated by the concordance index (C-index), calibration curves and decision curve analysis (DCA). Results: A total of 624 LCNEC patients were enrolled. Five prognostic factors for CSS were identified and merged to establish nomograms. In the training and validation cohorts, calibration curves displayed the nomogram predictions are in a good agreement with the actual survival. The C-Index of the training and validation cohorts were both higher than 0.8, and the DCA results showed that the nomogram has clinical validity and utility. Conclusion: The proposed nomogram resulted in accurate CSS prognostic prediction for patients with LCNEC.