Prognostic nomogram for predicting long-term cancer-specific survival in patients with lung carcinoid tumors

Background: Lung carcinoid is a rare malignant tumor with poor survival. The current study established a nomogram model for predicting cancer-specific survival (CSS) in patients with lung carcinoid tumors. Methods: A total of 1956 patients diagnosed with primary lung carcinoid tumors were extracted from the Surveillance, Epidemiology, and End Results database. The specific predictors of CSS for lung carcinoid tumors were identified and integrated to build a nomogram. Validation of the nomogram was conducted using parameters concordance index (C-index), calibration plots, decision curve analyses (DCAs), and the receiver operating characteristic (ROC) curve. Results: Age at diagnosis, grade, histological type, N stage, M stage, surgery of the primary site, radiation of the primary site, and tumor size were independent prognostic factors of CSS. High discriminative accuracy of the nomogram model was shown in the training cohort (C-index = 0.873), which was also testified in the internal validation cohort (C-index = 0.861). In both cohorts, the calibration plots showed good concordance between the predicted and observed CSS at 3, 5, and 10 years. The DCA showed great potential for clinical application. The ROC curve showed superior survival predictive ability of the nomogram model (area under the curve = 0.868). Conclusions: We developed a practical nomogram that provided independent predictions of CSS for patients with lung carcinoid tumors. This nomogram may have the potential to assist clinicians in prognostic evaluations or developing individualized therapies for patients with this neoplasm.