Prognostic Nomogram For Predicting Long-Term Survival In Bronchopulmonary Carcinoid Tumor Patients Receiving Resection

Background: We analyzed bronchopulmonary carcinoid tumor (BPC) patients receiving resection from the Surveillance, Epidemiology, and End Results (SEER) database to identify the predictive factors of their survival. Then, we developed and validated nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in BPC patients. Methods: BPC patients registered in the SEER database were included. They were divided into a training set and an internal validation set (7:3). BPC patients from our center were included as an external validation set. Independent prognostic factors identified by a Cox regression model in the training set were used to construct nomograms to predict survival. Discrimination and calibration plots were used to evaluate the predictive accuracy of the nomograms. The nomograms were evaluated in both the internal and the external validation datasets. Results: Age, pathological type, and N stage were identified as independent prognostic factors of OS and CSS by Cox analyses (all P<0.05). Tumor size >= 2.5 cm (P=0.045) was an independent factor for unfavorable CSS. Based on these variables, nomograms were constructed. All concordance indexes of the training set, internal validation set, and external validation set indicated that the nomograms had the preferable discriminatory ability. The calibration plots for predictions of the 1-, 3-, and 5-year OS and CSS were in excellent agreement. Conclusions: Age, pathological type, N stage, and tumor size were independent predictive factors of prognosis in BPC patients receiving resection. These nomograms could serve as effective and accurate tools for the prognostic evaluation of patients with BPCs.