Development and validation of a nomogram prognostic model for patients with neuroendocrine tumors of the thymus

• Main Authors: Jia‐Yu Tang, Hui‐Jiang Gao, Guo‐Dong Shi, Xiao‐Kang Guo, Wen‐Quan Yu, Hua‐Feng Wang, Yu‐Cheng Wei
• Format: Article
• Language: English
• Published: Wiley 2020-09-01
• Series: Thoracic Cancer
• Subjects: Neuroendocrine tumors; nomogram; prognosis; thymus
• Online Access: https://doi.org/10.1111/1759-7714.13556

Background The purpose of this study was to analyze the clinical characteristics and prognostic survival of patients with neuroendocrine tumors of the thymus (NETTs), and to develop and validate a nomogram model for predicting the prognosis of patients. Methods We conducted a retrospective analysis of patients with neuroendocrine tumors of the thymus in the Surveillance, Epidemiology, and End Results (SEER) database in the United States between 1988 and 2016. Cox scale risk regression analysis, the Kaplan‐Meier method and log‐rank test were used to carry out the significance test to determine the independent prognostic factors, from which a nomogram for NETTs was established. C‐index and calibration curve were used to evaluate the prediction accuracy of the model. External validation of the nomogram was performed using data from our center. Results A total of 254 patients with NETTs were collected in the SEER database. In the multivariable analysis, T stage, tumor grade, surgery, and chemotherapy were found to be independent factors affecting the prognosis of patients (all P < 0.05). A nomogram model was constructed based on these variables, and its c‐index was 0.707 (0.661–0.752). The c‐index results showed that the nomogram model had better authentication capability than the eighth edition of the tumor, node, metastasis (TNM) staging system and Masaoka‐Koga (MK) staging system. The calibration curve showed that the model could accurately predict patient prognosis. Conclusions The study established a nomogram model that predicted the overall survival rate of one‐, three‐ and five‐years, and used the survival prediction model to optimize individualized therapy and prognostic follow‐up through risk stratification.