A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study

Abstract

Background: Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients. Methods: GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system. Results: From 2004 to 2015, a total of 42,662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients (n=7,564) were randomly divided into training (3,782 patients) and testing sets (3,782 patients). In the univariate analysis, sex, age, race, tumor location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumor size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumor location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804–0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812–0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range. Conclusion: A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. We highly recommend to use our nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.