Prognostic Nomogram Based on the Metastatic Lymph Node Ratio for T(1-4)N(0-1)M(0) Pancreatic Neuroendocrine Tumors After Surgery

PURPOSE: This study aimed to investigate the prognostic significance of the metastatic lymph node ratio (LNR) in patients with pancreatic neuroendocrine tumors (pNETs) and to develop and validate nomograms to predict 5-, 7-, and 10-year overall survival (OS) and cancer-specific survival (CSS) rates for pNETs after surgical resection. METHODS: The demographics and clinicopathological information of T(1-4)N(0-1)M(0) pNET patients between 2004 and 2018 were extracted from the Surveillance, Epidemiology and End Results database. X-tile software was used to determine the best cutoff value for the LNR. Patients were randomly divided into the training and the validation groups. A Cox regression model was used in the training group to obtain independent prognostic factors to develop nomograms for predicting OS and CSS. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the nomograms. Patients were divided into four groups according to the model scores, and their survival curves were generated by the Kaplan–Meier method. RESULTS: A total of 806 patients were included in this study. The best cutoff value for the LNR was 0.16. The LNR was negatively correlated with both OS and CSS. Age, sex, marital status, primary site, grade, the LNR and radiotherapy were used to construct OS and CSS nomograms. In the training group, the C-index was 0.771 for OS and 0.778 for CSS. In the validation group, the C-index was 0.737 for OS and 0.727 for CSS. The calibration curves and AUC also indicated their good predictability. DCA demonstrated that the nomograms displayed better performance than the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition). Risk stratification indicated that patients with higher risk had a worse prognosis. CONCLUSIONS: The LNR is an independent negative prognostic factor for pNETs. The nomograms we built can accurately predict long-term survival for pNETs after surgery.