Development and validation of nomogram to predict lymph node metastasis preoperatively in patients with pancreatic neuroendocrine tumor

Background: Routine lymphadenectomy in pancreatic neuroendocrine tumors (pNETs) is debated. There lacks accurate model to predict lymph node metastasis (LNM) preoperatively in pNETs. Therefore, this study aimed at developing a nomogram in predicting LNM in pNETs preoperatively. Methods: Patients undergoing surgery from Surveillance, Epidemiology, and End Results (SEER) database (design cohort, n = 2742) and First Affiliated Hospital of Sun Yat-sen University (validation cohort, n = 136) were enrolled. Nomogram was developed based on risk factors determined by logistic regression analyses. The performance of nomogram was evaluated by area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis. Results: In design cohort, 915 of 2742 patients had LNM. Tumor in the pancreatic head, T stage, and tumor size were significantly associated with LNM (all p < 0.05). Prediction of nomogram was accurate with AUC of 0.776 in design cohort and 0.622 in validation cohort. The nomogram showed good agreement between prediction and observation in the design and validation cohort. Based on nomogram-predicted risk, patients with higher risk of LNM had worse overall survival over patients with lower risk of LNM (log-rank p < 0.001). Conclusions: The novel nomogram could accurately predict LNM in pNET preoperatively. For patients with high risk of LNM, lymphadenectomy was recommended.