The impact of surgery in metastatic pancreatic neuroendocrine tumors: a competing risk analysis

• Published in: Endocrine Connections 2019-03, Vol.8 (3), p.239-251
• Main Authors: He, Chao-bin, Zhang, Yu, Cai, Zhi-yuan, Lin, Xiao-jun
• Format: Article
• Language: English
• Subjects: competing risk analysis; metastasis; pancreatic neuroendocrine tumor; surgery
• ISSN: 2049-3614; 2049-3614
• DOI: 10.1530/EC-18-0485

Aim The role of surgery in the treatment of metastatic pancreatic neuroendocrine tumors (PNETs) was controversial. The objectives of this study were to illustrate the impact of surgery in improving the prognosis of patients with metastatic PNETs and build nomograms to predict overall survival (OS) and cancer-specific survival (CSS) based on a large population-based cohort. Methods Patients diagnosed with metastatic PNETs between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. Nomograms for estimating OS and CSS were established based on Cox regression model and Fine and Grey’s model. The precision of the nomograms was evaluated and compared using concordance index (C-index) and the area under receiver operating characteristic (ROC) curve (AUC). Results The study cohort included 1966 patients with metastatic PNETs. It was shown that the surgery provided survival benefit for all groups of patients with metastatic PNETs. In the whole study cohort, 1-, 2- and 3-year OS and CSS were 51.5, 37.1 and 29.4% and 53.0, 38.9 and 31.1%, respectively. The established nomograms were well calibrated, and had good discriminative ability, with C-indexes of 0.773 for OS prediction and 0.774 for CSS prediction. Conclusions Patients with metastatic PNETs could benefit from surgery when the surgery tolerance was acceptable. The established nomograms could stratify patients who were categorized as tumor-node-metastasis (TNM) IV stage into groups with diverse prognoses, showing better discrimination and calibration of the established nomograms, compared with 8th TNM stage system in predicting OS and CSS for patients with metastatic PNETs.