Prognostic Nomograms to Predict Overall Survival and Cancer-Specific Survival of Patients With Pancreatic Neuroendocrine Tumors: A Population-Based Study

• Published in: Pancreas Vol. 50; no. 3; pp. 414 - 422
• Main Authors: Song, Zuoli, Wang, Sumei, Wu, Yujing, Zhang, Jinjuan, Liu, Shuye
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 01-03-2021
• Subjects: Aged; Cohort Studies; Female; Humans; Kaplan-Meier Estimate; Male; Middle Aged; Multivariate Analysis; Neuroendocrine Tumors - mortality; Neuroendocrine Tumors - pathology; Nomograms; Pancreatic Neoplasms - mortality; Pancreatic Neoplasms - pathology; Population Surveillance - methods; Prognosis; Reproducibility of Results; Survival Rate
• ISSN: 0885-3177; 1536-4828
• DOI: 10.1097/MPA.0000000000001779

The objective of this research was to construct and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) in patients with pancreatic neuroendocrine tumors (pNETs).
We extracted 3787 patients with pNETs from the Surveillance, Epidemiology and End Results database. Nomograms for estimating 3- and 5-year OS and CSS were first established. Then, we used Harrell's Concordance Index, calibration plots, and the area under receiver operating characteristic curve to evaluate the nomograms. The Kaplan-Meier curve was plotted to evaluate the different survival outcomes.
In the multivariate analysis, age, grade, functional status, American Joint Committee on Cancer stage, and surgery were associated with OS and CSS. The established nomograms had good discriminative ability, with a Harrell's Concordance Index of 0.830 for OS and 0.855 for CSS. The calibration plots also revealed good agreement. The area under receiver operating characteristic curve values of the nomograms predicting 3- and 5-year OS and CSS rates were 0.836, 0.816 and 0.859, 0.841, respectively. In addition, Kaplan-Meier curve indicated that patients with higher risk had worse survival outcomes.
We have proposed and validated the nomograms predicting OS and CSS of pNETs. They can be convenient individualized tools to facilitate clinical decision making.