# Prognostic Nomograms for Patients With NF‐Pan‐NET After Pancreatectomy: A Retrospective Analysis Based on SEER Database

## TL;DR

This study creates accurate models to predict survival outcomes for patients with nonfunctional pancreatic neuroendocrine tumors after surgery, using data from a large database.

## Contribution

The study introduces novel nomograms and risk stratification systems that outperform existing staging systems for predicting patient outcomes.

## Key findings

- The developed nomograms accurately predict overall and cancer-specific survival in NF-Pan-NET patients.
- Risk subgroups defined by the new models show significant survival differences compared to traditional staging systems.
- The new models offer better guidance for postoperative surveillance and treatment decisions.

## Abstract

Surgical resection is the primary treatment option for patients diagnosed with nonfunctional pancreatic neuroendocrine tumors (NF‐Pan‐NETs). However, the postoperative prognostic evaluation for NF‐Pan‐NET patients remains obscure. This study aimed to construct an efficient model to predict the prognosis of NF‐Pan‐NET patients who have received surgical resection.

NF‐Pan‐NET patients after pancreatectomy were retrieved from the SEER database for the period of 2010 to 2019. A total of 2844 patients with NF‐Pan‐NET from SEER database were included in our study. After careful screening, six clinicopathological variables including age, grade, AJCC T stage, AJCC N stage, AJCC M stage, and chemotherapy were selected to develop nomograms to predict overall survival (OS) and cancer‐specific survival (CSS) respectively of the patients.

The novel models demonstrated high accuracy and discrimination in prognosticating resected NF‐Pan‐NET through various validation methods. Furthermore, the risk subgroups classified by the newly developed risk stratification systems based on the nomograms exhibited significant differences in both OS and CSS, surpassing the efficacy of the AJCC 8th TNM staging system. Novel nomograms and corresponding risk classification systems were developed to predict OS and CSS in patients with NF‐Pan‐NET after pancreatectomy.

The models demonstrated superior performance compared to traditional staging systems, providing clinicians with more accurate and personalized guidance for postoperative surveillance and treatment.