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A nomogram for predicting the risk of liver metastasis in non-functional neuroendocrine neoplasms: A population-based

Zhipeng Liu1, Faji Yang1, Yijie Hao1

  • 1Department of Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, PR China.

European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
|March 2, 2025
PubMed
Summary
This summary is machine-generated.

A new nomogram accurately predicts liver metastasis risk in non-functional gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). This tool aids early identification of high-risk patients for personalized treatment strategies.

Keywords:
Liver metastasisNomogram model:Risk prediction:BMI (body mass index)Non-functional gastroenteropancreatic neuroendocrine neoplasms

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Area of Science:

  • Oncology
  • Medical Diagnostics
  • Biostatistics

Background:

  • Non-functional gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are rare, with liver metastasis being a primary cause of mortality.
  • A significant unmet need exists for reliable tools to predict liver metastasis risk in GEP-NEN patients due to challenges in large cohort studies.

Purpose of the Study:

  • To develop and validate a nomogram model for predicting the risk of liver metastasis in patients with non-functional GEP-NENs.
  • To establish a clinically applicable tool for early risk stratification and personalized management.

Main Methods:

  • A retrospective cohort study of 838 patients with non-functional GEP-NENs (2009-2023).
  • Identification of independent risk factors (T stage, N stage, Ki-67 index, primary tumor site, BMI) using logistic regression.
  • Nomogram construction and performance evaluation via C-index, calibration curves, and decision curve analysis (DCA).

Main Results:

  • The nomogram achieved high predictive performance with C-indices of 0.839 (training) and 0.823 (validation).
  • Effective risk stratification was demonstrated, with significant survival differences between high-risk and low-risk groups (P < 0.0001).
  • Calibration curves confirmed strong agreement between predicted and observed outcomes.

Conclusions:

  • The developed nomogram serves as a reliable tool for predicting liver metastasis in non-functional GEP-NENs.
  • Facilitates early identification of high-risk patients, enabling personalized treatment and timely intervention.
  • Recommends multicenter validation and integration of molecular markers for enhanced clinical applicability.