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Correlation study of 18F-FDG PET/CT metabolic parameters, heterogeneity index, and microvascular invasion, and its nomogram potential in predicting microvascular invasion in liver cancer before liver transplantation

  • 0Graduate School, Zhejiang Chinese Medical University.
Nuclear Medicine Communications +

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June 24, 2025

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June 24, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study found that combining 18F-FDG PET/CT imaging parameters like total lesion glycolysis (TLG) and heterogeneity index (HI) with serum PIVKA-II can accurately predict microvascular invasion (MVI) in liver cancer patients before transplantation.

Area Of Science

  • Oncology
  • Radiology
  • Medical Imaging

Background

  • Hepatocellular carcinoma (HCC) is a prevalent global cancer, with a high incidence in China.
  • Microvascular invasion (MVI) is a critical factor for HCC recurrence after surgery.
  • 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) offers integrated metabolic and anatomical imaging for HCC assessment.

Purpose Of The Study

  • To evaluate the predictive capability of 18F-FDG PET/CT metabolic and heterogeneity parameters for MVI in HCC patients awaiting liver transplantation.
  • To develop a nomogram prediction model for MVI based on these imaging and clinical factors.

Main Methods

  • A retrospective analysis of 177 HCC patients (100 MVI-positive, 77 MVI-negative) who underwent liver transplantation.
  • Correlation analysis between clinical data, 18F-FDG PET/CT parameters (SUVmax, SUVmean, TLG, TLR, COV, HI), and MVI.
  • Logistic regression for identifying independent predictors and constructing a nomogram, validated with ROC and calibration curves.

Main Results

  • Significant differences in PIVKA-II, SUVmax, TLG, TLR, COV, and HI were observed between MVI-positive and MVI-negative groups.
  • PIVKA-II, TLG, HI, and TLR were identified as independent predictors of MVI.
  • The combined nomogram model achieved an AUC of 0.815, demonstrating superior predictive performance compared to individual parameters.

Conclusions

  • Integrating 18F-FDG PET/CT parameters (TLG, HI, TLR) with serum PIVKA-II provides a robust method for preoperative MVI prediction in HCC.
  • The validated nomogram model offers a reliable tool for clinical decision-making in liver transplantation candidates.
  • This approach enhances the assessment of MVI risk, potentially improving patient outcomes.

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