A streamlined predictive model for predicting the risk of recurrence after liver transplantation for hepatocellular carcinoma was constructed based on preoperative ¹⁸F-FDG PET/CT metabolic parameters and clinicopathological features.

Authors

Xianglei Kong, Sibo Zhou, Shengli Ye, Zhengye Cao, Jiaqi Wang

Published in

Frontiers in oncology. Volume 16. Pages 1832413. Epub Jul 27, 2026.

Abstract

This study aimed to combine preoperative fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters with postoperatively available clinicopathological features to construct a streamlined predictive model for recurrence risk after liver transplantation for hepatocellular carcinoma (HCC), providing a basis for individualized recurrence risk assessment and guiding diagnosis and treatment strategies.
This retrospective study included 176 HCC transplant recipients with preoperative ¹⁸F-FDG PET/CT, with a median follow-up of 12 months (range: 6-61 months). Clinicopathological and PET/CT metabolic data were collected. Univariate Cox regression screened for recurrence-related factors. After collinearity diagnosis (VIF > 10), PET parameters (coefficient of variation [COV] and total lesion glycolysis [TLG]) were manually selected and combined with Boruta-screened clinicopathological features to construct a multivariate Cox model, visualized as a nomogram. The model integrates preoperative PET parameters with postoperative pathology, serving as a posttransplant risk stratification tool rather than a purely preoperative aid, guiding postoperative surveillance intensity and adjuvant therapy planning after pathological microvascular invasion (MVI) confirmation. Model performance was assessed using area under the curve (AUC), calibration curves, and decision curve analysis.
Postoperative recurrence occurred in 80 of 176 patients (45.5%). Univariate analysis revealed that various clinicopathological factors, including PIVKA-II > 40 mAU/mL, alpha-fetoprotein (AFP) > 100 ng/mL, and tumor diameter ≥ 5 cm, as well as PET/CT metabolic parameters such as maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and TLG, were significantly associated with recurrence (all P < 0.05). Some PET parameters exhibited high collinearity (VIF > 10), and the Boruta algorithm selected five core clinicopathological variables. In multivariate analysis, positive MVI, elevated COV, and elevated TLG remained independent risk factors (all P < 0.05), and the model's concordance index (C-index) was 0.707. The nomogram could predict 1-, 2-, 4-, and 5-year recurrence-free survival (RFS) probabilities. Internal validation demonstrated AUCs of 80.0%, 83.5%, and 81.7% for predicting recurrence at 24, 48, and 60 months, respectively. At 48 months, the calibration curve closely matched the ideal diagonal (slope = 0.96, 95% CI: 0.89-1.03), and decision curve analysis confirmed significant net clinical benefit across threshold probabilities of 10% to 60%.
A simplified model using preoperative PET/CT metabolic parameters (COV, TLG) and MVI predicts HCC recurrence after liver transplantation with good discrimination, calibration, and clinical utility. It assists in precise risk assessment and individualized follow-up and is designed for postoperative surveillance rather than pre-transplant selection.

PMID:
42577329
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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