Related Experiment Video
Updated: Jul 10, 2025
A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
Published on: February 2, 2024
Performance of eight predictive models for hepatocellular carcinoma recurrence after liver transplantation: A
Ajacio Bandeira de Mello Brandão1, Santiago Rodriguez2, Cláudio Augusto Marroni1
1Graduate Program in Medicine: Hepatology, School of Medicine, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245, Centro 90050-170, Porto Alegre, RS, Brazil.
Introduction And Objectives:
Liver transplantation is the optimal treatment for patients with early hepatocellular carcinoma and cirrhosis. However, hepatocellular carcinoma recurs in approximately 15 % of individuals. This study aimed to assess the efficacy of predictive models for hepatocellular carcinoma recurrence after liver transplantation.
Patients And Methods:
This retrospective study included 381 patients with HCC and evaluated the performance of the following models: R3-AFP score, alpha-fetoprotein (AFP) model, University of California, Los Angeles (UCLA) nomogram, Pre-Model of Recurrence after Liver Transplantation (MORAL), Post-MORAL, and Combo MORAL models, Risk Estimation of Tumor Recurrence (RETREAT) model and Platelet to Lymphocyte Ratio (PLR) model.
Results:
The R3-AFP score, UCLA nomogram, AFP model, RETREAT, Combo MORAL, and Post-MORAL models exhibited comparable AUROCs, ranging from 0.785 to 0.733. The AUROCs for the R3-AFP model and AFP model were superior to those of the Pre-MORAL and PLR models. The UCLA nomogram, RETREAT score, Combo MORAL model, and Post-MORAL model performed similarly to the first two models, but were only superior to the PLR model.
Conclusions:
The R3-AFP model, UCLA nomogram, AFP model, RETREAT, Combo MORAL, and Post-MORAL models demonstrated a moderate predictive capacity for hepatocellular carcinoma recurrence following transplantation. No significant differences were observed among these models in their ability to predict recurrence.
