Related Experiment Video
Updated: Jun 23, 2025

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Continuous Risk Score Predicts Waitlist and Post-transplant Outcomes in Hepatocellular Carcinoma Despite Exception
Miho Akabane1, John C McVey2, Daniel J Firl3
1Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California.
Background & Aims:
Continuous risk-stratification of candidates and urgency-based prioritization have been utilized for liver transplantation (LT) in patients with non-hepatocellular carcinoma (HCC) in the United States. Instead, for patients with HCC, a dichotomous criterion with exception points is still used. This study evaluated the utility of the hazard associated with LT for HCC (HALT-HCC), an oncological continuous risk score, to stratify waitlist dropout and post-LT outcomes.
Methods:
A competing risk model was developed and validated using the UNOS database (2012-2021) through multiple policy changes. The primary outcome was to assess the discrimination ability of waitlist dropouts and LT outcomes. The study focused on the HALT-HCC score, compared with other HCC risk scores.
Results:
Among 23,858 candidates, 14,646 (59.9%) underwent LT and 5196 (21.8%) dropped out of the waitlist. Higher HALT-HCC scores correlated with increased dropout incidence and lower predicted 5-year overall survival after LT. HALT-HCC demonstrated the highest area under the curve (AUC) values for predicting dropout at various intervals post-listing (0.68 at 6 months, 0.66 at 1 year), with excellent calibration (R2 = 0.95 at 6 months, 0.88 at 1 year). Its accuracy remained stable across policy periods and locoregional therapy applications.
Conclusions:
This study highlights the predictive capability of the continuous oncological risk score to forecast waitlist dropout and post-LT outcomes in patients with HCC, independent of policy changes. The study advocates integrating continuous scoring systems like HALT-HCC in liver allocation decisions, balancing urgency, organ utility, and survival benefit.
More Related Videos
07:32Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018