Evidence map›Paper›PMID 40629061›Full record

ArticleCommunications medicine2025

Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model.

Zhihao Li, Itsuko Chih-Yi Chen, Leonardo Centonze, Christian T J Magyar, Woo Jin Choi, Tommy Ivanics, Grainne M O'Kane, Arndt Vogel, Lauren Erdman, Luciano De Carlis and 6 more

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Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Zhihao LiHBP & Multi-Organ Transplant Program, University Health Network, Toronto, Canada.ORCID http://orcid.org/0000-0002-5654-7043
Itsuko Chih-Yi ChenDepartment of Surgery, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
Leonardo CentonzeDepartment of General Surgery and Transplantation, Niguarda Ca' Granda Hospital, Milan, Italy.ORCID http://orcid.org/0000-0001-9371-7411
Christian T J MagyarHBP & Multi-Organ Transplant Program, University Health Network, Toronto, Canada.ORCID http://orcid.org/0000-0002-4570-949X
Woo Jin ChoiHBP & Multi-Organ Transplant Program, University Health Network, Toronto, Canada.
Tommy IvanicsHBP & Multi-Organ Transplant Program, University Health Network, Toronto, Canada.
Grainne M O'KaneDepartment of Medical Oncology, St. Vincent's University Hospital, Dublin, Republic of Ireland.ORCID http://orcid.org/0000-0002-8690-403X
Arndt VogelDivision of Gastroenterology and Hepatology, University Health Network, Toronto, Canada.
Lauren ErdmanThe Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
Luciano De CarlisDepartment of General Surgery and Transplantation, Niguarda Ca' Granda Hospital, Milan, Italy.
Jan LerutInstitut de Recherche Expérimentale et Clinique, Université catholique de Louvain, 10 Avenue Hippocrate, 1200, Brussels, Belgium.
Quirino LaiGeneral Surgery and Organ Transplantation Unit, Sapienza University of Rome, Rome, Italy.
Vatche G AgopianDivision of Liver and Pancreas Transplantation, Department of Surgery, Dumont-UCLA Transplant and Liver Cancer Centers, David Geffen School of Medicine at University of California, Los Angeles, CA, USA.
Neil MehtaDivision of Gastroenterology, Department of Medicine, University of California San Francisco, San Francisco, USA.
Chao-Long ChenDepartment of Surgery, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
Gonzalo SapisochinHBP & Multi-Organ Transplant Program, University Health Network, Toronto, Canada. Gonzalo.sapisochin@uhn.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOrgan shortages require prioritizing hepatocellular carcinoma (HCC) patients with the highest survival benefit for allografts. While traditional models like AFP, MORAL, and HALT-HCC are commonly used for recurrence risk prediction, the TRIUMPH model, which uses machine learning, has shown superior performance. This study aims to externally validate the model.

methodsThe cohort included 2844 HCC patients who underwent liver transplantation at six international centers from 2000-2022. The TRIUMPH model utilized a regularized Cox proportional hazards approach with a penalty term for coefficient adjustment. Discrimination was assessed using the c-index, and clinical utility was evaluated via decision curve analysis.

resultsThe most common liver diseases are hepatitis C (49%) and hepatitis B (27%). At listing, 84% meets the Milan criteria, and 91% are within criteria at transplant. Median model for end-stage liver disease score is 10 (IQR:8-14), alpha-fetoprotein level 8 ng/mL (IQR:4-25), and tumor size 2 cm (IQR:1.1-3.0). Living donor grafts are used in 24% of cases. Recurrence rate is 9.1% with a median time to recurrence of 17.5 months. Recurrence-free survival rates at 1/3/5 years are 95.7%/89.5%/87.7%, respectively. The TRIUMPH model achieves the highest c-index (0.71), outperforming MORAL (0.61, p = 0.049) and AFP (0.61, p = 0.04), though not significantly better than HALT-HCC (0.67, p = 0.28). TRIUMPH shows superior clinical utility up to a threshold of 0.6.

conclusionsThe TRIUMPH model demonstrates good accuracy and clinical utility in predicting post-transplant HCC recurrence. Its integration into organ allocation could improve transplantation outcomes.

Identifiers

PMID40629061
PMCPMC12238485

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.