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Cited by 53Hepatocellular Carcinoma Treatment and PrognosisOrgan Transplantation Techniques and OutcomesRadiomics and Machine Learning in Medical Imaging

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach

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Ivanics, Tommy, Nelson, Walter, Patel, Madhukar S., Claasen, Marco P.A.W., Lau, Lawrence, Gorgen, Andre, Abreu, Phillipe, Goldenberg, Anna +2 more·2022·Liver Transplantation
DOI 10.1002/lt.26332
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Abstract

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

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Authors
Ivanics, T., Nelson, W., Patel, M. S., Claasen, M. P. A. W., Lau, L., Gorgen, A., Abreu, P., Goldenberg, A., Erdman, L., & Sapisochin, G.
Affiliations
  • Uppsala University (SE)
  • University of Toronto (CA)
  • The University of Texas Southwestern Medical Center (US)
  • University Health Network (CA)
  • Multi‐Organ Transplant Program, Division of General Surgery,Toronto General Hospital,University Health Network,University of Toronto,Toronto,ON,Canada
ORCID
Venue
Liver Transplantation
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2021-11-05
Volume
28
Issue
4
Pages
593-602
ISSN
  • 1527-6465 (ISSN-L)
  • 1527-6473
DOI
10.1002/lt.26332
OpenAlex
W3202957538
Cited by
53
References
53
Citations by year
2026: 4 · 2025: 9 · 2024: 8 · 2023: 15 · 2022: 14 · 2021: 2
FWCI
5.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Liver Transplantation (publishedVersion)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Topics
  • Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
  • Health Sciences › Medicine › Surgery › Organ Transplantation Techniques and Outcomes
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
hepatocellular carcinoma, recurrence risk prediction, risk calculator, alpha-fetoprotein, survival support vector machine, DeepSurv, Toronto
MeSH terms
Machine Learning, alpha-Fetoproteins, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Neoplasm Recurrence, Local, Retrospective Studies, Risk Factors, Liver Transplantation

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