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

T. Ivanics, W. Nelson, M. S. Patel, M. P. A. W. Claasen, L. Lau, A. Gorgen, P. Abreu, A. Goldenberg, L. Erdman, G. Sapisochin*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)593-602
Number of pages10
JournalLiver Transplantation
Issue number4
Publication statusPublished - Apr 2022

Bibliographical note

Funding Information:
Gonzalo Sapisochin consults for, advises, and received grants from Roche. He consults for Novartis and also advises AstraZeneca.

Publisher Copyright:
Copyright © 2021 American Association for the Study of Liver Diseases.


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