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Dynamic Risk Modelling of Hepatocellular Carcinoma.

Zhenning Yu1,2, Shyna Zhuoying Gunalan1,2, Nicole Shu Ying Tang1,2

  • 1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

Liver International : Official Journal of the International Association for the Study of the Liver
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Markov model for hepatocellular carcinoma (HCC) to better predict patient survival and recurrence after treatment. The model identifies key factors influencing disease progression and survival, improving risk stratification for HCC patients.

Keywords:
hepatocellular carcinomaliver cancerrecurrencerisk factors

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Area of Science:

  • Hepatobiliary Medicine
  • Oncology
  • Biostatistics

Background:

  • Hepatocellular carcinoma (HCC) survival remains poor despite treatment advances.
  • Traditional survival analyses inadequately capture dynamic disease progression.
  • A continuous-time multi-state Markov model is proposed for HCC risk stratification.

Purpose of the Study:

  • To assess recurrence and survival risks following curative treatment for HCC.
  • To utilize a dynamic framework for understanding HCC disease trajectories.
  • To inform improved management strategies for HCC patients.

Main Methods:

  • A cohort study of 934 adult HCC patients undergoing curative treatment (transplantation, resection, ablation).
  • Inclusion of data from eight tertiary centers across Australia, China, Japan, Singapore, South Korea, and the United States.
  • Application of a continuous-time multi-state Markov model to analyze transitions between disease states.

Main Results:

  • Identified distinct clinical trajectories: recurrence, death without recurrence, and death after recurrence.
  • Median times to recurrence, death post-recurrence, and death without recurrence were 15.4, 51.3, and 23.1 months, respectively.
  • Advancing age and HCV infection increased recurrence risk; liver transplantation was protective. AFP, tumor size, INR, bilirubin, and BCLC stage predicted recurrence and mortality.

Conclusions:

  • The multi-state Markov model provides dynamic insights into HCC progression.
  • Updated estimates for time spent in transition states and predictors of disease progression were established.
  • This dynamic framework enhances risk stratification and management strategies for HCC.