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Machine learning-based decision-tree model for patients with single-large hepatocellular carcinoma.

Yi-Chen Lin1, Chun-Ting Ho1, Pei-Chang Lee1,2,3

  • 1Division of General Medicine, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.

Journal of the Chinese Medical Association : JCMA
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning (ML) decision-tree model accurately predicts prognosis for patients with single-large hepatocellular carcinoma (SLHCC). This tool uses routine clinical data to stratify risk and aid personalized treatment planning for SLHCC.

Keywords:
Decision-tree modelFibrosis-4Hepatocellular carcinomaMachine learningOutcomes

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Area of Science:

  • Hepatology
  • Machine Learning in Medicine
  • Oncology

Background:

  • Single-large hepatocellular carcinoma (SLHCC) presents prognostic heterogeneity.
  • Existing staging systems have limited accuracy for SLHCC prognosis.
  • A need exists for improved risk stratification in SLHCC.

Purpose of the Study:

  • To develop a machine learning (ML)-based decision-tree model for SLHCC.
  • To enhance individualized prognostic stratification for SLHCC patients.
  • To improve the predictive accuracy of staging systems for this specific subgroup.

Main Methods:

  • Retrospective study of 477 SLHCC patients.
  • Development of a decision-tree algorithm using multivariate Cox regression.
  • Model validation using accuracy and AUROC in training and validation cohorts.

Main Results:

  • Six variables independently associated with overall survival (OS) identified.
  • A decision-tree model incorporating treatment, creatinine, tumor size, and FIB-4 was developed.
  • The model achieved 74.3% accuracy and 0.756 AUROC in the training cohort, and 67.1% accuracy and 0.706 AUROC in the validation cohort.

Conclusions:

  • The ML decision-tree model effectively stratifies SLHCC patients by prognosis.
  • The model utilizes routine clinical and laboratory data.
  • This tool can complement existing staging systems and support personalized treatment decisions.