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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.

Chien-Hung Lu1, Ching-Wen Chang2,3, San-Chi Chen4,5,6

  • 1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.

Journal of Hepatocellular Carcinoma
|December 1, 2025
PubMed
Summary

Machine learning models accurately predict overall survival (OS) and progression-free survival (PFS) for unresectable hepatocellular carcinoma (HCC) patients treated with lenvatinib. These models stratify patients into risk groups, aiding clinical decision-making.

Keywords:
cox proportional hazards modelgradient boosting machineoverall survivalprogression-free survivalrandom survival forestsurvival models

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Lenvatinib is a key treatment for advanced unresectable hepatocellular carcinoma (HCC).
  • Patient outcomes with lenvatinib vary significantly, necessitating better predictive tools.
  • Existing machine learning (ML) models predict outcomes at specific time points, but comprehensive survival analysis with censored data is lacking.

Purpose of the Study:

  • To develop and compare ML-based survival models for predicting overall survival (OS) and progression-free survival (PFS) in unresectable HCC patients treated with lenvatinib.
  • To incorporate censored survival data for a more complete prognostic evaluation.
  • To identify key prognostic factors influencing survival outcomes.

Main Methods:

  • A retrospective multicenter study included 205 patients with unresectable HCC receiving lenvatinib.
  • Five ML-based survival models were developed and compared using Harrell's concordance index (C-index).
  • Predicted risk scores were used to stratify patients into low-, intermediate-, and high-risk groups and validated.

Main Results:

  • The GBM-Cox model demonstrated the highest predictive performance for OS (C-index=0.617) and PFS (C-index=0.645).
  • Risk stratification effectively separated patients into distinct OS and PFS groups (p<0.05).
  • Key predictors for OS included albumin-bilirubin (ALBI) score, alanine aminotransferase, and age; for PFS, they were macrovascular invasion, ALBI score, and alpha-fetoprotein.

Conclusions:

  • ML-based survival models can successfully stratify patients based on OS and PFS, aiding risk assessment.
  • The ALBI score emerged as a crucial prognostic factor for both OS and PFS.
  • These models offer potential for guiding clinical treatment decisions and prognostic evaluations in HCC management.