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Related Experiment Video

Updated: Jun 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

Ryan Yanzhe Lim1, Vigneshwaran Selvakumar2, Nicholas Syn3,4

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

Alimentary Pharmacology & Therapeutics
|June 18, 2026
PubMed

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Summary

A new prognostic model, the Liver Cancer Risk predictioN (LCRN) Index, shows improved accuracy for predicting hepatocellular carcinoma (HCC) outcomes. This tool incorporates liver function and tumor characteristics for better patient management.

Area of Science:

  • Hepatology and Oncology
  • Biomarker Discovery
  • Clinical Prognostics

Background:

  • Existing prognostic models for hepatocellular carcinoma (HCC) often lack sufficient accuracy.
  • There is a need for improved tools to predict HCC patient outcomes and guide treatment decisions.

Purpose of the Study:

  • To develop and validate a novel prognostic model for HCC.
  • The model aims to integrate biomarkers of liver function and tumor characteristics for enhanced predictive power.

Main Methods:

  • A cohort of 1102 HCC patients from international institutions was used for derivation and validation.
  • The Liver Cancer Risk predictioN (LCRN) Index was built using a gradient-boosted decision tree model within a Cox proportional hazards framework.
  • Performance was assessed using concordance index (C-index) and compared against established models like ALBI and BCLC staging.
Keywords:
liver cancermachine learning modelprognostic systems

Related Experiment Videos

Last Updated: Jun 19, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Main Results:

  • The LCRN Index incorporates factors including type 2 diabetes mellitus, ascites, hepatic encephalopathy, albumin, bilirubin, AFP, and largest tumor nodule diameter.
  • In external validation, the LCRN Index demonstrated superior discriminative ability (e.g., 5-year AUC: 0.75) and improved calibration compared to ALBI grade and BCLC stage.
  • The model showed promising performance in predicting HCC prognosis over 1, 3, and 5 years.

Conclusions:

  • The LCRN Index is a potentially valuable tool for prognosticating HCC.
  • Further validation could lead to significant clinical implications for HCC management and patient care.