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Related Experiment Video

Updated: Jun 26, 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

Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis

Chang-Lei Li1,2, Zhen Jia1,2, Zhi-Yuan Yao2,3

  • 1Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

Journal of Hepatocellular Carcinoma
|June 25, 2026
PubMed
Summary

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World journal of clinical cases·2024

A new machine learning model integrating ultrasound radiomics, steatosis grade, and clinical data improves risk prediction for hepatocellular carcinoma (HCC) patients after surgery. This approach enhances predictions of overall survival and recurrence-free survival, aiding personalized patient care.

Area of Science:

  • Hepatocellular Carcinoma (HCC) Research
  • Machine Learning in Oncology
  • Liver Disease Prognostics

Background:

  • Nonalcoholic fatty liver disease (NAFLD) is a growing cause of hepatocellular carcinoma (HCC).
  • The prognostic significance of liver steatosis in HCC requires further definition.
  • Accurate postoperative risk stratification for HCC is crucial.

Purpose of the Study:

  • To develop and validate an integrated machine learning model for postoperative risk stratification in HCC.
  • To combine ultrasound radiomics, pathological steatosis grading, and clinicopathological variables.
  • To assess the prognostic value of liver steatosis in HCC patients.

Main Methods:

  • Retrospective analysis of 639 HCC patients undergoing curative resection (2010-2023).
Keywords:
NAFLDhepatocellular carcinomamachine learningprognosisrecurrenceultrasound radiomics

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Published on: August 16, 2020

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Last Updated: Jun 26, 2026

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

Published on: October 10, 2018

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Extraction of radiomic features from ultrasound images and generation of a radiomics signature.
  • Integration of Kleiner score for hepatic steatosis and Boruta algorithm for clinicopathological variables.
  • Development and comparison of 101 machine learning models, with performance evaluated by AUC, Brier score, and calibration.

    • Main Results:

    • A random survival forest model demonstrated superior performance in predicting overall survival (OS) and recurrence-free survival (RFS).
    • Achieved 1-, 3-, and 5-year AUCs of 0.863, 0.794, 0.804 for OS and 0.828, 0.811, 0.823 for RFS.
    • The integrated model outperformed BCLC and CNLC staging systems in discrimination, calibration, and net clinical benefit.

    Conclusions:

    • An integrated radiopatho-clinical machine learning model shows strong internal validation for predicting OS and RFS post-curative resection.
    • Steatosis-related features offer valuable prognostic information for individualized postoperative surveillance.
    • External validation is necessary to confirm the model's generalizability.