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Related Experiment Video

Updated: May 12, 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

Preoperative Prediction of TACE Refractoriness in Hepatocellular Carcinoma Using CT-Based Radiomics Model.

Liyang Yang1, Disi Liu2, Shanshan Yang1

  • 1Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.

Journal of Hepatocellular Carcinoma
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

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This study developed a combined model using radiomics and clinical factors to predict early refractoriness to transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients, improving treatment decisions.

Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Hepatocellular carcinoma (HCC) is a primary liver cancer.
  • Transarterial chemoembolization (TACE) is a standard treatment for unresectable HCC.
  • Predicting early refractoriness to TACE is crucial for timely treatment adjustment.

Purpose of the Study:

  • To develop an integrated predictive model combining radiomics and clinical risk factors.
  • To predict early refractoriness to transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients.
  • To enhance treatment decision-making for HCC patients undergoing TACE.

Main Methods:

  • A cohort of 180 HCC patients from Hospital A and 42 from Hospital B were included.
  • Radiomic features were extracted from CT scans and selected using LASSO regression and Boruta algorithm.
Keywords:
HCCTACE refractorinessmachine learningnomogramradiomics

Related Experiment Videos

Last Updated: May 12, 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 models, including Random Forest, were developed and interpreted using SHAP analysis.
  • A combined model integrated radiomics (Radscore) with clinical factors (tumor diameter).

    • Main Results:

    • The Random Forest model achieved an AUC of 0.841 (testing) and 0.777 (validation).
    • Radiomic features significantly contributed to the model's predictive power, as shown by SHAP analysis.
    • The combined model demonstrated superior AUCs of 0.842 (testing) and 0.847 (validation), outperforming radiomics and clinical models alone.
    • Calibration and decision curve analyses confirmed the clinical utility of the combined model nomogram.

    Conclusions:

    • The integrated model shows strong predictive performance for early TACE refractoriness in HCC.
    • This model can aid clinicians in making informed decisions about subsequent TACE treatments.
    • The findings suggest potential for improved patient management and outcomes in HCC treatment.