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Predicting Response to Transarterial Chemoembolization in Hepatocellular Carcinoma Using Machine Learning Models.

Niharika Dutta1, Pankaj Gupta1

  • 1Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India.

The Indian Journal of Radiology & Imaging
|March 23, 2026
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Summary

Machine learning models can predict transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC). The clinical model showed the best performance, indicating potential for improved HCC treatment planning.

Keywords:
computed tomographydeep neural networkhepatocellular carcinomamachine learningtransarterial chemoembolizationtreatment response

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Hepatocellular carcinoma (HCC) is a major cause of cancer mortality.
  • Transarterial chemoembolization (TACE) is a primary treatment for intermediate-stage HCC.
  • Predicting TACE response is crucial but challenging.

Purpose of the Study:

  • To investigate machine learning (ML) models for predicting TACE response in HCC patients.
  • To compare the performance of clinical, radiomic, deep neural network (DNN), and combined models.

Main Methods:

  • Utilized the public WAW-TACE dataset with clinical data and CT images.
  • Trained four models: clinical, radiomic, DNN, and combined clinicoradiological.
  • Evaluated models using cross-validation and a held-out test set to predict TACE failure.

Main Results:

  • The clinical support vector machine model achieved 70% accuracy and an AUC of 0.778.
  • The radiomic logistic regression model showed 76.1% accuracy and an AUC of 0.740.
  • The DNN and combined models had lower predictive performance in this cohort.

Conclusions:

  • A multimodal approach was used to predict TACE response in HCC.
  • Further optimization and multicenter data are needed to improve predictive accuracy.