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Related Experiment Video

Updated: Jul 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and

Jin Jin1, Ying Jiang1, Yu-Lan Zhao1

  • 1Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.).

Academic Radiology
|October 22, 2023
PubMed
Summary

Radiomics models show promise in predicting hepatocellular carcinoma (HCC) recurrence, outperforming clinical models. Combining radiomics with clinical features further enhances prediction accuracy across various imaging techniques.

Keywords:
Hepatocellular carcinomaMeta-analysisPredictionRadiomicsRecurrence

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Area of Science:

  • Oncology
  • Radiology
  • Medical Informatics

Background:

  • Hepatocellular carcinoma (HCC) recurrence poses a significant challenge in patient management.
  • Accurate prediction of HCC recurrence is vital for tailoring treatment strategies and improving patient outcomes.
  • Radiomics models have emerged as a potential tool for predicting HCC recurrence.

Conclusions:

  • Radiomics models hold significant potential for predicting HCC recurrence, offering enhanced predictive value when combined with clinical features across diverse imaging modalities.
  • The integration of radiomics with clinical data shows a promising approach for improving the accuracy of HCC recurrence prediction.
  • Further research is warranted to optimize radiomics methodologies and validate findings in larger, multi-center studies.