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Related Experiment Video

Updated: May 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival

Yi Chen1,2, David Pasquier3,4,5, Damon Verstappen1

  • 1The D-Lab, Department of Precision Medicine, GROW-Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.

Journal of Cancer Research and Clinical Oncology
|February 13, 2025
PubMed
Summary

An ensemble model integrating radiomics, deep learning, and clinical data accurately predicts 2-year survival in hepatocellular carcinoma (HCC) patients undergoing stereotactic body radiation therapy (SBRT). This approach offers improved prognostic insights for HCC treatment.

Keywords:
Deep learningHandcrafted featuresHepatocellular carcinomaRadiomicsStereotactic body radiation therapy

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Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Hepatocellular carcinoma (HCC) presents a significant global health challenge with rising incidence and poor survival rates.
  • Stereotactic body radiation therapy (SBRT) is a key treatment modality for HCC, necessitating accurate survival prediction.

Purpose of the Study:

  • To develop a robust predictive model for 2-year survival in HCC patients treated with SBRT.
  • To integrate radiomics, deep learning features, and clinical data for enhanced predictive accuracy.

Main Methods:

  • Analysis of 186 HCC patients treated with SBRT.
  • Extraction of radiomics features from CT scans and collection of clinical data.
  • Development and validation of machine learning and deep learning models, including CNNs, using nested cross-validation.
  • Application of explainability techniques (e.g., Grad-CAM) to interpret model predictions.

Main Results:

  • Handcrafted radiomics features showed moderate predictive performance (AUC 0.59-0.72).
  • Deep learning models integrating image and clinical data improved prediction (AUC 0.71-0.81).
  • An ensemble model combining radiomics, deep learning, and clinical data achieved the highest AUC of 0.86 (95% CI: 0.80-0.93).

Conclusions:

  • The developed ensemble model offers a powerful tool for predicting survival in HCC patients undergoing SBRT.
  • Integration of diverse data types (radiomics, deep learning, clinical) enhances predictive capabilities.
  • Interpretability methods improve the transparency and clinical utility of predictive models.