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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.

Haseeb Mukhtar1,2, Ali Ganjizadeh1,3, Ajay Misra1,3

  • 1Department of Radiology, Mayo Clinic, Rochester, MN, USA.

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Artificial intelligence (AI) shows significant promise in vascular interventional radiology (VIR) for image analysis and prediction tasks. Further research and collaboration are essential for its responsible integration into patient care.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • AI excels in diagnostic radiology but has limited VIR application due to data variability.
  • Vascular interventional radiology (VIR) procedures often involve complex imaging analysis.

Purpose of the Study:

  • To review AI applications across preprocedural, intraprocedural, and post-procedural VIR stages.
  • To assess AI's impact on patient care using various imaging modalities.

Main Methods:

  • Comprehensive literature search of PubMed, Embase, and Web of Science.
  • Categorization of AI studies by imaging modality (CT, MRI, fluoroscopy/DSA, ultrasound, X-ray, multimodal) and task type (segmentation, detection, prediction).

Main Results:

  • Deep learning achieved high accuracy (0.82-0.962) in anatomical segmentation (e.g., aneurysms).
  • AI detection tasks showed high performance (e.g., 95% accuracy for endoleak detection, 0.97 AUC for stenosis).
  • Prediction models (AUC > 0.90) outperformed clinical assessments for outcomes after EVAR, TEVAR, TACE, TARE.

Conclusions:

  • AI demonstrates transformative potential in VIR, particularly with radiomics and machine learning for treatment response.
  • Challenges include data limitations, bias, and interpretability.
  • Clinician-AI expert collaboration is vital for safe and effective deployment in interventional radiology.