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Related Experiment Video

Updated: May 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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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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Predicting tumour response.

Samuel D Kyle1, W Phillip Law, Kenneth A Miles

  • 1Department of Radiology, Princess Alexandra Hospital, Brisbane, Australia; School of Medicine, University of Queensland, Southern Clinical School, Brisbane, Australia.

Cancer Imaging : the Official Publication of the International Cancer Imaging Society
|September 25, 2013
PubMed
Summary
This summary is machine-generated.

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Oncologic imaging can predict tumor response to cancer treatments by identifying therapeutic targets and resistance phenotypes. Further validation is needed for clinical integration to personalize therapy and improve patient outcomes.

Area of Science:

  • Oncologic imaging
  • Radiology
  • Molecular imaging

Background:

  • Individualized cancer treatment is becoming standard of care.
  • Predicting treatment response is crucial for optimizing therapy.
  • Current imaging methods offer insights into tumor biology and treatment response.

Purpose of the Study:

  • To define tumor response in oncology.
  • To illustrate how imaging techniques reveal biological characteristics predicting treatment benefit.
  • To review imaging approaches for identifying therapeutic targets and treatment-resistant phenotypes.

Main Methods:

  • Review of existing literature on oncologic imaging techniques.
  • Description of two main imaging approaches: target identification and resistance phenotype depiction.

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Published on: October 10, 2018

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  • Examples include radionuclide imaging, angiogenesis imaging, dynamic contrast-enhanced CT/MRI, diffusion-weighted MRI, hypoxia imaging, and 99mTc-MIBI imaging.

    • Main Results:

    • Imaging can identify therapeutic targets (e.g., radionuclide imaging) and predict resistance (e.g., hypoperfusion, necrosis, hypoxia, P-glycoprotein expression).
    • Techniques like dynamic contrast-enhanced CT/MRI, diffusion-weighted MRI, and specific radionuclide imaging show potential.
    • Clinical adoption is limited by insufficient validation and lack of correlation with endpoints like survival.

    Conclusions:

    • Imaging predictors of response hold significant potential for personalizing cancer therapy.
    • Refined imaging techniques can improve treatment efficacy, cost-effectiveness, and avoid futile treatments.
    • Further validation is essential for integrating these predictive tools into clinical practice.