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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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Fully automated stroke tissue estimation using random forest classifiers (FASTER).

Richard McKinley1, Levin Häni1, Jan Gralla1

  • 11 Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland.

Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism
|November 1, 2016
PubMed
Summary
This summary is machine-generated.

This study presents an automated method using MRI to accurately estimate brain tissue-at-risk (penumbra) in ischemic stroke patients. This approach improves upon traditional methods for better treatment selection.

Keywords:
Acute strokeendovascular therapymagnetic resonance diffusion imagingmagnetic resonance perfusionmathematical modeling

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Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging

Background:

  • Mechanical thrombectomy is effective for ischemic stroke within 6 hours.
  • Accurate assessment of the penumbra is crucial for personalized stroke treatment.
  • Current methods for penumbra estimation have limitations.

Purpose of the Study:

  • To introduce a fully automated method for estimating penumbra volume using multimodal MRI.
  • To improve the accuracy of tissue-at-risk assessment beyond predefined thresholds.

Main Methods:

  • Utilized multimodal MRI: diffusion-weighted imaging, T2w and T1w contrast-enhanced sequences, and dynamic susceptibility contrast perfusion MRI.
  • Developed an automated method to predict tissue damage under persistent occlusion and complete recanalization scenarios.
  • Applied the method to 19 test cases with thrombolysis in cerebral infarction grading of 1-2a.

Main Results:

  • The automated method showed a mean overestimation of final lesion volume of 30 ml, significantly less than 121 ml for manual thresholding.
  • Predicted tissue-at-risk volume was positively correlated with final lesion volume (p < 0.05).

Conclusions:

  • The automated MRI-based method offers a substantial improvement for identifying tissue-at-risk in ischemic stroke.
  • This method may aid in personalized treatment selection by providing a more accurate assessment of the penumbra.