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Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps.

Daan Peerlings1, Fasco van Ommen2, Edwin Bennink2,3

  • 1Department of Radiology, University Medical Center Utrecht, Utrecht, 3584CX, The Netherlands. d.peerlings@umcutrecht.nl.

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Summary

Multivariable probabilistic classification of computed tomography perfusion (CTP) data more accurately estimates ischemic stroke regions than single parameter thresholding. This advanced method improves precision and recall for infarct volume prediction.

Keywords:
Brain ischemiaLogistic modelsPerfusion imagingStrokeTomography, X-ray computed

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Area of Science:

  • Medical Imaging
  • Neurology
  • Radiology

Background:

  • Accurate estimation of ischemic stroke regions is crucial for effective treatment planning and outcome prediction.
  • Computed tomography perfusion (CTP) provides valuable hemodynamic information but requires robust analysis methods for infarct delineation.

Purpose of the Study:

  • To compare the efficacy of single parameter thresholding versus multivariable probabilistic classification for analyzing CTP parameter maps in ischemic stroke.

Main Methods:

  • Analysis of 225 CTP datasets from multicenter trials, categorized by arterial occlusion grade.
  • Generation of CTP parameter maps using commercial (ISP), bSVD, and NLR methods.
  • Comparison of conventional thresholding with a logistic regression-based probabilistic classification model.

Main Results:

  • Multivariable probabilistic classification demonstrated superior precision and recall compared to individual CTP parameter thresholding.
  • Probabilistic classification significantly reduced the median difference between estimated and follow-up infarct volumes across all CTP generation methods.

Conclusions:

  • Multivariable probabilistic maps derived from CTP data provide more accurate infarct lesion estimation than thresholded maps.
  • A harmonized, multivariable probabilistic approach enhances the classification of ischemic stroke regions, improving diagnostic accuracy.