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Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

Henk van Voorst1, Praneeta Konduri2, Adrien Ter Schiphorst2

  • 1Department of Radiology, Stanford School of Medicine, Stanford, California, United States of America; Department of Neurology, Stanford School of Medicine, Stanford, California, United States of America; Department of Neurology, Montpellier University Hospital, Montpellier, France; Department of Neurology, University Hospitals Leuven, Leuven, Belgium; Division of Experimental Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium; and Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium. hvv@stanford.edu.

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Summary
This summary is machine-generated.

Deep learning markers from noncontrast CT (DLNCCT) predict post-transfer core volume in acute ischemic stroke. These DLNCCT markers improved CT Perfusion (CTP) based prediction, aiding treatment decisions.

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Deep learning (DL) models can extract ischemic lesion imaging markers from noncontrast CT (DLNCCT).
  • Predictive value of DLNCCT markers for post-transfer core volume in acute ischemic stroke remains uncertain.

Purpose of the Study:

  • To evaluate if DLNCCT markers predict post-transfer core volume.
  • To compare DLNCCT marker prediction with ASPECTS and CT Perfusion (CTP).

Main Methods:

  • Post-hoc analysis of patients transferred for endovascular treatment (EVT).
  • Quantified total lesion volume, modified net water uptake (mNWU), and severely hypodense volume using DLNCCT.
  • Compared DLNCCT markers with ASPECTS and CTP for association and prediction of post-transfer core volume.

Main Results:

  • DLNCCT markers (total and severely hypodense volumes) were independent predictors of post-transfer core volume.
  • DLNCCT markers showed similar predictive performance to ASPECTS.
  • Adding DLNCCT markers to CTP significantly improved post-transfer core volume prediction.

Conclusions:

  • DLNCCT-derived total and severely hypodense volumes are valuable predictors of post-transfer core volume.
  • DLNCCT markers enhance the accuracy of CTP-based prediction models for acute ischemic stroke.