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Combined Near-infrared Fluorescent Imaging and Micro-computed Tomography for Directly Visualizing Cerebral Thromboemboli
Published on: September 25, 2016
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Thrombus imaging characteristics within acute ischemic stroke: similarities and interdependence
Nerea Arrarte Terreros1,2, Agnetha Ae Bruggeman2, Manon Kappelhof3,2
1Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands n.arrarteterreros@amsterdamumc.nl.
Journal of Neurointerventional Surgery
|July 14, 2022
Summary
Thrombus imaging characteristics in acute ischemic stroke are highly correlated. Predictive models must account for these relationships, as distinct thrombus types were not identified, highlighting the need for individualized treatment approaches.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Thrombus imaging characteristics influence stroke outcomes.
- Analyzing these features separately can lead to unreliable models due to multicollinearity.
- Understanding thrombus feature correlations is crucial for accurate prediction.
Purpose of the Study:
- To investigate the distribution, correlation, and clustering of thrombus imaging characteristics.
- To analyze a large dataset of patients with anterior-circulation acute ischemic stroke.
- To inform future predictive modeling and treatment strategies.
Main Methods:
- Utilized the MR CLEAN Registry dataset including 934 patients.
- Measured thrombus imaging features: occlusion location, distance (DT), length, density, perviousness, and clot burden score (CBS).
- Assessed intercorrelations using Spearman's coefficient and employed unsupervised clustering.
Main Results:
- All measured thrombus characteristics were significantly correlated.
- Higher CBS correlated with longer DT (ρ=0.67) and shorter thrombus length (ρ=-0.41).
- Proximal occlusions showed longer, denser, and less pervious thrombi; clustering yielded weak group cohesion.
Conclusions:
- Significant intercorrelations among thrombus imaging characteristics necessitate consideration in predictive models.
- Absence of distinct thrombus archetypes suggests treatment variability is key.
- Future research should account for thrombus feature interdependencies and individual variability.
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