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Updated: Nov 5, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Clot-based radiomics features predict first pass effect in acute ischemic stroke.

Orkun Sarioglu¹, Fatma C Sarioglu², Ahmet E Capar²

¹Department of Radiology, Izmir Democracy University, Izmir, Turkey.

Interventional Neuroradiology : Journal of Peritherapeutic Neuroradiology, Surgical Procedures and Related Neurosciences

|May 18, 2021

Summary

Radiomics features from clots on CT scans can predict the first pass effect (FPE) in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy. These features, along with female sex and baseline ASPECT score, improve FPE prediction accuracy.

Keywords:

Ischemic stroke artificial intelligence first pass effect thrombectomy

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A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

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Published on: September 16, 2017

Area of Science:

Radiology
Medical Imaging
Stroke Neurology

Background:

Acute ischemic stroke (AIS) requires rapid intervention, often mechanical thrombectomy (MT).
Predicting the first pass effect (FPE), a measure of successful reperfusion, is crucial for treatment planning.
Clot characteristics on imaging may influence MT outcomes.

Purpose of the Study:

To evaluate clot-based radiomics features (RFs) for predicting FPE in AIS patients.
To identify other clinical and imaging variables associated with FPE.

Main Methods:

Retrospective review of 52 AIS patients undergoing MT for anterior circulation large vessel occlusion.
Extraction of RFs from pre-treatment noncontrast computed tomography (NCCT) clot images.
Logistic regression analysis to identify independent predictors of FPE.

Main Results:

48.1% of patients achieved FPE.
Twelve RFs differed significantly between FPE and non-FPE groups.
Long-run low gray-level emphasis and zone percentage were independent predictors of FPE, alongside female sex and baseline ASPECT score >8.5.
A predictive model using these factors achieved 83% diagnostic accuracy for FPE.

Conclusions:

Clot-based radiomics features on NCCT show promise in estimating MT success (FPE) in AIS patients.
Integrating RFs with clinical factors enhances FPE prediction.