Clot-Based Radiomics Predict a Mechanical Thrombectomy Strategy for Successful Recanalization in Acute Ischemic Stroke

Stroke; a Journal of Cerebral Circulation
Jeremy HofmeisterPaolo Machi

Abstract

Mechanical thrombectomy (MTB) is a reference treatment for acute ischemic stroke, with several endovascular strategies currently available. However, no quantitative methods are available for the selection of the best endovascular strategy or to predict the difficulty of clot removal. We aimed to investigate the predictive value of an endovascular strategy based on radiomic features extracted from the clot on preinterventional, noncontrast computed tomography to identify patients with first-attempt recanalization with thromboaspiration and to predict the overall number of passages needed with an MTB device for successful recanalization. We performed a study including 2 cohorts of patients admitted to our hospital: a retrospective training cohort (n=109) and a prospective validation cohort (n=47). Thrombi were segmented on noncontrast computed tomography, followed by the automatic computation of 1485 thrombus-related radiomic features. After selection of the relevant features, 2 machine learning models were developed on the training cohort to predict (1) first-attempt recanalization with thromboaspiration and (2) the overall number of passages with MTB devices for successful recanalization. The performance of the models was evalu...Continue Reading

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Citations

Dec 20, 2020·Journal of Neuroradiology. Journal De Neuroradiologie·Alejandro TomaselloMarc Ribo
Mar 2, 2021·Stroke; a Journal of Cerebral Circulation·David S Liebeskind, Joanna M Wardlaw
May 19, 2021·Interventional Neuroradiology : Journal of Peritherapeutic Neuroradiology, Surgical Procedures and Related Neurosciences·Orkun SariogluUmit Belet

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Methods Mentioned

BETA
feature extraction

Software Mentioned

learning
Slicer
pyradiomics
3D
scikit

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