Journal article
Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke
Hulin Kuang, Wu Qiu, Anna M Boers, Scott Brown, Keith Muir, Charles BLM Majoie, Diederik WJ Dippel, Phil White, Jonathan Epstein, Peter J Mitchell, Antoni Davalos, Serge Bracard, Bruce Campbell, Jeffrey L Saver, Tudor G Jovin, Marta Rubiera, Alexander Khaw, Jai J Shankar, Enrico Fainardi, Michael D Hill Show all
STROKE | LIPPINCOTT WILLIAMS & WILKINS | Published : 2021
Abstract
BACKGROUND AND PURPOSE: Prediction of infarct extent among patients with acute ischemic stroke using computed tomography perfusion is defined by predefined discrete computed tomography perfusion thresholds. Our objective is to develop a threshold-free computed tomography perfusion-based machine learning (ML) model to predict follow-up infarct in patients with acute ischemic stroke. METHODS: Sixty-eight patients from the PRoveIT study (Measuring Collaterals With Multi-Phase CT Angiography in Patients With Ischemic Stroke) were used to derive a ML model using random forest to predict follow-up infarction voxel by voxel, and 137 patients from the HERMES study (Highly Effective Reperfusion Evalu..
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Funding Acknowledgements
Dr Menon holds the Heart and Stroke/University of Calgary Professorship in Stroke Imaging and Alberta Innovate Health Solution funding.