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Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models

Abstract : Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI 0 ), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI 24h ) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI 24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI 0 as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29–0.68]) than U-Net (0.48 [0.18–0.68]), Random Forests (0.51 [0.27–0.66]), and clinical thresholding method (0.45 [0.25–0.62]) ( P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.
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Contributor : Bertrand Thirion Connect in order to contact the contributor
Submitted on : Monday, August 30, 2021 - 4:10:46 PM
Last modification on : Friday, August 5, 2022 - 2:43:21 PM

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Joseph Benzakoun, Sylvain Charron, Guillaume Turc, Wagih Ben Hassen, Laurence Legrand, et al.. Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models. Journal of Cerebral Blood Flow and Metabolism, Nature Publishing Group, 2021, pp.0271678X2110243. ⟨10.1177/0271678X211024371⟩. ⟨hal-03329129⟩



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