Prediction of hemorrhagic transformation after experimental ischemic stroke using MRI-based algorithms

Author:

Bouts Mark JRJ1234,Tiebosch Ivo ACW1,Rudrapatna Umesh S1,van der Toorn Annette1,Wu Ona2,Dijkhuizen Rick M1

Affiliation:

1. Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands

2. Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

3. Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands

4. Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

Abstract

Estimation of hemorrhagic transformation (HT) risk is crucial for treatment decision–making after acute ischemic stroke. We aimed to determine the accuracy of multiparametric MRI-based predictive algorithms in calculating probability of HT after stroke. Spontaneously, hypertensive rats were subjected to embolic stroke and, after 3 h treated with tissue plasminogen activator (Group I: n = 6) or vehicle (Group II: n = 7). Brain MRI measurements of T2, T2*, diffusion, perfusion, and blood–brain barrier permeability were obtained at 2, 24, and 168 h post-stroke. Generalized linear model and random forest (RF) predictive algorithms were developed to calculate the probability of HT and infarction from acute MRI data. Validation against seven-day outcome on MRI and histology revealed that highest accuracy of hemorrhage prediction was achieved with a RF-based model that included spatial brain features (Group I: area under the receiver-operating characteristic curve (AUC) = 0.85 ± 0.14; Group II: AUC = 0.89 ± 0.09), with significant improvement over perfusion- or permeability-based thresholding methods. However, overlap between predicted and actual tissue outcome was significantly lower for hemorrhage prediction models (maximum Dice’s Similarity Index (DSI) = 0.20 ± 0.06) than for infarct prediction models (maximum DSI = 0.81 ± 0.06). Multiparametric MRI-based predictive algorithms enable early identification of post-ischemic tissue at risk of HT and may contribute to improved treatment decision-making after acute ischemic stroke.

Publisher

SAGE Publications

Subject

Cardiology and Cardiovascular Medicine,Neurology (clinical),Neurology

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