Prediction of hemorrhagic transformation after experimental ischemic stroke using MRI-based algorithms.
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.
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.
Mark Jrj
MJ
Bouts
1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Ivo Acw
IA
Tiebosch
1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Umesh S
US
Rudrapatna
1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Annette
A
van der Toorn
2 Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
Ona
O
Wu
1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Rick M
RM
Dijkhuizen
D000465:Algorithms; D000818:Animals; D002545:Brain Ischemia / Q000150:complications / Q000000981:diagnostic imaging* / Q000473:pathology; D002543:Cerebral Hemorrhage / Q000000981:diagnostic imaging* / Q000209:etiology / Q000473:pathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D004195:Disease Models, Animal; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008954:Models, Biological; D011237:Predictive Value of Tests; D011918:Rats, Inbred SHR; D020521:Stroke / Q000000981:diagnostic imaging* / Q000209:etiology / Q000473:pathology
Ischemic stroke; animal model; hemorrhage; magnetic resonance imaging; prediction