title: Artificial neural network prediction of ischemic tissue fate in acute stroke imaging.
abstract: Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin-spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.
authors.affiliation: Research Imaging Institute, UT Health Science, San Antonio, Texas 78229, USA.
authors.forename: Shiliang
authors.initials: S
authors.lastname: Huang
authors.forename: Qiang
authors.initials: Q
authors.lastname: Shen
authors.forename: Timothy Q
authors.initials: TQ
authors.lastname: Duong
mesh_terms: D000465:Algorithms; D000818:Animals; D002545:Brain Ischemia / Q000473:pathology*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004058:Diffusion; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D020244:Infarction, Middle Cerebral Artery / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D011237:Predictive Value of Tests; D051381:Rats; D017207:Rats, Sprague-Dawley; D020521:Stroke / Q000473:pathology*