Quantitative prediction of acute ischemic tissue fate using support vector machine

Accurate and quantitative prediction of ischemic tissue fate could improve decision-making in the clinical treatment of acute stroke. The goal of the present study is to explore the novel use of support vector machine (SVM) to predict infarct on a pixel-by-pixel basis using only acute cerebral blood flow (CBF), apparent diffusion coefficient (ADC) MRI data. The efficacy of SVM prediction model was tested on three stroke groups: 30-min, 60-min, and permanent middle cerebral-artery occlusion (n = 12 rats for each group). CBF, ADC and relaxation time constant (T2) were acquired during the acute phase up to 3 h and again at 24 h. Infarct was predicted using only acute (30-min) stroke data. Receiver-operating characteristic (ROC) analysis was used to quantify prediction accuracy. The areas under the receiver-operating curves were 86 ± 2.7%, 89 ± 1.4%, and 93 ± 0.8% using ADC + CBF data for the 30-min, 60-min and permanent middle cerebral artery occlusion (MCAO) group, respectively. Adding neighboring pixel information and spatial infarction incidence improved performance to 88 ± 2.8%, 94 ± 0.8%, and 97 ± 0.9%, respectively. SVM prediction compares favorably to a previously published artificial neural network (ANN) prediction algorithm operated on the same data sets. SVM prediction model has the potential to provide quantitative frameworks to aid clinical decision-making in the treatment of acute stroke.