Quantitative prediction of acute ischemic tissue fate using support vector machine

Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX 78229, USA.
Brain research (Impact Factor: 2.83). 08/2011; 1405:77-84. DOI: 10.1016/j.brainres.2011.05.066
Source: PubMed

ABSTRACT 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 3h and again at 24h. 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.

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Available from: Qiang Shen, Mar 17, 2014
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    • "Incorporating additional imaging data (such as CBF) could further improve prediction accuracy. Finally, although performance was evaluated by sensitivity and specificity calculations, future studies will utilize more sophisticated algorithms, such as support vector machine with separate training and experimental groups, to quantitatively predict tissue fate (Huang et al., 2011). "
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    ABSTRACT: Algorithms to predict ischemic tissue fate based on acute stroke MRI typically utilized data at a single time point. The goal of this study was to investigate the potential improvement in prediction accuracy when incorporating MRI diffusion data from multiple time points during acute phase to improve prediction accuracy. This study was carried out using MRI data from rats subjected to permanent, 60-min and 30-min of middle cerebral artery occlusion (MCAO). The sensitivity and specificity of prediction accuracy were calculated. In the permanent MCAO group, prediction with multiple time-point diffusion data improved sensitivity and specificity compared with prediction using a single time point. In the 60-min MCAO group, multiple time-point analysis improved specificity but decreased sensitivity compared to the single time-point analysis. In the 30-min MCAO group, multiple time-point analysis showed no statistically significant improvement in specificity and sensitivity compared with the single time point analysis. This is because reperfusion transiently or permanently reversed the decline in ADC values, resulting in increased uncertainty and thus decreased prediction accuracy. Incorporating this a priori information could further improve prediction accuracy in the reperfusion group. These findings suggest that incorporating MRI data from multiple time points could improve prediction accuracy under certain ischemic conditions.
    Brain research 04/2012; 1458:86-92. DOI:10.1016/j.brainres.2012.04.004 · 2.83 Impact Factor
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    • "While the OC percent changes may be biased by the low basal T 2 * -weighted signal, it may be more sensitive. Additional studies are needed to determine which analysis method will provide more accurate prediction of final infarct volume (Huang et al., 2011). With improved MRI sensitivity, the ability of basal T 2 * or T 2 weighted MRI to identify salvageable and non-salvageable tissue may be warrant. "
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    ABSTRACT: It has been recently shown that at-risk tissue exhibits exaggerated T(2)⁎-weighted MRI signal increases during transient oxygen challenge (OC), suggesting that the tissue is still metabolically active. This study further characterized the effects of transient OC on T(2)⁎-weighted MRI in permanent focal stroke rats (N=8) using additional quantitative measures. The major findings were: i) the ischemic core cluster showed no significant response, whereas the mismatch cluster showed markedly higher percent changes relative to normal tissue in the acute phase. ii) Many of the mismatch pixels showed exaggerated OC responses which became hyperintense on T(2)-weighted MRI at 24h. The area with exaggerated OC responses was larger than the mismatch, suggesting that some tissue with reduced diffusion were potentially at risk. iii) Basal T(2)⁎-weighted intensities on the perfusion-diffusion contourplot were high in normal tissue and low in the core, with a sharp transition in the mismatch. iv) OC-induced changes on the perfusion-diffusion contourplot dropped as perfusion and diffusion values fell below their respective viability thresholds. v) Basal T(1) increased slightly in the ischemic core (P<0.05). OC decreased T(1) in normal (P<0.05) but not in mismatch and core pixels. vi) OC decreased CBF in normal (P<0.05) but not in mismatch and core pixels. T(2)⁎-weighted MRI of OC has the potential to offer unique clinically relevant data.
    Brain research 11/2011; 1425:132-41. DOI:10.1016/j.brainres.2011.09.052 · 2.83 Impact Factor
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    ABSTRACT: Stroke is the fourth leading cause of death and the leading cause of long-term disability in USA. Brain imaging data from experimental stroke models and stroke patients have shown that there is often a gradual progression of potentially reversible ischemic injury toward infarction. Reestablishing tissue perfusion and/or treating with neuroprotective drugs in a timely fashion are expected to salvage some ischemic tissues. Diffusion-weighted imaging based on magnetic resonance imaging (MRI) in which contrast is based on water motion can detect ischemic injury within minutes after onsets, whereas computed tomography and other imaging modalities fail to detect stroke injury for at least a few hours. Along with quantitative perfusion imaging, the perfusion–diffusion mismatch which approximates the ischemic penumbra could be imaged noninvasively. This review describes recent progresses in the development and application of multimodal MRI and image analysis techniques to study ischemic tissue at risk in experimental stroke in rats.
    03/2011; 3(1). DOI:10.1007/s12975-011-0140-y
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