Department of Neurology, University of California, LA, USA.
Magn Reson Imaging. 2013 Jul;31(6):961-9. doi: 10.1016/j.mri.2013.03.013. Epub 2013 Apr 13.
Permeability images derived from magnetic resonance (MR) perfusion images are sensitive to blood-brain barrier derangement of the brain tissue and have been shown to correlate with subsequent development of hemorrhagic transformation (HT) in acute ischemic stroke. This paper presents a multi-center retrospective study that evaluates the predictive power in terms of HT of six permeability MRI measures including contrast slope (CS), final contrast (FC), maximum peak bolus concentration (MPB), peak bolus area (PB), relative recirculation (rR), and percentage recovery (%R). Dynamic T2*-weighted perfusion MR images were collected from 263 acute ischemic stroke patients from four medical centers. An essential aspect of this study is to exploit a classifier-based framework to automatically identify predictive patterns in the overall intensity distribution of the permeability maps. The model is based on normalized intensity histograms that are used as input features to the predictive model. Linear and nonlinear predictive models are evaluated using a cross-validation to measure generalization power on new patients and a comparative analysis is provided for the different types of parameters. Results demonstrate that perfusion imaging in acute ischemic stroke can predict HT with an average accuracy of more than 85% using a predictive model based on a nonlinear regression model. Results also indicate that the permeability feature based on the percentage of recovery performs significantly better than the other features. This novel model may be used to refine treatment decisions in acute stroke.
从磁共振（MR）灌注图像中得出的渗透性图像对脑组织的血脑屏障紊乱很敏感，并已被证明与急性缺血性中风后出血性转化（HT）的发展相关。本文介绍了一项多中心回顾性研究，评估了包括对比斜率（CS）、最终对比（FC）、最大峰值浓度（MPB）、峰值浓度区（PB）、相对再循环（rR）和恢复百分比（%R）在内的六种渗透性 MRI 测量值在 HT 方面的预测能力。从四个医疗中心的 263 名急性缺血性中风患者中收集了动态 T2*-加权灌注 MR 图像。本研究的一个重要方面是利用基于分类器的框架自动识别渗透性图整体强度分布中的预测模式。该模型基于归一化强度直方图，用作预测模型的输入特征。使用交叉验证评估线性和非线性预测模型，以衡量新患者的泛化能力，并提供不同类型参数的比较分析。结果表明，使用基于恢复百分比的灌注成像预测急性缺血性中风的 HT 平均准确率超过 85%，使用的是基于非线性回归模型的预测模型。结果还表明，基于恢复百分比的渗透性特征明显优于其他特征。该新型模型可用于细化急性中风的治疗决策。
