Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke.
Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke.
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DOI：
10.1371/journal.pone.0022626
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Ewing JR
中科院分区：
文献类型：
--
作者：
Bagher-Ebadian H;Jafari-Khouzani K;Mitsias PD;Lu M;Soltanian-Zadeh H;Chopp M;Ewing JR
在半球缺血性卒中中，缺血性病变的最终大小是临床功能结局最重要的相关因素。使用一组急性期MR图像（弥散加权DWI、T1加权T1WI、T2加权T2WI和质子密度加权PDWI）作为输入，并将3个月时的慢性T2WI作为结局指标，训练人工神经网络（ANN）以逐体素预测慢性T2WI的形式预测3个月结局。使用12名受试者（具有83个切片和140218体素）使用留一法交叉验证方法对ANN进行训练和测试，并计算受试者操作特征曲线（AUROC）下的面积以用于ANN的训练、测试和优化。在训练和优化之后，ANN产生与所有12名患者在3个月时的T2WI良好相关（r = 0.80，p <0.0001）的预测结果的图。 这一结果表明，训练后的人工神经网络可以提供一个稳定和准确的方式（AUROC = 0.89）的T2WI上的3个月的缺血性病变的估计。
In hemispheric ischemic stroke, the final size of the ischemic lesion is the most important correlate of clinical functional outcome. Using a set of acute-phase MR images (Diffusion-weighted - DWI, T1-weighted – T1WI, T2-weighted-T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a voxel-by-voxel forecast of the chronic T2WI. The ANN was trained and tested using 12 subjects (with 83 slices and 140218 voxels) using a leave-one-out cross-validation method with calculation of the Area Under the Receiver Operator Characteristic Curve (AUROC) for training, testing and optimization of the ANN. After training and optimization, the ANN produced maps of predicted outcome that were well correlated (r = 0.80, p<0.0001) with the T2WI at 3 months for all 12 patients. This result implies that the trained ANN can provide an estimate of 3-month ischemic lesion on T2WI in a stable and accurate manner (AUROC = 0.89).
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