Department of Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany.
Neuroimage. 2012 Jan 2;59(1):356-62. doi: 10.1016/j.neuroimage.2011.07.068. Epub 2011 Jul 30.
In those with drug refractory focal epilepsy, MR imaging is important for identifying structural causes of seizures that may be amenable to surgical treatment. In up to 25% of potential surgical candidates, however, MRI is reported as unremarkable even when employing epilepsy specific sequences. Automated MRI classification is a desirable tool to augment the interpretation of images, especially when changes are subtle or distributed and may be missed on visual inspection. Support vector machines (SVM) have recently been described to be useful for voxel-based MR image classification. In the present study we sought to evaluate whether this method is feasible in temporal lobe epilepsy, with adequate accuracy. We studied 38 patients with hippocampal sclerosis and unilateral (mesial) temporal lobe epilepsy (mTLE) (20 left) undergoing presurgical evaluation and 22 neurologically normal control subjects. 3D T1-weighted images were acquired at 3T (GE Excite), segmented into tissue classes, normalized and smoothed with SPM8. Diffusion tensor imaging (DTI) and double echo images for T2 relaxometry were also acquired and processed. The SVM analysis was done with the libsvm software package in a leave-one-out cross-validation design and predictive accuracy was measured. Local weighting was applied by SPM F-contrast maps. Best accuracies were achieved using the gray matter based segmentation (90-100%) and mean diffusivity (95-97%). For the three-way classification, accuracies were 88 and 93% respectively. Local weighting generally improved the accuracies except in the FA-based processing for which no effect was noted. Removing the hippocampus from the analysis, on the other hand, reduced the obtainable diagnostic indices but these were still >90% for DTI-based methods and lateralization based on gray matter maps. These findings show that automated SVM image classification can achieve high diagnostic accuracy in mTLE and that voxel-based MRI can be used at the individual subject level. This could be helpful for screening assessments of MRI scans in patients with epilepsy and when no lesion is detected on visual evaluation.
在药物难治性局灶性癫痫患者中，磁共振成像对于确定可能适合手术治疗的癫痫结构性病因非常重要。然而，在多达 25%的潜在手术候选者中，即使使用特定于癫痫的序列，磁共振成像也被报告为无明显异常。自动磁共振成像分类是一种有用的工具，可以增强图像的解释，特别是当变化细微或分布广泛，可能在视觉检查中被忽略时。支持向量机（SVM）最近被描述为用于基于体素的磁共振图像分类的有用方法。在本研究中，我们试图评估该方法在颞叶癫痫中的准确性是否足够。我们研究了 38 例患有海马硬化和单侧（内侧）颞叶癫痫（mTLE）（20 例左侧）的患者，这些患者正在接受术前评估，以及 22 例神经正常的对照者。在 3T（GE Excite）上采集 3D T1 加权图像，分割成组织类别，通过 SPM8 进行归一化和平滑处理。还采集并处理弥散张量成像（DTI）和双回波图像进行 T2 弛豫率测量。SVM 分析是在 leave-one-out 交叉验证设计中使用 libsvm 软件包进行的，并测量预测准确性。局部加权由 SPM F-对比图完成。基于灰质的分割（90-100%）和平均弥散度（95-97%）获得最佳准确性。对于三分类，准确率分别为 88%和 93%。局部加权通常可以提高准确性，除了在基于 FA 的处理中没有效果。另一方面，从分析中去除海马体降低了可获得的诊断指数，但对于基于 DTI 的方法和基于灰质图的侧化，这些指数仍>90%。这些发现表明，自动 SVM 图像分类可以在 mTLE 中实现高诊断准确性，并且基于体素的 MRI 可以在个体水平上使用。这可能有助于筛选癫痫患者的 MRI 扫描评估，以及在视觉评估中未发现病变时。
