Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.
Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.
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DOI：
10.1016/j.nicl.2015.07.010
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Tracy J
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文献类型：
--
作者：
Pustina D;Avants B;Sperling M;Gorniak R;He X;Doucet G;Barnett P;Mintzer S;Sharan A;Tracy J
对颞叶癫痫(TLE)患者的术前评估依赖于从多种神经成像方式获得的信息。目前尚不清楚模式和它们在预测癫痫发作焦点方面的综合能力之间的关系。我们研究了28例左侧TLE患者和30例右侧TLE患者(LTLE和RTLE)三种不同方式的不对称性，包括PET(葡萄糖代谢)、MRI(皮质厚度)和弥散张量成像(DTI；白质各向异性)。每个模型分别建立逐步Logistic回归模型和三种模型的组合模型，Bootstrap方法和分裂样本验证验证了预测的稳健性。在所有的多模式不对称性中，三个PET不对称性形成了最好的预测模型(在全样本中100%成功，在分裂样本验证中95%成功)。PET与其他方式的联合应用并不比单独使用PET效果更好。对新的临床病例进行概率分类，正确定侧7/7新发TLE患者(100%)和4/5手术后PET报告不一致或不具信息性的患者(80%)。LTLE的代谢与白质关系密切，RTLE的代谢与灰质关系密切。我们的数据表明，代谢是一种强大的模式，可以高精度地预测癫痫的偏侧性，并为自动化预测模型提供了很高的价值。致痫灶一侧可影响代谢与脑结构的关系。获得新的TLE患者分类所需的数据和工具已公开提供。我们的目的是从多通道不对称性预测癫痫发作的偏侧性。使用了葡萄糖代谢、皮质厚度和各向异性分数。代谢是唯一最能预测癫痫发作的方式。左侧TLE存在更密切的分数各向异性-代谢关系。我们用概率模型提供了预测成功的临床例子。
Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100% success in full sample, >95% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100%) and for 4/5 operated patients with discordant or non-informative PET reports (80%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available. We aimed at predicting seizure onset laterality from multiple modality asymmetries. Glucose metabolism, cortical thickness, and fractional anisotropy were used. Metabolism was the single most predictive modality to lateralize seizure onset. Closer fractional anisotropy–metabolism relationship was found in left TLE. We provide clinical examples of predictive success with a probabilistic model.