Magnetic Resonance Imaging Pattern Learning in Temporal Lobe Epilepsy: Classification and Prognostics
Magnetic Resonance Imaging Pattern Learning in Temporal Lobe Epilepsy: Classification and Prognostics
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DOI：
10.1002/ana.24341
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发表时间：
2015-03-01
影响因子：
11.2
通讯作者：
Bernasconi, Neda
中科院分区：
文献类型：
--
作者：
Bernhardt, Boris C.;Hong, Seok-Jun;Bernasconi, Neda
在颞叶癫痫（TLE）中，尽管海马萎缩使病灶偏侧化，但磁共振成像（MRI）预测术后结果的价值相当有限。先进的成像技术显示，广泛的近中颞叶结构损伤可能会阻碍单纯基于海马体的预测。越来越复杂和高维表示的MRI指标促使机器学习的转变，建立客观的，数据驱动的标准致病过程和预后MethodsWe应用聚类114个连续的单侧颞叶癫痫患者使用1.5T MRI配置文件来自海马，杏仁核和内嗅皮层的表面形态。为了评估该分类的诊断有效性，我们评估了其预测79例手术治疗患者的结果。在一个独立的队列中的27例患者评估3.0T MRI.ResultsFour类似大小的类划分我们的队列的结果预测的再现进行了评估，在所有的，改变跨越3 mesiotemporal结构。与46例对照组相比，TLE-I显示明显的双侧萎缩; TLE-II萎缩为同侧; TLE-III显示轻度双侧萎缩;而TLE-IV显示肥大。在组织病理学和癫痫发作的自由度方面，类别不同。基于表面的分类器在921%的患者中准确预测了结果，优于传统的容积法。复发的预测因子在结构上呈双侧分布。预测准确率同样高，在独立的队列（96%），支持generalization.InterpretationWe提供了一个新的描述的个体变异性在整个TLE频谱。类别成员关系与不同的损伤模式和结果预测因子相关，这些模式在空间上不重叠，强调机器学习能够解开形态对患者表型的差异贡献，最终改善癫痫手术的预后。《神经病学年鉴》2015;77：436-446
ObjectiveIn temporal lobe epilepsy (TLE), although hippocampal atrophy lateralizes the focus, the value of magnetic resonance imaging (MRI) to predict postsurgical outcome is rather modest. Prediction solely based on the hippocampus may be hampered by widespread mesiotemporal structural damage shown by advanced imaging. Increasingly complex and high-dimensional representation of MRI metrics motivates a shift to machine learning to establish objective, data-driven criteria for pathogenic processes and prognosis.MethodsWe applied clustering to 114 consecutive unilateral TLE patients using 1.5T MRI profiles derived from surface morphology of hippocampus, amygdala, and entorhinal cortex. To evaluate the diagnostic validity of the classification, we assessed its yield to predict outcome in 79 surgically treated patients. Reproducibility of outcome prediction was assessed in an independent cohort of 27 patients evaluated on 3.0T MRI.ResultsFour similarly sized classes partitioned our cohort; in all, alterations spanned over the 3 mesiotemporal structures. Compared to 46 controls, TLE-I showed marked bilateral atrophy; in TLE-II atrophy was ipsilateral; TLE-III showed mild bilateral atrophy; whereas TLE-IV showed hypertrophy. Classes differed with regard to histopathology and freedom from seizures. Classwise surface-based classifiers accurately predicted outcome in 921% of patients, outperforming conventional volumetry. Predictors of relapse were distributed bilaterally across structures. Prediction accuracy was similarly high in the independent cohort (96%), supporting generalizability.InterpretationWe provide a novel description of individual variability across the TLE spectrum. Class membership was associated with distinct patterns of damage and outcome predictors that did not spatially overlap, emphasizing the ability of machine learning to disentangle the differential contribution of morphology to patient phenotypes, ultimately refining the prognosis of epilepsy surgery. Ann Neurol 2015;77:436-446