Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
Ann Neurol. 2015 Mar;77(3):436-46. doi: 10.1002/ana.24341. Epub 2015 Jan 13.
In 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.
We 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.
Four 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 92 ± 1% 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.
We 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.
在颞叶癫痫（TLE）中，尽管海马萎缩使病灶侧化，但磁共振成像（MRI）预测手术效果的价值相当有限。仅基于海马的预测可能会受到广泛的内侧颞叶结构损伤的阻碍，这些损伤在先进的成像中显示出来。MRI 指标日益复杂和高维的表示形式促使人们转向机器学习，以建立客观的、数据驱动的致病过程和预后标准。
我们使用源自海马、杏仁核和内嗅皮层表面形态的 1.5T MRI 图谱，对 114 例连续单侧 TLE 患者进行聚类分析。为了评估分类的诊断有效性，我们评估了其在 79 例接受手术治疗的患者中的预测效果。在接受 3.0T MRI 评估的 27 例独立患者队列中，评估了预后预测的可重复性。
我们的队列被分为四个大小相似的类别；总的来说，改变跨越了 3 个内侧颞叶结构。与 46 名对照相比，TLE-I 显示出明显的双侧萎缩；TLE-II 为同侧萎缩；TLE-III 显示出轻度双侧萎缩；而 TLE-IV 则表现为肥大。在组织病理学和无癫痫发作方面，这些类别存在差异。基于表面的分类器可以准确地预测 92%±1%的患者的结局，优于传统的体积测量法。复发的预测因子分布在结构的双侧。在独立队列中，预测的准确性同样很高（96%），支持了其普遍性。
我们提供了 TLE 谱中个体变异性的新描述。类别归属与不同的损伤模式和预后预测因子相关，这些预测因子在空间上没有重叠，这强调了机器学习分离形态对患者表型的差异贡献的能力，最终改善了癫痫手术的预后。
