MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy
MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy
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DOI：
10.1212/wnl.0000000000012699
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发表时间：
2021-10-19
期刊：
影响因子：
9.9
通讯作者：
Bernasconi, Neda
中科院分区：
文献类型：
--
作者：
Caldairou, Benoit;Foit, Niels A.;Bernasconi, Neda
背景与目的：在接受颞叶癫痫(TLE)手术的患者中，有30%至50%的患者MRI未能显示出海马区的病理改变。为了解决这一临床挑战，我们开发了一种基于MRI的自动分类器，它可以对TLE中隐蔽的海马区病理进行侧向分类。方法我们训练了一个基于表面的线性判别分类器，该分类器使用T1加权(形态学)和T2加权和流体衰减反转恢复(FLAIR)/T1(强度)特征。该分类器用于60例TLE患者(平均年龄35.6岁，其中58%为女性)，经组织学证实为海马硬化症(HS)。根据神经放射学读数，42%的病例认为MRI阴性(根据海马体积测量，40%)。预测模型自动将患者标记为患有左侧或右侧TLE。将定侧准确性与电临床数据(包括手术侧)进行比较。在两个人口学和电临床特征相似的独立TLE队列中进一步评估了分类器的准确性(n=57，58%MRI阴性)。结果无论HS能见度如何，总的定侧准确率为93%(95%可信区间为92%~94%)。在MRI阴性的TLE中，T2和FLAIR/T1信号的组合在训练(84%，曲线下面积[AUC]0.95+/-0.02)和验证(队列1.90%，AUC 0.99；队列276%，AUC 0.94)队列中都提供了最高的准确性。讨论TLE偏侧化的预测模型建立在现成的常规MRI对比图上，其准确性高于视觉放射学评估。减少的T1加权信号和增加的T2加权信号的共同作用使合成FLAIR/T1对比剂在MRI阴性HS中特别有效，为广泛的临床转换奠定了基础。证据分类这项研究提供了第二类证据，证明在患有TLE和MRI阴性HS的患者中，基于MRI的自动分类器可以准确地确定病理的一侧。
Background and Objectives MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE. Methods We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative). Results The overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 +/- 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. Discussion This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. Classification of Evidence This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.