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首页> 外文期刊>Epilepsia: Journal of the International League against Epilepsy >Automated detection of focal cortical dysplasia type II II with surface‐based magnetic resonance imaging postprocessing and machine learning
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Automated detection of focal cortical dysplasia type II II with surface‐based magnetic resonance imaging postprocessing and machine learning

机译：具有表面基磁共振成像的局灶性皮质发育不良II II型自动检测，具有后处理和机器学习

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Summary Objective Focal cortical dysplasia ( FCD ) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging ( MRI ) postprocessing methods may provide essential help for detection of FCD . In this study, we utilized surface‐based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. Methods Sixty‐one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1‐volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k ‐fold cross‐validation. Results The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI . Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. Significance Automated surface‐based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy.
机译：发明内容局灶性皮质发育不良（FCD）是接受手术切除治疗药物渗透癫痫的患者的主要病理学。磁共振成像（MRI）后处理方法可以提供对FCD检测的基本帮助。在这项研究中，我们利用了基于表面的MRI形态学和机器学习，以便在3种不同的癫痫中心的FCD II型患者的混合队列中自动化病变检测。方法研究六十一患者癫痫患者和组织学证明FCD II型患者。使用3个不同的MRI扫描仪在3个不同的癫痫中心评估患者。 T1-体积序列用于后处理。普通数据库以120个健康的控制构建。我们还包括35种健康试验控制和15种疾病试验对照，组织学证实的海马硬化以评估特异性。计算并将其结合到非线性神经网络分类器中，该分类器被训练以识别损伤群集。我们通过执行接收器操作特征（ROC）分析来优化来自分类器的输出概率映射的阈值。检测的成功由最终集群和手动标记之间的重叠定义。使用k - 折叠交叉验证评估性能。结果0.9的阈值显示出最佳敏感性为73.7％，特异性为90.0％。 ROC分析曲线下的区域为0.75，这表明判别分类器。不同中心的患者对敏感性和特异性没有显着差异，表明性能的稳健性。初始正常MRI的患者比患者的患者均显着降低了较正的患者。子组分析显示培训组的大小和普通控制数据库受影响的分类器性能。具有机器学习的重要性自动化表面的MRI形态学显示不同中心和扫描仪的群组的强大表现。所提出的方法可以是提高药物渗透剂癫痫患者的预设评估中的FCD检测的有价值的工具。

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