Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.
Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.
复制标题
利用基于表面的磁共振成像后处理和机器学习自动检测 II 型局灶性皮质发育不良
作者：
Jin B;Krishnan B;Adler S;Wagstyl K;Hu W;Jones S;Najm I;Alexopoulos A;Zhang K;Zhang J;Ding M;Wang S;Pediatric Imaging, Neurocognition, and Genetics Study;Wang ZI
局灶性皮质发育不良(FCD)是手术切除治疗耐药癫痫患者的主要病理改变。MRI后处理方法可能为FCD的检出提供必要的帮助。在这项研究中，我们利用基于表面的MRI形态测量和机器学习在来自三个不同癫痫中心的II型FCD患者的混合队列中进行自动病变检测。61例耐药癫痫和组织学证实的FCD II型患者被纳入研究。这些患者在三个不同的癫痫中心使用三种不同的MRI扫描仪进行了评估。后处理采用T1-体积序列。以120名健康对照为对照，建立正常对照数据库。我们还包括35名健康对照和15名疾病对照，以评估其特异性。计算特征并将其结合到非线性神经网络分类器中，训练该分类器来识别病变聚类。通过ROC分析，优化了分类器输出概率图的阈值。检测的成功通过最终聚类和手动标记之间的重叠来定义。使用k-折交叉验证来评估性能。以0.9为临界值，其最佳灵敏度为73.7%，特异度为90.0%。ROC分析的曲线下面积为0.75，这表明这是一个判别性分类器。来自不同中心的患者的敏感度和特异度没有显著差异，这表明操作的稳健性。MRI最初正常的患者的正确检测率明显低于MRI明确阳性的患者。子组分析表明，训练组的大小和正常对照数据库的大小影响分类器的性能。配备了机器学习的基于表面的自动MRI形态测量显示，来自不同中心和扫描仪的队列表现强劲。所提出的方法可能是一种有价值的工具，以改进FCD检测在药物耐药癫痫患者的术前评估中。
Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. MRI post-processing 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 three different epilepsy centers. Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at three different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for post-processing. 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 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. 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 training group and normal control database impacted classifier performance. 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.
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影响因子：
3.7
作者：
Thesen T;Quinn BT;Carlson C;Devinsky O;DuBois J;McDonald CR;French J;Leventer R;Felsovalyi O;Wang X;Halgren E;Kuzniecky R
通讯作者：
Kuzniecky R
影响因子：
9.9
作者：
Hong, Seok-Jun;Kim, Hosung;Bernasconi, Andrea
通讯作者：
Bernasconi, Andrea
影响因子：
14.5
作者：
Wagner, Jan;Weber, Bernd;Huppertz, Hans-Juergen
通讯作者：
Huppertz, Hans-Juergen
影响因子：
2
作者：
Cardinale, Francesco;Francione, Stefano;Colombo, Nadia
通讯作者：
Colombo, Nadia
影响因子：
4.3
作者：
Weiskopf N;Suckling J;Williams G;Correia MM;Inkster B;Tait R;Ooi C;Bullmore ET;Lutti A
通讯作者：
Lutti A