Automated detection of cortical dysplasia type II in MRI-negative epilepsy
Automated detection of cortical dysplasia type II in MRI-negative epilepsy
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DOI：
10.1212/wnl.0000000000000543
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发表时间：
2014-07-01
期刊：
影响因子：
9.9
通讯作者：
Bernasconi, Andrea
中科院分区：
文献类型：
--
作者：
Hong, Seok-Jun;Kim, Hosung;Bernasconi, Andrea
目的：自动检测局灶性皮质发育不良（FCD）II型颞外癫痫患者最初诊断为MRI阴性的常规检查的1.5和3.0T scannes.Methods：我们实现了一个自动分类器依赖于基于表面的功能FCD的形态和强度，利用他们的协方差。该方法在19例患者（15例经组织学证实的FCD）上进行了测试，在3.0T下进行扫描，并使用留一策略进行交叉验证。我们评估了24名健康对照和11名颞叶癫痫疾病对照的特异性。在20名健康对照和14名在1.5T下检查的经组织学证实的FCD患者中评估了交叉数据集分类性能。在健康或疾病对照中未检测到损伤）。在50%的病例中，单个簇与FCD病变共定位，而在其余病例中，发现中值为1个病变外簇。（在3.0T数据上训练）与1.5T数据集的性能相当（敏感性71%，特异性95%）。在最初诊断为MRI阴性的患者中，我们的全自动多变量方法提供了比标准放射学评估更高的灵敏度。所提出的方法显示了跨队列，扫描仪和场强的普遍性。机器学习可以通过促进关于致痫区的假设制定来辅助术前决策。证据分类：本研究提供了II类证据，证明MRI模式的自动机器学习可以准确识别最初诊断为MRI阴性的颞外癫痫患者中的FCD。
Objective: To detect automatically focal cortical dysplasia (FCD) type II in patients with extratemporal epilepsy initially diagnosed as MRI-negative on routine inspection of 1.5 and 3.0T scans.Methods: We implemented an automated classifier relying on surface-based features of FCD morphology and intensity, taking advantage of their covariance. The method was tested on 19 patients (15 with histologically confirmed FCD) scanned at 3.0T, and cross-validated using a leave-one-out strategy. We assessed specificity in 24 healthy controls and 11 disease controls with temporal lobe epilepsy. Cross-dataset classification performance was evaluated in 20 healthy controls and 14 patients with histologically verified FCD examined at 1.5T.Results: Sensitivity was 74%, with 100% specificity (i.e., no lesions detected in healthy or disease controls). In 50% of cases, a single cluster colocalized with the FCD lesion, while in the remaining cases a median of 1 extralesional cluster was found. Applying the classifier (trained on 3.0T data) to the 1.5T dataset yielded comparable performance (sensitivity 71%, specificity 95%).Conclusion: In patients initially diagnosed as MRI-negative, our fully automated multivariate approach offered a substantial gain in sensitivity over standard radiologic assessment. The proposed method showed generalizability across cohorts, scanners, and field strengths. Machine learning may assist presurgical decision-making by facilitating hypothesis formulation about the epileptogenic zone.Classification of evidence: This study provides Class II evidence that automated machine learning of MRI patterns accurately identifies FCD among patients with extratemporal epilepsy initially diagnosed as MRI-negative.