Birmingham Children's Hospital, Steelhouse Lane, Birmingham B4 6NH, UK.
Birmingham Children's Hospital, Steelhouse Lane, Birmingham B4 6NH, UK.
Epilepsy Res. 2024 Oct;206:107429. doi: 10.1016/j.eplepsyres.2024.107429. Epub 2024 Aug 6.
Paediatric patients with drug-resistant focal epilepsy (DRFE) who have no clear focal lesion identified on conventional structural magnetic resonance imaging (MRI) are a particularly challenging cohort to treat and form an increasing part of epilepsy surgery programs. A recently developed deep-learning-based MRI lesion detection algorithm, the Multicentre Lesion Detection (MELD) algorithm, has been shown to aid detection of focal cortical dysplasia (FCD). We applied this algorithm retrospectively to a cohort of MRI-negative children with refractory focal epilepsy who underwent stereoelectroencephalography (SEEG) to determine its accuracy in identifying unseen epileptic lesions, seizure onset zones and clinical outcomes.
We retrospectively applied the MELD algorithm to a consecutive series of MRI-negative patients who underwent SEEG at our tertiary Paediatric Epilepsy Surgery centre. We assessed the extent to which the identified MELD cluster or lesion area corresponded with the clinical seizure hypothesis, the epileptic network, and the positron emission tomography (PET) focal hypometabolic area. In those who underwent resective surgery, we analysed whether the region of MELD abnormality corresponded with the surgical target and to what extent this was associated with seizure freedom.
We identified 37 SEEG studies in 28 MRI-negative children in whom we could run the MELD algorithm. Of these, 14 (50 %) children had clusters identified on MELD. Nine (32 %) children had clusters concordant with seizure hypothesis, 6 (21 %) had clusters concordant with PET imaging, and 5 (18 %) children had at least one cluster concordant with SEEG electrode placement. Overall, 4 MELD clusters in 4 separate children correctly predicted either seizure onset zone or irritative zone based on SEEG stimulation data. Sixteen children (57 %) went on to have resective or lesional surgery. Of these, only one patient (4 %) had a MELD cluster which co-localised with the resection cavity and this child had an Engel 1 A outcome.
In our paediatric cohort of MRI-negative patients with drug-resistant focal epilepsy, the MELD algorithm identified abnormal clusters or lesions in half of cases, and identified one radiologically occult focal cortical dysplasia. Machine-learning-based lesion detection is a promising area of research with the potential to improve seizure outcomes in this challenging cohort of radiologically occult FCD cases. However, its application should be approached with caution, especially with regards to its specificity in detecting FCD lesions, and there is still work to be done before it adds to diagnostic utility.
对于在常规结构磁共振成像（MRI）上未发现明确局灶性病变的耐药性局灶性癫痫（DRFE）儿科患者，治疗是一项极具挑战性的任务，且这些患者在癫痫手术项目中所占比例不断增加。最近开发的基于深度学习的 MRI 病变检测算法——多中心病变检测（MELD）算法，已被证明有助于检测局灶性皮质发育不良（FCD）。我们将该算法应用于一组接受立体脑电图（SEEG）检查的难治性局灶性癫痫且 MRI 阴性的儿童患者中，以确定其识别未发现的癫痫病变、发作起始区和临床结果的准确性。
我们回顾性地将 MELD 算法应用于在我们的三级儿科癫痫手术中心接受 SEEG 检查的连续系列 MRI 阴性患者中。我们评估了识别出的 MELD 簇或病变区域与临床癫痫假说、癫痫网络和正电子发射断层扫描（PET）局灶性低代谢区域的吻合程度。对于接受切除术的患者，我们分析了 MELD 异常区域与手术靶点的吻合程度，以及该吻合程度与癫痫无发作之间的关系。
我们在 28 名 MRI 阴性的儿童中识别出 37 项 SEEG 研究，其中 14 名（50%）儿童的 MELD 算法可以识别出簇。9 名（32%）儿童的簇与癫痫假说一致，6 名（21%）儿童的簇与 PET 成像一致，5 名（18%）儿童的簇至少有一个与 SEEG 电极放置一致。总体而言，基于 SEEG 刺激数据，MELD 算法在 4 名不同儿童的 4 个簇中正确预测了发作起始区或刺激性区。16 名儿童（57%）接受了切除术或病灶切除术。其中，只有一名患者（4%）的 MELD 簇与切除腔共定位，该患儿的癫痫无发作 1A 级。
在我们的 MRI 阴性耐药性局灶性癫痫儿科患者队列中，MELD 算法在一半的病例中识别出异常簇或病变，并识别出一个影像学隐匿性局灶性皮质发育不良。基于机器学习的病变检测是一个很有前途的研究领域，有可能改善这一具有挑战性的影像学隐匿性 FCD 病例组的癫痫发作结果。然而，在其增加诊断效用之前，其应用应谨慎进行，特别是在检测 FCD 病变的特异性方面，仍有许多工作要做。
