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Purpose: People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients' computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition., Methods: The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes., Results: The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes., Conclusions: The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by "IRCCS Neuromed" (Pozzilli, Italy), 30th July 2020.

Purpose: The purpose of this study was to report long-term seizure outcome in patients who underwent frontal lobe epilepsy (FLE) surgery., Method: This retrospective study included 44 consecutive subjects who underwent resective surgery for intractable FLE at IRCCS NEUROMED (period 2001-2014), followed up for at least 2 years (mean: 8.7 years). All patients underwent noninvasive presurgical evaluation and/or invasive electroencephalography (EEG) monitoring when nonconcordant data were obtained or epileptogenic zone was hypothesized to be close to the eloquent cortex. Electroclinical, neuroimaging, surgical data, and histology were compared to seizure outcome., Results: Mean epilepsy duration was 19 years; mean age at surgery was 31.6 years. Preoperative magnetic resonance imaging (MRI) showed a frontal lesion in 86.4 % of cases. Scalp video-electroencephalography (VEEG) monitoring detected a focal ictal onset in 90% of cases. Twenty-seven patients (61.4%) underwent invasive recordings. Resections involved dorsolateral (47.7%), medial (9%), orbital (13.6%), and rolandic (13.6%) region. Lobectomy within functional boundaries was performed in the remaining 7 cases (16%). Transient and permanent neurological deficits were observed in 2 and 3 cases, respectively. Histology revealed focal cortical dysplasia (45.5%), World Health Organization (WHO) I-II grade tumors (15.9%), gliosis (22.7%), vascular malformations (4.5%), Rasmussen encephalitis (6.8%), and normal tissue (4.5%). At last observation 68.1% of patients were in Engel's class I, 11.4% in class II, 9% in class III, and 11.4% in class IV. A favorable outcome was associated with focal ictal scalp EEG onset (p = 0.0357)., Conclusion: Surgery is a safe treatment option in drug-resistant FLE with a satisfying long-term outcome. These data highlight the importance of an appropriate selection of potential surgical candidates., (Copyright © 2018 Elsevier Inc. All rights reserved.)