MELD as an Adjunct for SEEG Trajectories

Overview

Epilepsy is a disorder of the brain which is associated with disabling seizures and affects 100,000 people under 25. Many children with epilepsy also have a learning disability or problems with development. Although better outcomes occur in children who are successfully treated early for their epilepsy, 25% continue to have seizures despite best medical treatment. One potential treatment is a neurosurgical operation to remove parts of the brain that generate seizures. A proportion of these children have electrodes inserted into their brains as part of their clinical assessment, termed stereoelectroencephalography (SEEG), to help localise these regions. Subsequent surgery is not always successful - up to 40% of children will have ongoing seizures 5 years after surgery. The planning of where to place SEEG electrodes relies on experts (neurologists, neurophysiologists and neurosurgeons) using information from multiple sources, which are used to generate hypotheses about where the seizures are coming from. The main components are the patient's magnetic resonance imaging (MRI) scan and video-electroencephalography (EEG) recordings during seizures. Using this information, between 5-18 electrodes are implanted and the recordings continue for 5-15 days in hospital. A focus is identified in about 75% of cases which means that the focus is sometimes missed. This prospective single arm pilot study aims to assess a new automated lesion detection algorithm, MELD, designed to identify focal cortical dysplasias (the most common pathology associated with focal epilepsy in children) on otherwise 'normal' MRI scans. The investigators will assess whether MELD can be used to improve the targeting of abnormalities in children undergoing SEEG recording at Great Ormond Street Hospital

Epilepsy is a disorder of the brain that is associated with disabling seizures. It affects 100,000 children in the UK, 25-30% of whom will be classed as drug resistant.3 In these children, there is increasing evidence that resective epilepsy surgery in appropriate candidates can lead to seizure freedom and improve quality of life and cognitive outcomes.4-6 However, about 30% of children do not achieve seizure freedom following epilepsy surgery and data suggests that these figures are not improving over time despite increasing use of intracranial evaluation via stereoelectroencephalography (SEEG). 7 The planning of where to place SEEG electrodes currently relies on an expert multidisciplinary team consisting of neurologists, neurophysiologists and neurosurgeons. Information from multiple sources, mainly the patient's magnetic resonance imaging (MRI) scan and video-electroencephalography (EEG) recording, are used to generate hypotheses about the location of the clinical seizure onset zone (SOZ). Using this information, between 5-18 electrodes are implanted and the recordings continue for 5-15 days in hospital. In a retrospective review of 75 SEEG cases, a focus was identified in about 77% of cases which means that the focus is sometimes missed. This prospective single arm pilot study to aims assess a new automated lesion detection algorithm, MELD, designed to identify focal cortical dysplasias (the most common pathology associated with focal epilepsy in children) on otherwise 'normal' MRI scans.1 This algorithm was developed in-house by collaborators in this grant application. In our subsequent retrospective study of 34 SEEG patients, the algorithm colocalised with the SEEG-defined SOZ in 62% of all patients with a cortical SOZ and 86% of all patients with a histologically confirmed focal cortical dysplasia.2 Importantly, there were 3 patients whose SOZ was thought to be missed on SEEG who had MELD-identified lesions that were not implanted. In order to improve the algorithm, investigators have subsequently launched an international multicentre collaboration (https://meldproject.github.io//) to increase the number of lesion positive and control scans available to train the algorithm, improving its sensitivity, specificity and accuracy. This project has gathered over 550 lesional and 350 control scans, which will be used to train the algorithm. The prospective MAST Trial is therefore the ideal next step in the evaluating the utility of the MELD algorithm in identifying abnormal areas of the brain that could be responsible for seizures.