MAST: MELD as an Adjunct for SEEG Trajectories
Study Details
Study Description
Brief Summary
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
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Detailed Description
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: MELD-assisted SEEG trajectory planning Following routine clinical planning, the MELD algorithm will be run on the enrolled patient's scans. Up to 3 extra electrodes may be used to target lesion clusters identified by the algorithm such that the investigators will record from the top 3 clusters, with the aim of improving the rate of identification of a focal seizure onset zone in patients undergoing SEEG. |
Procedure: MELD algorithm use to aid in the planning of SEEG electrode trajectories
During the routine SEEG planning meetings, the planning of SEEG trajectories, including the number and location of electrodes, will follow the usual clinical pathway and be planned according to the expertise of the attending neurosurgeon, neurophysiologist and neurologist at the multidisciplinary team meeting. Once the trajectories have been planned, anonymised scans for each patient (linked to them via a unique study ID) will be run through the MELD classifier and the top 3 MELD identified lesion clusters will be considered for further implantation.
These top 3 MELD classifier identified clusters will then be merged with the existing clinical plan to assess if each of the clusters are already being sampled by an SEEG electrode. If there is already an electrode in each lesion, no adjustments will be made. If there are clusters that are not being recorded from, and it is technically possible, extra electrodes (up to 3) will be added to record from these additional locations.
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Outcome Measures
Primary Outcome Measures
- Additional contacts in neurophysiologically defined seizure onset zone [Baseline (During inpatient admission)]
For each patient, the investigators will assess whether any of the additional electrodes (added as part of the trial to record from detected lesions) were in the neurophysiologically (SEEG) defined seizure onset zone. This will be a dichotomous yes/no outcome for each patient.
Secondary Outcome Measures
- Pre-implantation confidence [Baseline (During inpatient admission)]
Pre-implantation confidence of the MDT members in identifying a seizure onset zone (prior to MELD information) ie. a measure of the difficulty of the SEEG exploration on Likert scale 0-10
- Number of electrodes added [Baseline (During inpatient admission)]
Simple number
- Number of electrodes added [Baseline (During inpatient admission)]
Number of electrodes already in identified lesions
- Was a MELD-identified lesion part of the SOZ (and if so how many?) [Baseline (During inpatient admission)]
Yes/no and simple numerical outcome
- Would the SOZ have been identified without MELD? [Baseline (During inpatient admission)]
Yes/No outcome
- Blinded neurophysiological assessment of the SOZ contacts with and without additional electrodes [Baseline (During inpatient admission)]
Description of contacts in SOZ with and without additional electrodes
- Putative resection boundaries with and without the additional electrodes, to be modelled by a neurosurgeon ie. a measure of whether or not this would have changed subsequent surgical strategy [Baseline (During inpatient admission)]
Description of resection and how it may have changes
- adverse events such as bleeding [Baseline (During inpatient admission)]
Safety of adding additional electrodes
Eligibility Criteria
Criteria
Inclusion Criteria:
- Patients aged 3-18 undergoing SEEG recording as part of the investigation of their epilepsy at Great Ormond Street Hospital for Children.
Exclusion Criteria:
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Tuberous sclerosis
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Prior resective epilepsy surgery
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Insufficient imaging datasets for the algorithm
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Lack of informed consent
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Great Ormond Street Hospital NHS Foundation Trust | London | United Kingdom | WC1N 3JH |
Sponsors and Collaborators
- Great Ormond Street Hospital for Children NHS Foundation Trust
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 19NI10