2021
DOI: 10.1101/2021.02.01.21250734
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

MELD Project: Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia

Abstract: Background Drug-resistant focal epilepsy is often caused by focal cortical dysplasia (FCD). The impact of FCD location on clinical presentation and surgical outcome is largely unknown. We created a large neuroimaging cohort of patients with individually mapped FCDs to determine predictors of lesion location and postsurgical seizure freedom to aid presurgical decision-making. Methods The Multi-centre Epilepsy Lesion Detection (MELD) project collated a retrospective cohort of 580 patients with epilepsy due to F… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
1
1
1

Relationship

2
1

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 28 publications
0
2
0
Order By: Relevance
“…The first step to further generalise the applicability would be to construct a large multicentre data set that accounts for variability in subjects, scan strength and protocols across centres. This is currently being constructed as part of the MELD study 19…”
Section: Introductionmentioning
confidence: 99%
“…The first step to further generalise the applicability would be to construct a large multicentre data set that accounts for variability in subjects, scan strength and protocols across centres. This is currently being constructed as part of the MELD study 19…”
Section: Introductionmentioning
confidence: 99%
“…Here, as part of the MELD Project (Wagstyl et al, 2021), we aimed to collate a cohort of patients from multiple epilepsy surgery centres, across multiple MRI scanners including both 1.5T and 3T field strengths; create protocols for de-centralised MRI post-processing; and develop an open-access, robust and interpretable surface-based classifier to detect FCD.…”
Section: Introductionmentioning
confidence: 99%