Evidence map›Paper›PMID 39441414›Full record

ArticleNeuroradiology2025

Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: a 3 Tesla MRI pilot study.

Luigi Vincenzo Pastore, Sniya Valsa Sudhakar, Kshitij Mankad, Enrico De Vita, Asthik Biswas, Martin M Tisdall, Aswin Chari, Matteo Figini, M Zubair Tahir, Sophie Adler and 8 more

Abstract read
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In one paragraph

Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. AI in epilepsy neuroimaging.Current opinion in neurology · 2026
    Review
  5. Article
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Luigi Vincenzo PastoreDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, 40138, Italy. luigiv.pastore@gmail.com.ORCID http://orcid.org/0000-0002-8267-3975
Sniya Valsa SudhakarDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0002-7632-9705
Kshitij MankadDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0001-5979-9337
Enrico De VitaDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0001-8068-1288
Asthik BiswasDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0003-2010-9524
Martin M TisdallDevelopmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK.ORCID http://orcid.org/0000-0001-8880-8386
Aswin ChariDevelopmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK.ORCID http://orcid.org/0000-0003-0053-147X
Matteo FiginiCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.ORCID http://orcid.org/0000-0002-8238-2262
M Zubair TahirDevelopmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK.
Sophie AdlerDevelopmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK.ORCID http://orcid.org/0000-0002-3978-7424
Friederike MoellerDepartment of Neurophysiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0003-2434-2825
J Helen CrossNeurology/Epilepsy Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID http://orcid.org/0000-0001-7345-4829
Suresh PujarNeurology/Epilepsy Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID http://orcid.org/0000-0002-9894-4194
Konrad WagstylCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Mathilde RipartCentre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Ulrike LöbelDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.ORCID http://orcid.org/0000-0001-9844-3464
Luigi CirilloDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, 40138, Italy.ORCID http://orcid.org/0000-0002-0424-3400
Felice D'ArcoDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK.

Funding

Epilepsy Research Institute P2208Rosetrees Trust A2665UKRI Medical Research Council MR/W031566/1
6 · The paper itself

Abstract

backgroundMalformations of cortical development (MCDs) in children with focal epilepsy pose significant diagnostic challenges, and a precise radiological diagnosis is crucial for surgical planning. New MRI sequences and the use of artificial intelligence (AI) algorithms are considered very promising in this regard, yet studies evaluating the relative contribution of each diagnostic technique are lacking.

methodsThe study was conducted using a dedicated "EPI-MCD MR protocol" with a 3 Tesla MRI scanner in patients with focal epilepsy and previously negative MRI. MRI sequences evaluated included 3D FLAIR, 3D T1 MPRAGE, T2 Turbo Spin Echo (TSE), 3D T1 MP2RAGE, and Arterial Spin Labelling (ASL). Two paediatric neuroradiologists scored each sequence for localisation and extension of the lesion. The MELD-FCD AI classifier's performance in identifying pathological findings was also assessed. We only included patients where a diagnosis of MCD was subsequently confirmed on histology and/or sEEG.

resultsThe 3D FLAIR sequence showed the highest yield in detecting epileptogenic lesions, with 3D T1 MPRAGE, T2 TSE, and 3D T1 MP2RAGE sequences showing moderate to low yield. ASL was the least useful. The MELD-FCD classifier achieved a 69.2% true positive rate. In one case, MELD identified a subtle area of cortical dysplasia overlooked by the neuroradiologists, changing the management of the patient.

conclusionsThe 3D FLAIR sequence is the most effective in the MRI-based diagnosis of subtle epileptogenic lesions, outperforming other sequences in localisation and extension. This pilot study emphasizes the importance of careful assessment of the value of additional sequences.

Indexed as

Epilepsies, PartialMagnetic Resonance ImagingMalformations of Cortical DevelopmentAdolescentAlgorithmsArtificial IntelligenceChildChild, PreschoolFemaleHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalMalePilot ProjectsSpin LabelsSpin LabelsASLFocal cortical dysplasiasFocal epilepsyMalformations of cortical developmentMELDMOGHEMP2RAGEPolymicrogyria

Identifiers

PMID39441414

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.