Evidence map›Paper›PMID 39399717›Full record

ReviewTranslational pediatrics2024

Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review.

Luigi Vincenzo Pastore, Enrico De Vita, Sniya Valsa Sudhakar, Ulrike Löbel, Kshitij Mankad, Asthik Biswas, Luigi Cirillo, Suresh Pujar, Felice D'Arco

Abstract readReview
In one paragraph

Review in Translational pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Luigi Vincenzo PastoreDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.ORCID https://orcid.org/0000-0002-8267-3975
Enrico De VitaDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-8068-1288
Sniya Valsa SudhakarDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0002-7632-9705
Ulrike LöbelDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-9844-3464
Kshitij MankadDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-5979-9337
Asthik BiswasDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0003-2010-9524
Luigi CirilloDepartment of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.ORCID https://orcid.org/0000-0002-0424-3400
Suresh PujarNeurology/Epilepsy Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0002-9894-4194
Felice D'ArcoDepartment of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0001-7396-591X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, focusing on advanced MRI sequences, focal epilepsy, and the integration of artificial intelligence (AI) in the diagnostic process. Key Content and Findings: New MRI sequences, including magnetization prepared 2 rapid gradient echo (MP2RAGE), edge-enhancing gradient echo (EDGE), and fluid and white matter suppression (FLAWS), have demonstrated enhanced capabilities in detecting subtle epileptogenic lesions. Quantitative MRI techniques, notably magnetic resonance fingerprinting (MRF), alongside innovative post-processing methods, are emphasized for their effectiveness in delineating cortical malformations, whether used alone or in combination with ultra-high field MRI systems. Furthermore, the integration of AI in radiology is progressing, providing significant support in accurately localizing lesions, and potentially optimizing pre-surgical planning. Conclusions: While advanced neuroimaging and AI offer significant improvements in the diagnostic process for epilepsy, some challenges remain. These include long acquisition times, the need for extensive data analysis, and a lack of large, standardized datasets for AI validation. However, the future holds promise as research continues to integrate these technologies into clinical practice. These efforts will improve the clinical applicability and effectiveness of these advanced techniques in epilepsy management, paving the way for more accurate diagnoses and better patient outcomes.

Indexed as

artificial intelligence (AI)Focal epilepsymalformations of cortical development (MCDs)neuroimagingpaediatric

Identifiers

PMID39399717
PMCPMC11467228

What OpenQuestion holds

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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.