Evidence map›Paper›PMID 39532244›Full record

ReviewMagnetic resonance imaging2025

Detection challenges of temporal encephaloceles in epilepsy: A retrospective analysis.

Alexander V Ortiz, Jarrod J Eisma, Dann Martin, Andre H Lagrange, Cari Motuzas, William Nobis, Bassel W Abou-Khalil, Victoria L Morgan, Jonah Fox

Abstract readReview
In one paragraph

Review in Magnetic resonance imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Alexander V OrtizDepartment of Radiology, Stanford University Medical Center, Stanford, CA, USA. Electronic address: aortiz2@stanford.edu.
Jarrod J EismaDepartment of Chemical and Physical Biology, Vanderbilt University, Nashville, TN, USA.
Dann MartinDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Andre H LagrangeDepartment of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
Cari MotuzasDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
William NobisDepartment of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
Bassel W Abou-KhalilDepartment of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
Victoria L MorganDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
Jonah FoxDepartment of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.

Funding

Overall: Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Center at VanderbiltP50HD103537 · NICHD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jeffrey L Neul · 2020 to 2026
$10.3M
The Role of Network Connectivity in Post-Surgical Seizure Recurrence in Temporal Lobe EpilepsyR01NS110130 · NINDS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MORGAN, VICTORIA L · 2018 to 2023
$2.5M
MRI Connectivity Biomarkers of Treatment Response in Focal EpilepsyR01NS108445 · NINDS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MORGAN, VICTORIA L · 2019 to 2024
$2.5M
NICHD NIH HHS P50 HD103537NINDS NIH HHS R01 NS108445NINDS NIH HHS R01 NS110130
6 · The paper itself

Abstract

Temporal encephaloceles (TEs) are herniations of cerebral parenchyma through structural defects in the floor of the middle cranial fossa. They are a relatively common, but only relatively recently identified potential cause of drug-resistant epilepsy. Uncontrolled epilepsy is associated with many negative long term health consequences including a heightened risk of death. The most effective treatment for drug-resistant epilepsy is surgery. One of the most predictive factors associated with successful surgery is identification of an abnormality on imaging. However, TEs can be difficult to detect and are often overlooked on neuroimaging studies. Improving our ability to accurately detect TEs by MRI is an important step in improving surgical outcomes in patients with drug-resistant epilepsy. We performed a review on existing imaging modalities for detecting TEs and report on our attempt to use a voxel-based morphometry (VBM) algorithm to detect TEs in T1-weighted MRIs of 81 patients from a database comprised of 25 patients with confirmed encephaloceles and 56 controls. Our program's sensitivity and specificity were compared to those of two neuroradiologists and two epileptologists using visualization during surgery as the gold standard. On average, the neuroradiologists and epileptologists had sensitivities of 41 % and 58 % and specificities of 81 % and 60 % while our VBM-based approach had sensitivities and specificities ranging from 11 % to 50 % and 0.2 % to 17 %, respectively. This work provides an overview of the different imaging modalities utilized in the detection of TEs and highlights the difficulties associated with their detection for both experienced physicians and cutting-edge computational methods. Our findings suggest that VBM-based methods could potentially be used to enhance clinicians' ability to detect TEs thereby facilitating surgical planning, improving surgical outcomes by allowing for more specific targeting, and bettering the long-term health and well-being of patients with drug-resistant epilepsy secondary to TEs.

Indexed as

Drug Resistant EpilepsyEncephaloceleAdolescentAdultAlgorithmsFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityYoung AdultEncephaloceleEpilepsyMorphometryRefractory epilepsyTemporal encephaloceleVoxel-based

Identifiers

PMID39532244
PMCPMC11614672

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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.