Evidence map›Paper›PMID 23565107›Full record

ArticleFrontiers in neurology2013

Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET.

Wesley T Kerr, Stefan T Nguyen, Andrew Y Cho, Edward P Lau, Daniel H Silverman, Pamela K Douglas, Navya M Reddy, Ariana Anderson, Jennifer Bramen, Noriko Salamon and 2 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. [Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021
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  17. Quantitative Research ofFrontiers in neuroscience · 2019
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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

12 authors.

Wesley T KerrDepartment of Biomathematics, David Geffen School of Medicine, University of California Los Angeles Los Angeles, CA, USA ; Laboratory of Integrative Neuroimaging Technology, Department of Psychiatry, Neuropsychiatric Institute, University of California Los Angeles Los Angeles, CA, USA.
Stefan T Nguyen
Andrew Y Cho
Edward P Lau
Daniel H Silverman
Pamela K Douglas
Navya M Reddy
Ariana Anderson
Jennifer Bramen
Noriko Salamon
John M Stern
Mark S Cohen

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Systems in Integrative BiologyT32GM008185 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHOU, TOM · 1987 to 2022
$4.6M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsR90DA023422 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.7M
Real-Time Automated Detection of Craving States with fMRI and EEGR33DA026109 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2010 to 2012
$1.7M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsT90DA022768 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.5M
NIDA NIH HHS R33 DA026109NIDA NIH HHS R90 DA023422NIDA NIH HHS T90 DA022768NIGMS NIH HHS T32 GM008042NIGMS NIH HHS T32 GM008185
6 · The paper itself

Abstract

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69-90%] or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement - not replace - expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

Indexed as

computer-aided diagnosisepilepsyfluoro-deoxyglucose positron emission tomographymachine learningmutual informationPETtemporal lobe epilepsy

Identifiers

PMID23565107
PMCPMC3615243

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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.