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

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....

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Autores principales: Kerr, Wesley T., Nguyen, Stefan T., Cho, Andrew Y., Lau, Edward P., Silverman, Daniel H., Douglas, Pamela K., Reddy, Navya M., Anderson, Ariana, Bramen, Jennifer, Salamon, Noriko, Stern, John M., Cohen, Mark S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615243/
https://www.ncbi.nlm.nih.gov/pubmed/23565107
http://dx.doi.org/10.3389/fneur.2013.00031
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author Kerr, Wesley T.
Nguyen, Stefan T.
Cho, Andrew Y.
Lau, Edward P.
Silverman, Daniel H.
Douglas, Pamela K.
Reddy, Navya M.
Anderson, Ariana
Bramen, Jennifer
Salamon, Noriko
Stern, John M.
Cohen, Mark S.
author_facet Kerr, Wesley T.
Nguyen, Stefan T.
Cho, Andrew Y.
Lau, Edward P.
Silverman, Daniel H.
Douglas, Pamela K.
Reddy, Navya M.
Anderson, Ariana
Bramen, Jennifer
Salamon, Noriko
Stern, John M.
Cohen, Mark S.
author_sort Kerr, Wesley T.
collection PubMed
description 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.
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spelling pubmed-36152432013-04-05 Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET Kerr, Wesley T. Nguyen, Stefan T. Cho, Andrew Y. Lau, Edward P. Silverman, Daniel H. Douglas, Pamela K. Reddy, Navya M. Anderson, Ariana Bramen, Jennifer Salamon, Noriko Stern, John M. Cohen, Mark S. Front Neurol Neuroscience 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. Frontiers Media S.A. 2013-04-03 /pmc/articles/PMC3615243/ /pubmed/23565107 http://dx.doi.org/10.3389/fneur.2013.00031 Text en Copyright © 2013 Kerr, Nguyen, Cho, Lau, Silverman, Douglas, Reddy, Anderson, Bramen, Salamon, Stern and Cohen. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Neuroscience
Kerr, Wesley T.
Nguyen, Stefan T.
Cho, Andrew Y.
Lau, Edward P.
Silverman, Daniel H.
Douglas, Pamela K.
Reddy, Navya M.
Anderson, Ariana
Bramen, Jennifer
Salamon, Noriko
Stern, John M.
Cohen, Mark S.
Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title_full Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title_fullStr Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title_full_unstemmed Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title_short Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET
title_sort computer-aided diagnosis and localization of lateralized temporal lobe epilepsy using interictal fdg-pet
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615243/
https://www.ncbi.nlm.nih.gov/pubmed/23565107
http://dx.doi.org/10.3389/fneur.2013.00031
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