-
Je něco špatně v tomto záznamu ?
Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery
J. Thomas, C. Abdallah, K. Jaber, M. Khweileh, O. Aron, I. Doležalová, V. Gnatkovsky, D. Mansilla, P. Nevalainen, R. Pana, S. Schuele, J. Singh, A. Suller-Marti, A. Urban, J. Hall, F. Dubeau, L. Maillard, P. Kahane, J. Gotman, B. Frauscher
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
CIHR - Canada
PubMed
39178901
DOI
10.1088/1741-2552/ad7323
Knihovny.cz E-zdroje
- MeSH
- dítě MeSH
- dospělí MeSH
- elektroencefalografie * metody MeSH
- epilepsie * chirurgie diagnóza patofyziologie MeSH
- klinické rozhodování * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- reprodukovatelnost výsledků MeSH
- stereotaktické techniky MeSH
- záchvaty diagnóza chirurgie patofyziologie MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.
Department of Epileptology University Hospital Bonn Bonn Germany
Department of Neurology Duke University Medical Center Durham NC United States of America
Department of Neurology Northwestern University Chicago IL United States of America
Department of Neurology University Hospital of Nancy Lorraine University F 54000 Nancy France
Department of Pediatrics Schulich School of Medicine and Dentistry Western University London Canada
Montreal Neurological Institute and Hospital McGill University Montréal Québec H3A 2B4 Canada
Research Center for Automatic Control of Nancy Lorraine University CNRS UMR 7039 Vandoeuvre France
University of Pittsburgh Comprehensive Epilepsy Center Pittsburgh United States of America
Citace poskytuje Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc25003941
- 003
- CZ-PrNML
- 005
- 20250206104815.0
- 007
- ta
- 008
- 250121s2024 enk f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1088/1741-2552/ad7323 $2 doi
- 035 __
- $a (PubMed)39178901
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a enk
- 100 1_
- $a Thomas, John $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada $u Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States of America $1 https://orcid.org/0000000301443746
- 245 10
- $a Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery / $c J. Thomas, C. Abdallah, K. Jaber, M. Khweileh, O. Aron, I. Doležalová, V. Gnatkovsky, D. Mansilla, P. Nevalainen, R. Pana, S. Schuele, J. Singh, A. Suller-Marti, A. Urban, J. Hall, F. Dubeau, L. Maillard, P. Kahane, J. Gotman, B. Frauscher
- 520 9_
- $a Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.
- 650 _2
- $a lidé $7 D006801
- 650 12
- $a elektroencefalografie $x metody $7 D004569
- 650 _2
- $a mužské pohlaví $7 D008297
- 650 _2
- $a ženské pohlaví $7 D005260
- 650 12
- $a epilepsie $x chirurgie $x diagnóza $x patofyziologie $7 D004827
- 650 _2
- $a dospělí $7 D000328
- 650 12
- $a klinické rozhodování $x metody $7 D000066491
- 650 _2
- $a mladý dospělý $7 D055815
- 650 _2
- $a mladiství $7 D000293
- 650 _2
- $a záchvaty $x diagnóza $x chirurgie $x patofyziologie $7 D012640
- 650 _2
- $a dítě $7 D002648
- 650 _2
- $a stereotaktické techniky $7 D013238
- 650 _2
- $a lidé středního věku $7 D008875
- 650 _2
- $a reprodukovatelnost výsledků $7 D015203
- 655 _2
- $a časopisecké články $7 D016428
- 655 _2
- $a práce podpořená grantem $7 D013485
- 700 1_
- $a Abdallah, Chifaou $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Jaber, Kassem $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada $u Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States of America
- 700 1_
- $a Khweileh, Mays $u Department of Neurology, Duke University Medical Center, Durham, NC, United States of America
- 700 1_
- $a Aron, Olivier $u Department of Neurology, University Hospital of Nancy, Lorraine University, F-54000 Nancy, France $u Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR, 7039 Vandoeuvre, France
- 700 1_
- $a Doležalová, Irena $u Brno Epilepsy Center, First Department of Neurology, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic
- 700 1_
- $a Gnatkovsky, Vadym $u Department of Epileptology, University Hospital Bonn, Bonn, Germany
- 700 1_
- $a Mansilla, Daniel $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Nevalainen, Päivi $u Epilepsia Helsinki, Full member of ERN EpiCare, Department of Clinical Neurophysiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- 700 1_
- $a Pana, Raluca $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Schuele, Stephan $u Department of Neurology, Northwestern University, Chicago, IL, United States of America
- 700 1_
- $a Singh, Jaysingh $u Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States of America
- 700 1_
- $a Suller-Marti, Ana $u Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada $u Department of Pediatrics, Schulich School of Medicine and Dentistry, Western University, London, Canada
- 700 1_
- $a Urban, Alexandra $u University of Pittsburgh Comprehensive Epilepsy Center, Pittsburgh, United States of America
- 700 1_
- $a Hall, Jeffery $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Dubeau, François $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Maillard, Louis $u Department of Neurology, University Hospital of Nancy, Lorraine University, F-54000 Nancy, France $u Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR, 7039 Vandoeuvre, France
- 700 1_
- $a Kahane, Philippe $u Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
- 700 1_
- $a Gotman, Jean $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada
- 700 1_
- $a Frauscher, Birgit $u Montreal Neurological Institute and Hospital, McGill University, Montréal, Québec H3A 2B4, Canada $u Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States of America $u Department of Neurology, Duke University Medical Center, Durham, NC, United States of America $1 https://orcid.org/0000000160641529
- 773 0_
- $w MED00188777 $t Journal of neural engineering $x 1741-2552 $g Roč. 21, č. 5 (2024)
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/39178901 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y - $z 0
- 990 __
- $a 20250121 $b ABA008
- 991 __
- $a 20250206104810 $b ABA008
- 999 __
- $a ok $b bmc $g 2263593 $s 1239948
- BAS __
- $a 3
- BAS __
- $a PreBMC-MEDLINE
- BMC __
- $a 2024 $b 21 $c 5 $e 20241004 $i 1741-2552 $m Journal of neural engineering $n J Neural Eng $x MED00188777
- GRA __
- $p CIHR $2 Canada
- LZP __
- $a Pubmed-20250121
