Automated fusion of multimodal imaging data for identifying
epileptogenic lesions in patients with inconclusive magnetic
resonance imaging

J 2021

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

MAREČEK, Radek, Pavel ŘÍHA, Michaela BARTOŇOVÁ, Martin KOJAN, Martin LAMOŠ et. al.

Základní údaje

Originální název

Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging

Autoři

MAREČEK, Radek (203 Česká republika, domácí), Pavel ŘÍHA (203 Česká republika, domácí), Michaela BARTOŇOVÁ (703 Slovensko, domácí), Martin KOJAN (703 Slovensko, domácí), Martin LAMOŠ (203 Česká republika, domácí), Martin GAJDOŠ (203 Česká republika, domácí), Lubomír VOJTÍŠEK (203 Česká republika, domácí), Michal MIKL (203 Česká republika, domácí), Marek BARTOŇ (203 Česká republika, domácí), Irena DOLEŽALOVÁ (203 Česká republika, domácí), Martin PAIL (203 Česká republika, domácí), Ondřej STRÝČEK (203 Česká republika, domácí), Marta PAŽOURKOVÁ (203 Česká republika), Milan BRÁZDIL (203 Česká republika, domácí) a Ivan REKTOR (203 Česká republika, garant, domácí)

Vydání

Human Brain mapping, Hoboken, WILEY-BLACKWELL, 2021, 1065-9471

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.399

Kód RIV

RIV/00216224:14740/21:00120107

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.1002/hbm.25413

UT WoS

000633522300001

Klíčová slova anglicky

data fusion; neuroimaging; nonlesional epilepsy; seizure onset zone

Štítky

14110127, CF MAFIL, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 10. 2024 09:48, Mgr. Adéla Pešková

Anotace

V originále

Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.

Návaznosti

LM2018129, projekt VaV

Název: Národní infrastruktura pro biologické a medicínské zobrazování Czech-BioImaging

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, National research infrastructure for biological and medical imaging

NV17-32292A, projekt VaV

Název: Detekce léze u nelezionální epilepsie s využitím multimodálního zobrazování

Citovat

MAREČEK, Radek, Pavel ŘÍHA, Michaela BARTOŇOVÁ, Martin KOJAN, Martin LAMOŠ, Martin GAJDOŠ, Lubomír VOJTÍŠEK, Michal MIKL, Marek BARTOŇ, Irena DOLEŽALOVÁ, Martin PAIL, Ondřej STRÝČEK, Marta PAŽOURKOVÁ, Milan BRÁZDIL a Ivan REKTOR. Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging. Human Brain mapping. Hoboken: WILEY-BLACKWELL, 2021, roč. 42, č. 9, s. 2921-2930. ISSN 1065-9471. Dostupné z: https://dx.doi.org/10.1002/hbm.25413.

@article{1781359,
   author = {Mareček, Radek and Říha, Pavel and Bartoňová, Michaela and Kojan, Martin and Lamoš, Martin and Gajdoš, Martin and Vojtíšek, Lubomír and Mikl, Michal and Bartoň, Marek and Doležalová, Irena and Pail, Martin and Strýček, Ondřej and Pažourková, Marta and Brázdil, Milan and Rektor, Ivan},
   article_location = {Hoboken},
   article_number = {9},
   doi = {http://dx.doi.org/10.1002/hbm.25413},
   keywords = {data fusion; neuroimaging; nonlesional epilepsy; seizure onset zone},
   language = {eng},
   issn = {1065-9471},
   journal = {Human Brain mapping},
   title = {Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging},
   url = {https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25413},
   volume = {42},
   year = {2021}
}

TY  - JOUR
ID  - 1781359
AU  - Mareček, Radek - Říha, Pavel - Bartoňová, Michaela - Kojan, Martin - Lamoš, Martin - Gajdoš, Martin - Vojtíšek, Lubomír - Mikl, Michal - Bartoň, Marek - Doležalová, Irena - Pail, Martin - Strýček, Ondřej - Pažourková, Marta - Brázdil, Milan - Rektor, Ivan
PY  - 2021
TI  - Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging
JF  - Human Brain mapping
VL  - 42
IS  - 9
SP  - 2921-2930
EP  - 2921-2930
PB  - WILEY-BLACKWELL
SN  - 10659471
KW  - data fusion
KW  - neuroimaging
KW  - nonlesional epilepsy
KW  - seizure onset zone
UR  - https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25413
N2  - Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.
ER  -

MAREČEK, Radek, Pavel ŘÍHA, Michaela BARTOŇOVÁ, Martin KOJAN, Martin LAMOŠ, Martin GAJDOŠ, Lubomír VOJTÍŠEK, Michal MIKL, Marek BARTOŇ, Irena DOLEŽALOVÁ, Martin PAIL, Ondřej STRÝČEK, Marta PAŽOURKOVÁ, Milan BRÁZDIL a Ivan REKTOR. Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging. \textit{Human Brain mapping}. Hoboken: WILEY-BLACKWELL, 2021, roč.~42, č.~9, s.~2921-2930. ISSN~1065-9471. Dostupné z: https://dx.doi.org/10.1002/hbm.25413.

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