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Title

Personalized structural image analysis in patients with temporal lobe epilepsy

Authors
Rummel, Christian
Slavova, Nedelina
Seiler, Andrea
Abela, Eugenio
Hauf, Martinus
Burren, Yuliya
Weisstanner, Christian
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Published in Scientific Reports. 2017, vol. 7, no. 1, p. 10883
Abstract Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.
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PMID: 28883420
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Article (Published version) (4.4 MB) - public document Free access
Structures
Research group Epilepsie et Chirurgie (149)
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RUMMEL, Christian et al. Personalized structural image analysis in patients with temporal lobe epilepsy. In: Scientific Reports, 2017, vol. 7, n° 1, p. 10883. https://archive-ouverte.unige.ch/unige:99723

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Deposited on : 2017-11-28

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