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  Automatic detection of cortical dysplasia type II in MRI-negative epilepsy

Hong, S.-J., Kim, H., Schrader, D., Bernasconi, N., Bernhardt, B. C., & Bernasconi, A. (2014). Automatic detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology, 83(1), 48-55. doi:10.1212/WNL.0000000000000543.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-ADB0-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7EE9-A
Genre: Journal Article

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 Creators:
Hong, Seok-Jun1, Author
Kim, Hosung1, Author
Schrader, Dewi1, Author
Bernasconi, Neda1, Author
Bernhardt, Boris C.1, Author              
Bernasconi, Andrea1, Author
Affiliations:
1Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, QC, Canada, ou_persistent22              

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 Abstract: Objective: To detect automatically focal cortical dysplasia (FCD) type II in patients with extratemporal epilepsy initially diagnosed as MRI-negative on routine inspection of 1.5 and 3.0T scans. Methods: We implemented an automated classifier relying on surface-based features of FCD morphology and intensity, taking advantage of their covariance. The method was tested on 19 patients (15 with histologically confirmed FCD) scanned at 3.0T, and cross-validated using a leave-one-out strategy. We assessed specificity in 24 healthy controls and 11 disease controls with temporal lobe epilepsy. Cross-dataset classification performance was evaluated in 20 healthy controls and 14 patients with histologically verified FCD examined at 1.5T. Results: Sensitivity was 74%, with 100% specificity (i.e., no lesions detected in healthy or disease controls). In 50% of cases, a single cluster colocalized with the FCD lesion, while in the remaining cases a median of 1 extralesional cluster was found. Applying the classifier (trained on 3.0T data) to the 1.5T dataset yielded comparable performance (sensitivity 71%, specificity 95%). Conclusion: In patients initially diagnosed as MRI-negative, our fully automated multivariate approach offered a substantial gain in sensitivity over standard radiologic assessment. The proposed method showed generalizability across cohorts, scanners, and field strengths. Machine learning may assist presurgical decision-making by facilitating hypothesis formulation about the epileptogenic zone. Classification of evidence: This study provides Class II evidence that automated machine learning of MRI patterns accurately identifies FCD among patients with extratemporal epilepsy initially diagnosed as MRI-negative.

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Language(s): eng - English
 Dates: 2013-08-222014-02-052014-06-042014-07-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1212/WNL.0000000000000543
PMID: 24898923
PMC: PMC4114179
Other: Epub 2014
 Degree: -

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Title: Neurology
Source Genre: Journal
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Pages: - Volume / Issue: 83 (1) Sequence Number: - Start / End Page: 48 - 55 Identifier: ISSN: 0028-3878
CoNE: https://pure.mpg.de/cone/journals/resource/954925246073