title: Automated detection of cortical dysplasia type II in MRI-negative epilepsy.
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%).
  
  
  CONCLUSIONS
  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.
  
  
  METHODS
  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.
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada.
authors.forename: Seok-Jun
authors.initials: SJ
authors.lastname: Hong
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada.
authors.forename: Hosung
authors.initials: H
authors.lastname: Kim
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada.
authors.forename: Dewi
authors.initials: D
authors.lastname: Schrader
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada.
authors.forename: Neda
authors.initials: N
authors.lastname: Bernasconi
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada.
authors.forename: Boris C
authors.initials: BC
authors.lastname: Bernhardt
authors.affiliation: From the NeuroImaging of Epilepsy Laboratory, Department of Neurology and McConnell Brain Imaging Center, McGill University, Montreal Neurological Institute and Hospital, Canada. andrea@bic.mni.mcgill.ca.
authors.forename: Andrea
authors.initials: A
authors.lastname: Bernasconi
mesh_terms: D000328:Adult; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D001330:Electronic Data Processing / Q000379:methods*; D004827:Epilepsy / Q000209:etiology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D054220:Malformations of Cortical Development / Q000150:complications / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D055815:Young Adult