title: Multiclass Support Vector Machine-Based Lesion Mapping Predicts Functional Outcome in Ischemic Stroke Patients.
abstract: OBJECTIVE
  The aim of this study was to investigate if ischemic stroke final infarction volume and location can be used to predict the associated functional outcome using a multi-class support vector machine (SVM).
  
  
  METHODS
  Sixty-eight follow-up MR FLAIR datasets of ischemic stroke patients with known modified Rankin Scale (mRS) functional outcome after 30 days were used. The infarct regions were segmented and used to calculate the percentage of lesioned voxels in the predefined MNI, Harvard-Oxford cortical and subcortical atlas regions as well as using four problem-specific VOIs, which were identified from the database using voxel-based lesion symptom mapping. An overall of 12 SVM classification models for predicting the corresponding mRS score were generated using the lesion overlap values from the different brain region definitions, stroke laterality information, and the optional parameters infarct volume, admission NIHSS, and patient age.
  
  
  RESULTS
  Leave-one-out cross validations revealed that including information about the stroke location in terms of lesion overlap measurements led to improved mRS prediction results compared to classification models not utilizing the stroke location information. Furthermore, integration of the optional features led to improved mRS prediction results in all cases tested. The problem-specific brain regions and additional integration of the optional features led to the best mRS predictions with a precise multi-value mRS prediction accuracy of 56%, sliding window multi-value mRS prediction accuracy (mRS±1) of 82%, and binary mRS (0-2 vs. 3-5) prediction accuracy of 85%.
  
  
  CONCLUSIONS
  Therefore, a graded SVM-based functional stroke outcome prediction using the problem-specific brain regions for lesion overlap quantification leads to promising results but needs to be further validated using an independent database to rule out a potential methodical bias and overfitting effects. The prediction of the graded mRS functional outcome could be a valuable tool if combined with voxel-wise tissue outcome predictions based on multi-parametric datasets acquired at the acute phase.
authors.affiliation: Department of Radiology and Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada.
authors.forename: Nils Daniel
authors.initials: ND
authors.lastname: Forkert
authors.affiliation: Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Verleger
authors.affiliation: Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
authors.forename: Bastian
authors.initials: B
authors.lastname: Cheng
authors.affiliation: Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
authors.forename: Götz
authors.initials: G
authors.lastname: Thomalla
authors.affiliation: Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany; Department of Health Sciences, Boston University, 635 Commonwealth Ave, Boston, MA, 02215, United States of America.
authors.forename: Claus C
authors.initials: CC
authors.lastname: Hilgetag
authors.affiliation: Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
authors.forename: Jens
authors.initials: J
authors.lastname: Fiehler
mesh_terms: D000368:Aged; D002545:Brain Ischemia / Q000175:diagnosis* / Q000503:physiopathology; D001931:Brain Mapping; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology; D060388:Support Vector Machine