title: Applying instance-based techniques to prediction of final outcome in acute stroke.
abstract: OBJECTIVE
  Acute cerebral stroke is a frequent cause of death and the major cause of adult neurological disability in the western world. Thrombolysis is the only established treatment of ischemic stroke; however, its use carries a substantial risk of symptomatic intracerebral hemorrhage. A clinical tool to guide the use of thrombolysis would be very valuable. One of the major goals of such a tool would be the identification of potentially salvageable tissue. This requires an accurate prediction of the extent of infarction if untreated. In this study, we investigate the applicability of highly flexible instance-based (IB) methods for such predictions.
  
  
  METHODS
  Based on information obtained from magnetic resonance imaging of 14 patients with acute stroke, we explored three different implementations of the IB method: k-NN, Gaussian weighted, and constant radius search classification. Receiver operating characteristics analysis, in particular area under the curve (AUC), was used as performance measure.
  
  
  RESULTS
  We found no significant difference (P = 0.48) in performance for the optimal k-NN (k = 164, AUC = 0.814 +/- 0.001) and Gaussian weight (sigma = 0.17, AUC = 0.813 +/- 0.001) implementations, while they were both significantly better (P < 1 x 10(-6) for both) than the constant radius implementation (R = 0.28, AUC = 0.809 +/- 0.001). Qualitative analyses of the distribution of instances in the feature space indicated that non-infarcted instances tends to cluster together while infarcted instances are more dispersed, and that there may not exist a stringent boundary separating infarcted from non-infarcted instances.
  
  
  CONCLUSIONS
  This study shows that IB methods can be used, and may be advantageous, for predicting final infarct in patients with acute stroke, but further work must be done to make them clinically applicable.
authors.affiliation: DIMAC A/S, Højbjerg, Denmark. cgottrup@pet.auh.dk
authors.forename: Christian
authors.initials: C
authors.lastname: Gottrup
authors.forename: Knud
authors.initials: K
authors.lastname: Thomsen
authors.forename: Peter
authors.initials: P
authors.lastname: Locht
authors.forename: Ona
authors.initials: O
authors.lastname: Wu
authors.forename: A Gregory
authors.initials: AG
authors.lastname: Sorensen
authors.forename: Walter J
authors.initials: WJ
authors.lastname: Koroshetz
authors.forename: Leif
authors.initials: L
authors.lastname: Østergaard
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D001810:Blood Volume / Q000502:physiology; D002545:Brain Ischemia / Q000175:diagnosis* / Q000503:physiopathology; D002544:Cerebral Infarction / Q000175:diagnosis / Q000503:physiopathology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003661:Decision Support Techniques; D005500:Follow-Up Studies; D005544:Forecasting; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012372:ROC Curve; D012680:Sensitivity and Specificity; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology; D015912:Thrombolytic Therapy; D013997:Time Factors; D016896:Treatment Outcome