Applying instance-based techniques to prediction of final outcome in acute stroke

Authors:
Christian Gottrup;Knud Thomsen;Peter Locht;Ona Wu;A. Gregory Sorensen;Walter J. Koroshetz;Leif Østergaard
Affiliations:
DIMAC A/S, Højbjerg, Denmark and Department of Neuroradiology, Center of Functionally Integrative Neuroscience, írhus University Hospital, Building 30, Nørrebrogade 44, DK-8000 ...;DIMAC A/S, Højbjerg, Denmark;DIMAC A/S, Højbjerg, Denmark;Department of Neuroradiology, Center of Functionally Integrative Neuroscience, írhus University Hospital, Building 30, Nørrebrogade 44, DK-8000 írhus C, Denmark and MGH/MIT/HMS Athi ...;MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA;Stroke Unit, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA;Department of Neuroradiology, Center of Functionally Integrative Neuroscience, írhus University Hospital, Building 30, Nørrebrogade 44, DK-8000 írhus C, Denmark
Venue:
Artificial Intelligence in Medicine
Year:
2005

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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 and materials: : 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 (@s = 0.17, AUC = 0.813 +/- 0.001) implementations, while they were both significantly better (P