Prediction of Stroke Outcome from Early MRI Data using an Adaptive Neural Network

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DESCRIPTION (provided by applicant): We have previously used combined MRI image sets and iterative self-organizing data analysis (ISODATA), to obtain a time-independent prediction of eventual lesion volume in animal models of stroke and in humans. Recently, we have refined and improved this approach by introducing an adaptive neural network (ANN) as a predictor of the T2-weighted image at 3 months, thus providing an essentially continuous descriptor of tissue outcome, rather than the much coarser classification scheme produced in ISODATA. In this R03 application, we propose to apply this methodology to the reanalysis of an existing data set of human studies obtained over a period of ten years by the human arm of a stroke program project grant, and thereby provide an early and robust predictor of outcome in stroke. An external stroke data base will be used as an independent validating set. Finally, an examination of MRI changes in an open-label trial, and in a blinded trial, of the anti-platelet drug abciximab will be conducted. This latter examination will allow the description of the operating characteristics of the ANN predictor (i.e., its connection to clinical measures) to be refined. If this effort is successful, we will produce a surrogate MRI outcome measure that will be quickly available (essentially in real time) to predict the final results of stroke in the parenchyma of the brain, at the acute and subacute stages of stroke. This will allow the real-time assessment of treatment effects in acute and subacute stroke patients. PUBLIC HEALTH RELEVANCE: Stroke is a leading cause of death and disability in the United States. Using MRI images taken in the early stages of stroke, we aim to produce a predictor of stroke outcome so that therapeutic interventions can be assessed in real-time. PHS 398/2590 (Rev. 09/04, Reissued 4/2006) 1 Continuation Format Page