Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging.

ABSTRACT: The relationship between stroke topography and functional outcome has largely been studied with binary manual lesion segmentations. However, stroke topography may be better characterized by continuous variables capable of reflecting the severity of ischemia, which may be more pertinent for long-term outcome. Diffusion Tensor Imaging (DTI) constitutes a powerful means of quantifying the degree of acute ischemia and its potential relation to functional outcome. Our aim was to investigate whether using more clinically pertinent imaging parameters with powerful machine learning techniques could improve prediction models and thus provide valuable insight on critical brain areas important for long-term outcome. Eighty-seven thrombolyzed patients underwent a DTI sequence at 24 h post-stroke. Func

SUBMITTER: Moulton E 

PROVIDER: S-EPMC6462821 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature