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Scalzo F, Nour M, Liebeskind DS. Data science of stroke imaging and enlightenment of the penumbra. Front Neurol. 2015;6:8doi: 10.3389/fneur.2015.00008.
Scalzo, F., Nour, M., & Liebeskind, D. S. (2015). Data science of stroke imaging and enlightenment of the penumbra. Frontiers in neurology, 68. https://doi.org/10.3389/fneur.2015.00008
Scalzo, Fabien, et al. "Data science of stroke imaging and enlightenment of the penumbra." Frontiers in neurology vol. 6 (2015): 8. doi: https://doi.org/10.3389/fneur.2015.00008
Scalzo F, Nour M, Liebeskind DS. Data science of stroke imaging and enlightenment of the penumbra. Front Neurol. 2015 Mar 05;6:8. doi: 10.3389/fneur.2015.00008. eCollection 2015. PMID: 25798125; PMCID: PMC4350425.
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Front Neurol. 2015 Mar 05;6:8. doi: 10.3389/fneur.2015.00008. eCollection 2015.

Data science of stroke imaging and enlightenment of the penumbra.

Frontiers in neurology

Fabien Scalzo, May Nour, David S Liebeskind

Affiliations

  1. Department of Neurology, Neurovascular Imaging Research Core, University of California Los Angeles , Los Angeles, CA , USA.

PMID: 25798125 PMCID: PMC4350425 DOI: 10.3389/fneur.2015.00008

Abstract

Imaging protocols of acute ischemic stroke continue to hold significant uncertainties regarding patient selection for reperfusion therapy with thrombolysis and mechanical thrombectomy. Given that patient inclusion criteria can easily introduce biases that may be unaccounted for, the reproducibility and reliability of the patient screening method is of utmost importance in clinical trial design. The optimal imaging screening protocol for selection in targeted populations remains uncertain. Acute neuroimaging provides a snapshot in time of the brain parenchyma and vasculature. By identifying the at-risk but still viable penumbral tissue, imaging can help estimate the potential benefit of a reperfusion therapy in these patients. This paper provides a perspective about the assessment of the penumbral tissue in the context of acute stroke and reviews several neuroimaging models that have recently been developed to assess the penumbra in a more reliable fashion. The complexity and variability of imaging features and techniques used in stroke will ultimately require advanced data driven software tools to provide quantitative measures of risk/benefit of recanalization therapy and help aid in making the most favorable clinical decisions.

Keywords: CT imaging; MRI imaging; acute stroke; cerebral ischemia; neuroimaging; neurology

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