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  • Wu, O. (author)

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

  • Article/chapterEnglish2019

Publisher, publication year, extent ...

  • Ovid Technologies (Wolters Kluwer Health),2019

Numbers

  • LIBRIS-ID:oai:gup.ub.gu.se/282925
  • https://gup.ub.gu.se/publication/282925URI
  • https://doi.org/10.1161/strokeaha.119.025373DOI

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  • Language:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (rho=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm(3) (0.9-16.6 cm(3)). Patients with small artery occlusion stroke subtype had smaller lesion volumes (P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

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  • Winzeck, S. (author)
  • Giese, A. K. (author)
  • Hancock, B. L. (author)
  • Etherton, M. R. (author)
  • Bouts, Mjrj (author)
  • Donahue, K. (author)
  • Schirmer, M. D. (author)
  • Irie, R. E. (author)
  • Mocking, S. J. T. (author)
  • McIntosh, E. C. (author)
  • Bezerra, R. (author)
  • Kamnitsas, K. (author)
  • Frid, P. (author)
  • Wasselius, J. (author)
  • Cole, J. W. (author)
  • Xu, H. C. (author)
  • Holmegaard, LukasGothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology(Swepub:gu)xhollu (author)
  • Jimenez-Conde, J. (author)
  • Lemmens, R. (author)
  • Lorentzen, Erik,1974Gothenburg University,Göteborgs universitet,Institutionen för biomedicin,Institute of Biomedicine(Swepub:gu)xjacer (author)
  • McArdle, P. F. (author)
  • Meschia, J. F. (author)
  • Roquer, J. (author)
  • Rundek, T. (author)
  • Sacco, R. L. (author)
  • Schmidt, R. (author)
  • Sharma, P. (author)
  • Slowik, A. (author)
  • Stanne, Tara M,1979Gothenburg University,Göteborgs universitet,Institutionen för biomedicin,Institute of Biomedicine(Swepub:gu)xmcdta (author)
  • Thijs, V. (author)
  • Vagal, A. (author)
  • Woo, D. (author)
  • Bevan, S. (author)
  • Kittner, S. J. (author)
  • Mitchell, B. D. (author)
  • Rosand, J. (author)
  • Worrall, B. B. (author)
  • Jern, Christina,1962Gothenburg University,Göteborgs universitet,Institutionen för biomedicin,Institute of Biomedicine(Swepub:gu)xjerch (author)
  • Lindgren, A. G. (author)
  • Maguire, J. (author)
  • Rost, N. S. (author)
  • Göteborgs universitetInstitutionen för neurovetenskap och fysiologi (creator_code:org_t)

Related titles

  • In:Stroke: Ovid Technologies (Wolters Kluwer Health)50:7, s. 1734-17410039-24991524-4628

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