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

Wu, Ona; Winzeck, Stefan; Giese, Anne-Katrin; Hancock, Brandon L; Etherton, Mark R; Bouts, Mark J R J; Donahue, Kathleen; Schirmer, Markus D; Irie, Robert E; Mocking, Steven J T; McIntosh, Elissa C; Bezerra, Raquel; Kamnitsas, Konstantinos; Frid, Petrea; Wasselius, Johan; Cole, John W; Xu, Huichun; Holmegaard, Lukas; Jiménez-Conde, Jordi; Lemmens, Robin; Lorentzen, Eric; McArdle, Patrick F; Meschia, James F; Roquer, Jaume; Rundek, Tatjana; Sacco, Ralph L; Schmidt, Reinhold; Sharma, Pankaj; Slowik, Agnieszka; Stanne, Tara M; Thijs, Vincent N; Vagal, Achala; Woo, Daniel; Bevan, Stephen; Kittner, Steven J; Mitchell, Braxton D; Rosand, Jonathan; Worrall, Bradford B; Jern, Christina; Lindgren, Arne G; Maguire, Jane; Rost, Natalia S

doi:10.1161/STROKEAHA.119.025373

Title

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

Publication Date

2019-07

Author(s)

Wu, Ona

Winzeck, Stefan

Giese, Anne-Katrin

Hancock, Brandon L

Etherton, Mark R

Bouts, Mark J R J

Donahue, Kathleen

Schirmer, Markus D

Irie, Robert E

Mocking, Steven J T

McIntosh, Elissa C

Bezerra, Raquel

Kamnitsas, Konstantinos

Frid, Petrea

Wasselius, Johan

Cole, John W

Xu, Huichun

Holmegaard, Lukas

Jiménez-Conde, Jordi

Lemmens, Robin

Lorentzen, Eric

McArdle, Patrick F

Meschia, James F

Roquer, Jaume

Rundek, Tatjana

Sacco, Ralph L

Schmidt, Reinhold

Sharma, Pankaj

Slowik, Agnieszka

Stanne, Tara M

Thijs, Vincent N

Vagal, Achala

Woo, Daniel

Bevan, Stephen

Kittner, Steven J

Mitchell, Braxton D

Rosand, Jonathan

Worrall, Bradford B

Jern, Christina

Lindgren, Arne G

Maguire, Jane

Rost, Natalia S

Subject

diffusion magnetic resonance imaging

machine learning

phenotype

risk factors

Stroke

Type of document

Journal Article

OrcId

0000-0002-6614-8417

#PLACEHOLDER_PARENT_METADATA_VALUE#

DOI

10.1161/STROKEAHA.119.025373

Abstract

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 (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). 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.

Link

link

Citation

Stroke 2019; 50(7): 1734-1741

Jornal Title

Stroke

Austin Health

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