Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
Wu, O
Winzeck, S
Giese, AK
Hancock, BL
Etherton, MR
Bouts, MJRJ
Donahue, K
Schirmer, MD
Irie, RE
Mocking, SJT
McIntosh, EC
Bezerra, R
Kamnitsas, K
Frid, P
Wasselius, J
Cole, JW
Xu, H
Holmegaard, L
Jiménez-Conde, J
Lemmens, R
Lorentzen, E
McArdle, PF
Meschia, JF
Roquer, J
Rundek, T
Sacco, RL
Schmidt, R
Sharma, P
Slowik, A
Stanne, TM
Thijs, V
Vagal, A
Woo, D
Bevan, S
Kittner, SJ
Mitchell, BD
Rosand, J
Worrall, BB
Jern, C
Lindgren, AG
Maguire, J
Rost, NS
Rost, NS
- Publication Type:
- Journal Article
- Citation:
- Stroke, 2019, 50 (7), pp. 1734 - 1741
- Issue Date:
- 2019-07-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 00007670-201907000-00016.pdf | Published Version | 1.02 MB |
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© 2019 American Heart Association, Inc. 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.
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