학술논문
Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
Document Type
Academic Journal
Author
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; 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.
Source
Stroke. Jul 01, 2019 50(7):1734-1741
Subject
Language
English
ISSN
0039-2499
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 cm (0.9–16.6 cm). 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.