@article{f29de9b48b7f40cd9aa145fe82638749,
title = "Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data",
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.",
keywords = "diffusion magnetic resonance imaging, machine learning, phenotype, risk factors, stroke",
author = "Ona Wu and Stefan Winzeck and Giese, {Anne Katrin} and Hancock, {Brandon L.} and Etherton, {Mark R.} and Bouts, {Mark J.R.J.} and Kathleen Donahue and Schirmer, {Markus D.} and Irie, {Robert E.} and Mocking, {Steven J.T.} and McIntosh, {Elissa C.} and Raquel Bezerra and Konstantinos Kamnitsas and Petrea Frid and Johan Wasselius and Cole, {John W.} and Huichun Xu and Lukas Holmegaard and Jordi Jim{\'e}nez-Conde and Robin Lemmens and Eric Lorentzen and McArdle, {Patrick F.} and Meschia, {James F.} and Jaume Roquer and Tatjana Rundek and Sacco, {Ralph L.} and Reinhold Schmidt and Pankaj Sharma and Agnieszka Slowik and Stanne, {Tara M.} and Vincent Thijs and Achala Vagal and Daniel Woo and Stephen Bevan and Kittner, {Steven J.} and Mitchell, {Braxton D.} and Jonathan Rosand and Worrall, {Bradford B.} and Christina Jern and Lindgren, {Arne G.} and Jane Maguire and Rost, {Natalia S.}",
note = "Funding Information: We acknowledge the support of Nvidia Corporation with the donation of the Tesla K40 GPU used for this research. Funding Information: This work was supported by National Institutes of Health: R01NS030678 (Drs Vagal and Woo), R01NS039987 (Dr Worrall), R01NS042733 (Drs Worrall and Meschia), R01NS059775 (Dr Wu), R01NS063925 (Dr Wu), R01NS082285 (Drs Rost and Wu), R01NS086905 (Drs Rost, Wu, and Kittner), R01NS100178 (Dr Mitchell), R01NS100417 (Dr Vagal), R01NS103824-01 (Dr Vagal), R01NS29993 (Drs Sacco and Rundek), P50NS051343 (Dr Wu), U01NS069208 (Drs Worrall, Rosand, Wu, and Cole), U10NS077311 (Dr Vagal), K23NS064052 (Dr Rost), EB015325 (Dr Wu), 1S10RR019307 (Dr Wu). The Swedish state under the agreement between the Swedish government and the county councils, the Avtal om L?karutbildning och Forskning (ALF) agreement: (Drs Lindgren, Jern, and Wasselius); Swedish Stroke Association: (Drs Lindgren, Jern, and Wasselius); Swedish Heart and Lung Foundation: (Drs Lindgren and Jern); Lund University: (Dr Lindgren); Region Sk?ne: (Dr Lindgren); Sk?ne University Hospital: (Dr Lindgren); Freemasons Lodge of Instruction Eos in Lund: (Dr Lindgren); Foundation of F?rs & Frosta: (Dr Lindgren); and Swedish Research Council: (Dr Jern). Spanish Ministry of Science and Innovation (PI051737, PI10/02064, PI12/01238, PI15/00451); European Regional Development Fund (EDRF) Red de Investigaci?n Cardiovascular (RD12/0042/0020); Fundaci? la Marat? TV3 (76/C/2011); Recercaixa'13 (JJ086116): (Dr Jim?nez-Conde); European Research Council: Horizon 2020 MSC Global Fellowship No. 753896 (Dr Schirmer); Fonds voor Wetenschappelijk Onderzoek: 1841918 N (Dr Lemmens); Crafoord Foundation: (Dr Wasselius); Henry Smith Charity, Qatar National Research Fund, Stroke Association (United Kingdom), Dept of Health (United Kingdom), British Council, United Kingdom-India Education Research Initiative (UKIERI): Bio-Repository of DNA in Stroke (BRAINS); American Heart Association: Clinical Research Training Fellowship (Dr Etherton); Cardiovascular Genome-Phenome Study (No. 15GPSPG23770000, Dr Cole); Discovery Grant supported by the Bayer Group (No. 17IBDG33700328, Dr Cole), Uncovering new patterns in cardiovascular disease and stroke (No. 18UNPG34030160, Dr Wu); President's PhD Scholarship of Imperial College London: (K. Kamnitsas); and Department of Veterans Affairs (United States): Baltimore Research Enhancement Program (Dr Cole). Publisher Copyright: {\textcopyright} 2019 American Heart Association, Inc.",
year = "2019",
month = jul,
day = "1",
doi = "10.1161/STROKEAHA.119.025373",
language = "English (US)",
volume = "50",
pages = "1734--1741",
journal = "Stroke",
issn = "0039-2499",
publisher = "Lippincott Williams and Wilkins",
number = "7",
}