Citations 1
Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
Wu, Ona , Winzeck, Stefan , 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 , Wassélius, Johan , Xu, Huichun , Jiménez-Conde, Jordi , Lorentzen, Eric, McArdle, Patrick F. , Schmidt, Reinhold , Sharma, Pankaj , Słowik, Agnieszka , Stanne, Tara M. , Thijs, Vincent , Woo, Daniel , Kittner, Steven J. , Worrall, Bradford B. , Maguire, Jane , Rost, Natalia S. , Investigators, on behalf of the MRI-GENIE and GISCOME, Wu, Ona , Giese, Anne‐Katrin , Cole, John W. , Holmegaard, Lukas , Lemmens, Robin , Meschia, James F. , Roquer, Jaume , Rundek, Tatjana , Sacco, Ralph L. , Vagal, Achala , Bevan, Steve , Mitchell, Braxton D. , Rosand, Jonathan M. , Jern, Christina , Lindgren, Arne G. , Investigators, on behalf of the MRI-GENIE and GISCOME
Stroke | June 10, 2019 | Volume: 50 | Issue: 7 | Pages: 1734-1741
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 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.