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Rapid automated quantification of cerebral leukoaraiosis on CT: a multicentre validation study

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Title: Rapid automated quantification of cerebral leukoaraiosis on CT: a multicentre validation study
Authors: Chen, L
Carlton Jones, AL
Mair, G
Patel, R
Gontsarova, A
Ganesalingam, J
Math, N
Dawson, A
Basaam, A
Cohen, D
Mehta, A
Wardlaw, J
Rueckert, D
Bentley, P
Item Type: Journal Article
Abstract: Purpose - To validate a fully-automated, machine-learning method (random forest) for segmenting cerebral white matter lesions (WML) on computerized tomography (CT). Materials and Methods – A retrospective sample of 1082 acute ischemic stroke cases was obtained, comprising unselected patients: 1) treated with thrombolysis; or 2) undergoing contemporaneous MR imaging and CT; and 3) a subset of IST-3 trial participants. Automated (‘Auto’) WML images were validated relative to experts’ manual tracings on CT, and co-registered FLAIR-MRI; and ratings using two conventional ordinal scales. Analyses included correlations between CT and MR imaging volumes, and agreements between Auto and expert ratings. Results - Auto WML volumes correlated strongly with expert-delineated WML volumes on MR imaging and on CT (r2=0.85, 0.71 respectively; p<0.001). Spatial-similarity of Auto-maps, relative to MRI-WML, was not significantly different to that of expert CT-WML tracings. Individual expert CT-WML volumes correlated well with each other (r2=0.85), but varied widely (range: 91% of mean estimate; median 11 cc; range: 0.2 – 68 cc). Agreements between Auto and consensus-expert ratings were superior or similar to agreements between individual pairs of experts (kappa: 0.60, 0.64 vs. 0.51, 0.67 for two score systems; p<0.01 for first comparison). Accuracy was unaffected by established infarction, acute ischemic changes, or atrophy (p>0.05). Auto preprocessing failure rate was 4%; rating errors occurred in a further 4%. Total Auto processing time averaged 109s (range: 79 - 140 s). Conclusion - An automated method for quantifying CT cerebral white matter lesions achieves a similar accuracy to experts in unselected and multicenter cohorts.
Issue Date: 1-Aug-2018
Date of Acceptance: 12-Feb-2018
URI: http://hdl.handle.net/10044/1/57261
DOI: https://dx.doi.org/10.1148/radiol.2018171567
ISSN: 0033-8419
Publisher: Radiological Society of North America
Start Page: 573
End Page: 581
Journal / Book Title: Radiology
Volume: 288
Issue: 2
Copyright Statement: © RSNA, 2018
Sponsor/Funder: National Institute for Health Research
Funder's Grant Number: ll-LA-0814-20007
Keywords: IST-3 Collaborative Group
11 Medical And Health Sciences
Nuclear Medicine & Medical Imaging
Publication Status: Published
Online Publication Date: 2018-05-15
Appears in Collections:Computing
Department of Medicine (up to 2019)
Faculty of Engineering