Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

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http://hdl.handle.net/10138/332345

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Hokkinen , L M I , Mäkelä , T O , Savolainen , S & Kangasniemi , M M 2021 , ' Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke ' , European radiology experimental , vol. 5 , no. 1 , 25 . https://doi.org/10.1186/s41747-021-00225-1

Title: Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke
Author: Hokkinen, Lasse M I; Mäkelä, Teemu Olavi; Savolainen, Sauli; Kangasniemi, Marko Matti
Contributor organization: HUS Medical Imaging Center
Department of Diagnostics and Therapeutics
Department of Physics
University Management
Helsinki In Vivo Animal Imaging Platform (HAIP)
Sauli Savolainen / Principal Investigator
Doctoral Programme in Clinical Research
Doctoral Programme in Materials Research and Nanosciences
Clinicum
Date: 2021-06-24
Language: eng
Number of pages: 11
Belongs to series: European radiology experimental
ISSN: 2509-9280
DOI: https://doi.org/10.1186/s41747-021-00225-1
URI: http://hdl.handle.net/10138/332345
Abstract: Background Computed tomography angiography (CTA) imaging is needed in current guideline-based stroke diagnosis, and infarct core size is one factor in guiding treatment decisions. We studied the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from CTA and compared the results to a CT perfusion (CTP)-based commercially available software (RAPID, iSchemaView). Methods We retrospectively selected 83 consecutive stroke cases treated with thrombolytic therapy or receiving supportive care that presented to Helsinki University Hospital between January 2018 and July 2019. We compared CNN-derived ischaemic lesion volumes to final infarct volumes that were manually segmented from follow-up CT and to CTP-RAPID ischaemic core volumes. Results An overall correlation of r = 0.83 was found between CNN outputs and final infarct volumes. The strongest correlation was found in a subgroup of patients that presented more than 9 h of symptom onset (r = 0.90). A good correlation was found between the CNN outputs and CTP-RAPID ischaemic core volumes (r = 0.89) and the CNN was able to classify patients for thrombolytic therapy or supportive care with a 1.00 sensitivity and 0.94 specificity. Conclusions A CTA-based CNN software can provide good infarct core volume estimates as observed in follow-up imaging studies. CNN-derived infarct volumes had a good correlation to CTP-RAPID ischaemic core volumes.
Subject: 3126 Surgery, anesthesiology, intensive care, radiology
114 Physical sciences
Computed tomography angiography
Stroke
Deep learning
Machine learning
Convolutional neural network
ANGIOGRAPHY SOURCE IMAGES
COMPUTED-TOMOGRAPHY
PERFUSION
PLATFORM
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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