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Automated quantitative evaluation of brain MRI may be more accurate for discriminating preterm born adults
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Jurcoane, Alina, Daamen, Marcel, Keil, Vera C., Scheef, Lukas, Bäuml, Josef G., Meng, Chun, Wohlschläger, Afra M., Sorg, Christian, Busch, Barbara, Baumann, Nicole, Wolke, Dieter, Bartmann, Peter, Boecker, Henning, Lüchters, Guido, Marinova, Milka and Hattingen, Elke (2019) Automated quantitative evaluation of brain MRI may be more accurate for discriminating preterm born adults. European Radiology, 29 (7). pp. 3533-3542. doi:10.1007/s00330-019-06099-7 ISSN 0938-7994.
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WRAP-Automated-quantitative-evaluation-MRI-accurate-adults-Wolke-2019.pdf - Accepted Version - Requires a PDF viewer. Download (829Kb) | Preview |
Official URL: https://doi.org/10.1007/s00330-019-06099-7
Abstract
Objective
To investigate the structural brain abnormalities and their diagnostic accuracy through qualitative and quantitative analysis in term born and very preterm birth or with very low birth weight (VP/VLBW) adults.
Methods
We analyzed 3-T MRIs acquired in 2011–2013 from 67 adults (27 term born controls, mean age 26.4 years, 8 females; 40 VP/VLBWs, mean age 26.6 years, 16 females). We compared automatic segmentations of the white matter, deep gray matter and cortical gray matter, manual corpus callosum measurements and visual ratings of the ventricles and white matter with t tests, logistic regression, and receiver operator characteristic (ROC) curves.
Results
Automatic segmentation correctly classified 84% of cases; visual ratings correctly classified 63%. Quantitative volumetry based on automatic segmentation revealed higher ventricular volume, lower posterior corpus callosum, and deep gray matter volumes in VP/VLBW subjects compared to controls (p < 0.01). Visual rating and manual measurement revealed a thinner corpus callosum in VP/VLBW adults (p = 0.04) and deformed lateral ventricles (p = 0.03) and tendency towards more “dirty” white matter (p = 0.06). Automatic/manual measures combined with visual ratings correctly classified 87% of cases. Stepwise logistic regression identified three independent features that correctly classify 81% of cases: ventricular volume, deep gray matter volume, and white matter aspect.
Conclusion
Enlarged and deformed lateral ventricles, thinner corpus callosum, and “dirty” white matter are prevalent in preterm born adults. Their visual evaluation has low diagnostic accuracy. Automatic volume quantification is more accurate but time consuming. It may be useful to ask for prematurity before initiating further diagnostics in subjects with these alterations.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Psychology | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Brain -- Abnormalities, Premature infants, Birth weight, Low | |||||||||||||||
Journal or Publication Title: | European Radiology | |||||||||||||||
Publisher: | Springer | |||||||||||||||
ISSN: | 0938-7994 | |||||||||||||||
Official Date: | 1 July 2019 | |||||||||||||||
Dates: |
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Volume: | 29 | |||||||||||||||
Number: | 7 | |||||||||||||||
Page Range: | pp. 3533-3542 | |||||||||||||||
DOI: | 10.1007/s00330-019-06099-7 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | This is a post-peer-review, pre-copyedit version of an article published in European Radiology. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00330-019-06099-7 | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Copyright Holders: | © European Society of Radiology 2019 | |||||||||||||||
Date of first compliant deposit: | 16 April 2019 | |||||||||||||||
Date of first compliant Open Access: | 6 February 2020 | |||||||||||||||
RIOXX Funder/Project Grant: |
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