Alina Jurcoane1,2,3,4, Marcel Daamen5,6, Vera C Keil7, Lukas Scheef5, Josef G Bäuml8,9, Chun Meng8,9, Afra M Wohlschläger8,9, Christian Sorg8,9,10, Barbara Busch6, Nicole Baumann11, Dieter Wolke11,12, Peter Bartmann6, Henning Boecker5, Guido Lüchters13, Milka Marinova7, Elke Hattingen7,14. 1. Functional Neuroimaging Group, Department of Radiology, University Hospital Bonn, Bonn, Germany. alinajw@outlook.de. 2. Section of Neuroradiology, Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53105, Bonn, Germany. alinajw@outlook.de. 3. Department of Neonatology, University Hospital Bonn, Bonn, Germany. alinajw@outlook.de. 4. Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt, Germany. alinajw@outlook.de. 5. Functional Neuroimaging Group, Department of Radiology, University Hospital Bonn, Bonn, Germany. 6. Department of Neonatology, University Hospital Bonn, Bonn, Germany. 7. Section of Neuroradiology, Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53105, Bonn, Germany. 8. Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany. 9. TUM-NIC Neuroimaging Center, Technische Universität München, Munich, Germany. 10. Department of Psychiatry, Klinikum rechts der Isar, Munich, Germany. 11. Department of Psychology, University of Warwick, Coventry, UK. 12. Warwick Medical School, University of Warwick, Coventry, UK. 13. Center for Development Research, University of Bonn, Bonn, Germany. 14. Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt, Germany.
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. KEY POINTS: • Our study confirms prior reports showing that structural brain abnormalities related to preterm birth persist into adulthood. • In the clinical practice, if large and deformed lateral ventricles, small and thin corpus callosum, and "dirty" white matter are visible on MRI, ask for prematurity before considering other diagnoses. • Although prevalent, visual findings have low accuracy; adding automatic segmentation of lateral ventricles and deep gray matter nuclei improves the diagnostic accuracy.
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. KEY POINTS: • Our study confirms prior reports showing that structural brain abnormalities related to preterm birth persist into adulthood. • In the clinical practice, if large and deformed lateral ventricles, small and thin corpus callosum, and "dirty" white matter are visible on MRI, ask for prematurity before considering other diagnoses. • Although prevalent, visual findings have low accuracy; adding automatic segmentation of lateral ventricles and deep gray matter nuclei improves the diagnostic accuracy.
Entities:
Keywords:
Adults; Low birth weight; Magnetic resonance imaging; Preterm birth
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