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Automated quantitative evaluation of brain MRI may be more accurate for discriminating preterm born adults

  • Magnetic Resonance
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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.

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Abbreviations

AUC:

Area under the curve

FLAIR:

Fluid attenuated inversion recovery

PACS:

Picture archiving and communication system

ROC:

Receiver operator characteristic

T1- or T2-w:

T1- or T2-weighted

VP/VLBW:

Very preterm birth or very low birth weight

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Acknowledgements

We thank all current and former Bavarian Longitudinal Study Group members who contributed to study organization, recruitment, data collection, management, and analyses, including (in alphabetical order) Stephan Czeschka, Claudia Grünzinger, Julia Jaekel, Christian Koch, Diana Kurze, Sonja Perk, Andrea Schreier, Antje Strasser, Julia Trummer, and Eva van Rossum. Most importantly, we thank all the study participants for their efforts to take part in this study.

Funding

This study has received funding from the German Federal Ministry of Education and Science (BMBF 01ER0801 to PB and DW, BMBF 01EV0710 to AMW, BMBF 01ER0803 to CS) and the Kommission für Klinische Forschung, Technische Universität München (KKF 8765162 to CS).

Author information

Authors and Affiliations

  1. Functional Neuroimaging Group, Department of Radiology, University Hospital Bonn, Bonn, Germany

    Alina Jurcoane, Marcel Daamen, Lukas Scheef & Henning Boecker

  2. Section of Neuroradiology, Department of Radiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53105, Bonn, Germany

    Alina Jurcoane, Vera C. Keil, Milka Marinova & Elke Hattingen

  3. Department of Neonatology, University Hospital Bonn, Bonn, Germany

    Alina Jurcoane, Marcel Daamen, Barbara Busch & Peter Bartmann

  4. Institute for Neuroradiology, University Hospital Frankfurt, Frankfurt, Germany

    Alina Jurcoane & Elke Hattingen

  5. Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany

    Josef G. Bäuml, Chun Meng, Afra M. Wohlschläger & Christian Sorg

  6. TUM-NIC Neuroimaging Center, Technische Universität München, Munich, Germany

    Josef G. Bäuml, Chun Meng, Afra M. Wohlschläger & Christian Sorg

  7. Department of Psychiatry, Klinikum rechts der Isar, Munich, Germany

    Christian Sorg

  8. Department of Psychology, University of Warwick, Coventry, UK

    Nicole Baumann & Dieter Wolke

  9. Warwick Medical School, University of Warwick, Coventry, UK

    Dieter Wolke

  10. Center for Development Research, University of Bonn, Bonn, Germany

    Guido Lüchters

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  1. Alina Jurcoane
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Corresponding author

Correspondence to Alina Jurcoane.

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Guarantor

The scientific guarantor of this publication is Dr. Alina Jurcoane.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors, GL, has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects in this study.

Ethical approval

The Ethics Committee at the University Hospital Bonn approved the study.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in several articles (https://www.ncbi.nlm.nih.gov/pubmed/?term=bartmann+wolke+mri). These were on topics of functional MRI, diffusion tensor imaging or behavioral measures; the current article is the only publication with neuroradiological focus.

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• prospective

• cross-sectional study

• performed at one institution

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Milka Marinova and Elke Hattingen share authorship.

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Jurcoane, A., Daamen, M., Keil, V.C. et al. Automated quantitative evaluation of brain MRI may be more accurate for discriminating preterm born adults. Eur Radiol 29, 3533–3542 (2019). https://doi.org/10.1007/s00330-019-06099-7

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