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



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.


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.


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.


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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3



Area under the curve


Fluid attenuated inversion recovery


Picture archiving and communication system


Receiver operator characteristic

T1- or T2-w:

T1- or T2-weighted


Very preterm birth or very low birth weight


  1. 1.

    Blencowe H, Cousens S, Oestergaard MZ et al (2012) National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 379:2162–2172.

    Article  Google Scholar 

  2. 2.

    Murphy M, McLoughlin G (2015) Born too soon: preterm birth in Europe trends, causes and prevention. Entre Nous:10–12

  3. 3.

    Zeitlin J, Szamotulska K, Drewniak N et al (2013) Preterm birth time trends in Europe: a study of 19 countries. BJOG 120:1356–1365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Martin JA, Hamilton BE, Ph D et al (2017) Births: final data for 2015. Natl Vital Stat Rep 66

  5. 5.

    Delnord M, Blondel B, Zeitlin J (2015) What contributes to disparities in the preterm birth rate in European countries? Curr Opin Obstet Gynecol 27:133–142.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Saigal S, Doyle LW (2008) An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet 371:261–269.

    Article  PubMed  Google Scholar 

  7. 7.

    Allin M, Rooney M, Griffiths T et al (2006) Neurological abnormalities in young adults born preterm. J Neurol Neurosurg Psychiatry 77:495–499.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Anderson PJ (2014) Neuropsychological outcomes of children born very preterm. Semin Fetal Neonatal Med 19:90–96.

    Article  PubMed  Google Scholar 

  9. 9.

    Baron IS, Rey-Casserly C (2010) Extremely preterm birth outcome: a review of four decades of cognitive research. Neuropsychol Rev 20:430–452.

    Article  PubMed  Google Scholar 

  10. 10.

    Crump C, Sundquist K, Sundquist J, Winkleby MA (2011) Gestational age at birth and mortality in young adulthood. JAMA 306:1233–1240.

    Article  CAS  PubMed  Google Scholar 

  11. 11.

    Lawn JE, Blencowe H, Oza S et al (2014) Every newborn: progress, priorities, and potential beyond survival. Lancet 384:189–205.

    Article  Google Scholar 

  12. 12.

    Hack M, Flannery DJ, Schluchter M et al (2002) Outcomes in young adulthood for very-low-birth-weight infants. N Engl J Med 346:149–157.

    Article  PubMed  Google Scholar 

  13. 13.

    Rees S, Inder T (2005) Fetal and neonatal origins of altered brain development. Early Hum Dev 81:753–761.

    Article  PubMed  Google Scholar 

  14. 14.

    Bäuml JG, Daamen M, Meng C et al (2014) Correspondence between aberrant intrinsic network connectivity and gray-matter volume in the ventral brain of preterm born adults. Cereb Cortex.

  15. 15.

    Mathur AM, Neil JJ, Inder TE (2010) Understanding brain injury and neurodevelopmental disabilities in the preterm infant: the evolving role of advanced magnetic resonance imaging. Semin Perinatol 34:57–66.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Ment LR, Hirtz D, Hüppi PS (2009) Imaging biomarkers of outcome in the developing preterm brain. Lancet Neurol 8:1042–1055.

    Article  PubMed  Google Scholar 

  17. 17.

    Glass HC, Bonifacio SL, Peloquin S et al (2010) Neurocritical care for neonates. Neurocrit Care 12:421–429.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Barkovich AJ, Raybaud C (2012) Pediatric neuroimaging. Wolters Kluwer Health, Philadelphia

    Google Scholar 

  19. 19.

    Cheong JLY, Thompson DK, Wang HX et al (2009) Abnormal white matter signal on MR imaging is related to abnormal tissue microstructure. AJNR Am J Neuroradiol 30:623–628.

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Dubois J, Hu PS, Hüppi PS, Dubois J (2006) Diffusion tensor imaging of brain development. Semin Fetal Neonatal Med 11:489–497.

    Article  PubMed  Google Scholar 

  21. 21.

    Rutherford MA, Supramaniam V, Ederies A et al (2010) Magnetic resonance imaging of white matter diseases of prematurity. Neuroradiology 52:505–521.

    Article  PubMed  Google Scholar 

  22. 22.

    Degnan AJ, Wisnowski JL, Choi S et al (2015) Alterations of resting state networks and structural connectivity in relation to the prefrontal and anterior cingulate cortices in late prematurity. Neuroreport 26:22–26.

    Article  PubMed  Google Scholar 

  23. 23.

    Volpe JJ (2009) The encephalopathy of prematurity–brain injury and impaired brain development inextricably intertwined. Semin Pediatr Neurol 16:167–178.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Basten M, Jaekel J, Johnson S et al (2015) Preterm birth and adult wealth: mathematics skills count. Psychol Sci 26:1608–1619.

    Article  PubMed  Google Scholar 

  25. 25.

    Eryigit Madzwamuse S, Baumann N, Jaekel J et al (2014) Neuro-cognitive performance of very preterm or very low birth weight adults at 26 years. J Child Psychol Psychiatry n/a–n/a.

  26. 26.

    Greene MF (2002) Outcomes of very low birth weight in young adults. N Engl J Med 346:146–148.

    Article  PubMed  Google Scholar 

  27. 27.

    Baumann N, Bartmann P, Wolke D (2016) Health-related quality of life into adulthood after very preterm birth. Pediatrics 137.

  28. 28.

    Aukland SM, Elgen IB, Odberg MD et al (2014) Ventricular dilatation in ex-prematures: only confined to the occipital region? MRI-based normative standards for 19-year-old ex-prematures without major handicaps. Acta Radiol 55:470–477.

    Article  PubMed  Google Scholar 

  29. 29.

    Bjuland KJ, Rimol LM, Løhaugen GCC, Skranes J (2014) Brain volumes and cognitive function in very-low-birth-weight (VLBW) young adults. Eur J Paediatr Neurol 18:578–590.

    Article  PubMed  Google Scholar 

  30. 30.

    Nosarti C, Woo K, Walshe M et al (2014) Preterm birth and structural brain alterations in early adulthood. Neuroimage Clin 6:180–191.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Odberg MD, Aukland SM, Rosendahl K, Elgen IB (2010) Cerebral MRI and cognition in nonhandicapped, low birth weight adults. Pediatr Neurol 43:258–262.

    Article  PubMed  Google Scholar 

  32. 32.

    Breeman LD, Jaekel J, Baumann N et al (2017) Neonatal predictors of cognitive ability in adults born very preterm: a prospective cohort study. Dev Med Child Neurol 59:477–483.

    Article  PubMed  Google Scholar 

  33. 33.

    Daamen M, Bäuml JG, Scheef L et al (2015) Neural correlates of executive attention in adults born very preterm. Neuroimage Clin 9:581–591.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Jurcoane A, Daamen M, Scheef L et al (2016) White matter alterations of the corticospinal tract in adults born very preterm and/or with very low birth weight. Hum Brain Mapp 37:289–299.

    Article  PubMed  Google Scholar 

  35. 35.

    Riegel K, Ohrt B, Wolke D, Österlund K (1995) Die Entwicklung gefährdet geborener Kinder bis zum fünften Lebensjahr. (The development of at-risk children until the fifth year of life. The Arvo Ylppö longitudinal study in South Bavaria and South Finland). Ferdinand Enke Verlag, Stuttgart

  36. 36.

    Wolke D, Meyer R (1999) Cognitive status, language attainment, and prereading skills of 6-year-old very preterm children and their peers: the Bavarian Longitudinal Study. Dev Med Child Neurol 41:94–109

    Article  CAS  PubMed  Google Scholar 

  37. 37.

    Aster M von, Wechsler D (2009) Wechsler-Intelligenztest für Erwachsene : WIE ; Manual ; Übersetzung und Adaptation der WAIS-III von David Wechsler, 2., korr. Aufl. Pearson Assessment & Information, Frankfurt/M

  38. 38.

    Tschampa HJ, Urbach H, Malter M et al (2015) Magnetic resonance imaging of focal cortical dysplasia: comparison of 3D and 2D fluid attenuated inversion recovery sequences at 3T. Epilepsy Res 116:8–14.

    Article  PubMed  Google Scholar 

  39. 39.

    Jenkinson M, Beckmann CF, Behrens TEJ et al (2012) FSL. Neuroimage 62:782–790.

    Article  Google Scholar 

  40. 40.

    Ge Y, Grossman RI, Babb JS et al (2003) Dirty-appearing white matter in multiple sclerosis: volumetric MR imaging and magnetization transfer ratio histogram analysis. AJNR Am J Neuroradiol 24:1935–1940

    PubMed  Google Scholar 

  41. 41.

    Rothman KJ, Greenland S, Lash TL (2008) Modern epidemiology, 3rd edn. Lippincott Williams & Wilkins, Philadelphia

  42. 42.

    Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, New York

    Google Scholar 

  43. 43.

    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

    Google Scholar 

  44. 44.

    Pandit AS, Ball G, Edwards AD, Counsell SJ (2013) Diffusion magnetic resonance imaging in preterm brain injury. Neuroradiology 55:65–95.

    Article  PubMed  Google Scholar 

  45. 45.

    Feldman HM, Lee ES, Loe IM et al (2012) White matter microstructure on diffusion tensor imaging is associated with conventional magnetic resonance imaging findings and cognitive function in adolescents born preterm. Dev Med Child Neurol 54:809–814.

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Stewart AL, Rifkin L, Amess PN et al (1999) Brain structure and neurocognitive and behavioural function in adolescents who were born very preterm. Lancet 353:1653–1657

    Article  CAS  PubMed  Google Scholar 

  47. 47.

    Griffiths ST, Elgen IB, Chong WK et al (2013) Cerebral magnetic resonance imaging findings in children born extremely preterm, very preterm, and at term. Pediatr Neurol 49:113–118.

    Article  PubMed  Google Scholar 

  48. 48.

    Ifflaender S, Rüdiger M, Konstantelos D et al (2013) Prevalence of head deformities in preterm infants at term equivalent age. Early Hum Dev 89:1041–1047.

    Article  PubMed  Google Scholar 

  49. 49.

    Melbourne L, Murnick J, Chang T et al (2015) Regional brain biometrics at term-equivalent age and developmental outcome in extremely low-birth-weight infants. Am J Perinatol 32:1177–1184.

    Article  PubMed  Google Scholar 

  50. 50.

    Meng C, Bäuml JG, Daamen M et al (2015) Extensive and interrelated subcortical white and gray matter alterations in preterm-born adults. Brain Struct Funct.

  51. 51.

    Nosarti C, Giouroukou E, Healy E et al (2008) Grey and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome. Brain 131:205–217.

    Article  PubMed  Google Scholar 

  52. 52.

    Giger ML (2018) Machine learning in medical imaging. J Am Coll Radiol.

Download references


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.


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



Corresponding author

Correspondence to Alina Jurcoane.

Ethics declarations


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 ( These were on topics of functional MRI, diffusion tensor imaging or behavioral measures; the current article is the only publication with neuroradiological focus.


• prospective

• cross-sectional study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Milka Marinova and Elke Hattingen share authorship.

Electronic supplementary material


(DOCX 21 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation


  • Preterm birth
  • Low birth weight
  • Adults
  • Magnetic resonance imaging