2D linear measures of ventricular enlargement may be relevant markers of brain atrophy and long-term disability progression in multiple sclerosis



Aim of this study was to investigate the reliability and validity of 2D linear measures of ventricular enlargement as indirect markers of brain atrophy and possible predictors of clinical disability.


In this retrospective longitudinal analysis of relapsing-remitting MS patients, brain volumes were computed at baseline and after 2 years. Frontal horn width (FHW), intercaudate distance (ICD), third ventricle width (TVW), and 4th ventricle width were obtained. Two-dimensional measures associated with brain volume at correlation analyses were entered in linear and logistic regression models testing the relationship with baseline clinical disability and 10-year confirmed disability progression (CDP), respectively. Possible cutoff values for clinically relevant atrophy were estimated via receiver operating characteristic (ROC) analyses and probed as 10-year CDP predictors using hierarchical logistic regression.


Eighty-seven patients were available (61/26 = F/M; 34.1 ± 8.5 years). Moderate negative correlations emerged between ICD and TVW and normalized brain volume (NBV; p < 0.001) and percentage brain volume change per year (PBVC/y) and FHW, ICD, and TVW annual changes (p ≤ 0.005). Baseline disability was moderately associated with NBV, ICD, and TVW (p < 0.001), while PBVC/y predicted 10-year CDP (p = 0.01). A cutoff percentage ICD change per year (PICDC/y) value of 4.38%, corresponding to − 0.91% PBVC/y, correlated with 10-year CDP (p = 0.04). These estimated cutoff values provided extra value for predicting 10-year CDP (PBVC/y: p = 0.001; PICDC/y: p = 0.03).


Two-dimensional measures of ventricular enlargement are reproducible and clinically relevant markers of brain atrophy, with ICD and its increase over time showing the best association with clinical disability. Specifically, a cutoff PICDC/y value of 4.38% could serve as a potential surrogate marker of long-term disability progression.

Key Points

Assessment of ventricular enlargement as a rapidly accessible indirect marker of brain atrophy may prove useful in cases in which brain volume quantification is not practicable.

Two-dimensional linear measures of ventricular enlargement represent reliable, valid, and clinically relevant markers of brain atrophy.

A cutoff annualized percentage brain volume change of − 0.91% and the corresponding annualized percentage increase of 4.38% for intercaudate distance are able to discriminate patients who will develop long-term disability progression.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5



4th ventricle width


Area under the receiver operating characteristic curve


Confirmed disability progression


Expanded Disability Status Scale


Frontal horn width




Intraclass correlation coefficient


Intercaudate distance


Multiple sclerosis


Normalized brain volume


No evidence of disease activity


Percentage 4th ventricle width change per year


Percentage brain volume change per year


Percentage frontal horn width change per year


Percentage intercaudate distance change per year


Percentage third ventricle width change per year


Receiver operating characteristic


Total skull diameter


Third ventricle width


White matter


  1. 1.

    Tintore M, Vidal-Jordana A, Sastre-Garriga J (2019) Treatment of multiple sclerosis—success from bench to bedside. Nat Rev Neurol 15:53–58

    CAS  Article  Google Scholar 

  2. 2.

    Wattjes MP, Rovira A, Miller D et al (2015) Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—establishing disease prognosis and monitoring patients. Nat Rev Neurol 11:597–606

    CAS  PubMed  Google Scholar 

  3. 3.

    van Munster CE, Uitdehaag BM (2017) Outcome measures in clinical trials for multiple sclerosis. CNS Drugs 31:217–236

    Article  Google Scholar 

  4. 4.

    Giovannoni G, Turner B, Gnanapavan S, Offiah C, Schmierer K, Marta M (2015) Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Disord 4:329–333

    Article  Google Scholar 

  5. 5.

    Havrdova E, Giovannoni G, Gold R et al (2017) Effect of delayed-release dimethyl fumarate on no evidence of disease activity in relapsing-remitting multiple sclerosis: integrated analysis of the phase III DEFINE and CONFIRM studies. Eur J Neurol 24:726–733

    CAS  Article  Google Scholar 

  6. 6.

    Zivadinov R, Uher T, Hagemeier J et al (2016) A serial 10-year follow-up study of brain atrophy and disability progression in RRMS patients. Mult Scler 22:1709–1718

    Article  Google Scholar 

  7. 7.

    Sanfilipo MP, Benedict RH, Weinstock-Guttman B, Bakshi R (2006) Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology 66:685–692

    Article  Google Scholar 

  8. 8.

    De Stefano N, Stromillo ML, Giorgio A et al (2016) Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry 87:93–99

    Article  Google Scholar 

  9. 9.

    Kappos L, De Stefano N, Freedman MS et al (2016) Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing-remitting multiple sclerosis. Mult Scler 22:1297–1305

    Article  Google Scholar 

  10. 10.

    Smith SM, Zhang Y, Jenkinson M et al (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. NeuroImage 17:479–489

    Article  Google Scholar 

  11. 11.

    Battaglini M, Jenkinson M, De Stefano N (2012) Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 33:2062–2071

    Article  Google Scholar 

  12. 12.

    Turner B, Ramli N, Blumhardt LD, Jaspan T (2001) Ventricular enlargement in multiple sclerosis: a comparison of three-dimensional and linear MRI estimates. Neuroradiology 43:608–614

    CAS  Article  Google Scholar 

  13. 13.

    Butzkueven H, Kolbe SC, Jolley DJ et al (2008) Validation of linear cerebral atrophy markers in multiple sclerosis. J Clin Neurosci 15:130–137

    CAS  Article  Google Scholar 

  14. 14.

    Polman CH, Reingold SC, Banwell B et al (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69:292–302

    Article  Google Scholar 

  15. 15.

    Lublin FD (2014) New multiple sclerosis phenotypic classification. Eur Neurol 72(Suppl 1):1–5

    Article  Google Scholar 

  16. 16.

    Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33:1444–1452

    CAS  Article  Google Scholar 

  17. 17.

    Inglese M, Petracca M, Mormina E et al (2017) Cerebellar volume as imaging outcome in progressive multiple sclerosis. PLoS One 12:e0176519. https://doi.org/10.1371/journal.pone.0176519

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Benedict RH, Weinstock-Guttman B, Fishman I, Sharma J, Tjoa CW, Bakshi R (2004) Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 61:226–230

    Article  Google Scholar 

  19. 19.

    Youden WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35

    CAS  Article  Google Scholar 

  20. 20.

    Dwyer MG, Hagemeier J, Bergsland N et al (2018) Establishing pathological cut-offs for lateral ventricular volume expansion rates. Neuroimage Clin 18:494–501

    Article  Google Scholar 

  21. 21.

    Hegen H, Bsteh G, Berger T (2018) ‘No evidence of disease activity’—is it an appropriate surrogate in multiple sclerosis? Eur J Neurol 25:1107–e1101. https://doi.org/10.1111/ene.13669

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD (2001) Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 54:774–781

    CAS  Article  Google Scholar 

  23. 23.

    Steyerberg EW, Harrell FE Jr (2016) Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69:245–247

    Article  Google Scholar 

  24. 24.

    Young IR, Hall AS, Pallis CA, Legg NJ, Bydder GM, Steiner RE (1981) Nuclear magnetic resonance imaging of the brain in multiple sclerosis. Lancet 2:1063–1066

    CAS  Article  Google Scholar 

  25. 25.

    Rao SM, Glatt S, Hammeke TA et al (1985) Chronic progressive multiple sclerosis. Relationship between cerebral ventricular size and neuropsychological impairment. Arch Neurol 42:678–682

    CAS  Article  Google Scholar 

  26. 26.

    Pasquier F, Leys D, Weerts JG, Mounier-Vehier F, Barkhof F, Scheltens P (1996) Inter- and intraobserver reproducibility of cerebral atrophy assessment on MRI scans with hemispheric infarcts. Eur Neurol 36:268–272

    CAS  Article  Google Scholar 

  27. 27.

    Scheltens P, Pasquier F, Weerts JG, Barkhof F, Leys D (1997) Qualitative assessment of cerebral atrophy on MRI: inter- and intra-observer reproducibility in dementia and normal aging. Eur Neurol 37:95–99

    CAS  Article  Google Scholar 

  28. 28.

    Wang C, Beadnall HN, Hatton SN et al (2016) Automated brain volumetrics in multiple sclerosis: a step closer to clinical application. J Neurol Neurosurg Psychiatry 87:754–757

    CAS  Article  Google Scholar 

  29. 29.

    Caon C, Zvartau-Hind M, Ching W, Lisak RP, Tselis AC, Khan OA (2003) Intercaudate nucleus ratio as a linear measure of brain atrophy in multiple sclerosis. Neurology 60:323–325

    CAS  Article  Google Scholar 

  30. 30.

    Martola J, Stawiarz L, Fredrikson S et al (2008) Rate of ventricular enlargement in multiple sclerosis: a nine-year magnetic resonance imaging follow-up study. Acta Radiol 49:570–579

    CAS  Article  Google Scholar 

  31. 31.

    Muller M, Esser R, Kotter K, Voss J, Muller A, Stellmes P (2013) Third ventricular enlargement in early stages of multiple sclerosis is a predictor of motor and neuropsychological deficits: a cross-sectional study. BMJ Open 3:e003582. https://doi.org/10.1136/bmjopen-2013-003582

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Pontillo G, Cocozza S, Lanzillo R et al (2019) Determinants of deep gray matter atrophy in multiple sclerosis: a multimodal MRI study. AJNR Am J Neuroradiol 40:99–106

    CAS  Article  Google Scholar 

  33. 33.

    Eshaghi A, Prados F, Brownlee WJ et al (2018) Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol 83:210–222

    CAS  Article  Google Scholar 

  34. 34.

    Ghione E, Bergsland N, Dwyer MG et al (2018) Brain atrophy is associated with disability progression in patients with MS followed in a clinical routine. AJNR Am J Neuroradiol 39:2237–2242

    CAS  Article  Google Scholar 

  35. 35.

    Uher T, Vaneckova M, Krasensky J et al (2019) Pathological cut-offs of global and regional brain volume loss in multiple sclerosis. Mult Scler 25:541–553

    Article  Google Scholar 

  36. 36.

    Battaglini M, Gentile G, Luchetti L et al (2019) Lifespan normative data on rates of brain volume changes. Neurobiol Aging 81:30–37

    Article  Google Scholar 

  37. 37.

    Cree BA, Gourraud PA, Oksenberg JR et al (2016) Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 80:499–510

    Article  Google Scholar 

  38. 38.

    Rotstein DL, Healy BC, Malik MT, Chitnis T, Weiner HL (2015) Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA Neurol 72:152–158

    Article  Google Scholar 

  39. 39.

    Rovira A, Wattjes MP, Tintore M et al (2015) Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process. Nat Rev Neurol 11:471–482

    Article  Google Scholar 

  40. 40.

    Traboulsee A, Simon JH, Stone L et al (2016) Revised recommendations of the Consortium of MS Centers Task Force for a standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis. AJNR Am J Neuroradiol 37:394–401

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Sirio Cocozza.

Ethics declarations


The scientific guarantor of this publication is Mario Quarantelli.

Conflict of interest

S.C. and C.R. received fees for speaking from Genzyme.

M.M. has received research grants from ECTRIMS-MAGNIMS and from Merck.

The remaining authors have no conflict of interest to declare.

The remaining 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 (M.Q.) has significant statistical expertise.

Informed consent

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

Ethical approval

Institutional review board approval was obtained.


• Retrospective

• Observational

• Performed at one institution

Additional information

Publisher’s note

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

Electronic supplementary material


(DOCX 29 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pontillo, G., Cocozza, S., Di Stasi, M. et al. 2D linear measures of ventricular enlargement may be relevant markers of brain atrophy and long-term disability progression in multiple sclerosis. Eur Radiol 30, 3813–3822 (2020). https://doi.org/10.1007/s00330-020-06738-4

Download citation


  • Multiple sclerosis
  • Magnetic resonance imaging
  • Brain
  • Atrophy