Close Correlation between Quantitative and Qualitative Assessments of White Matter Lesions

Background: White matter lesions (WML) increase with age and are associated with stroke, cognitive decline and dementia. They can be visually rated or computationally assessed. Methods: We compared WML Fazekas visual rating scores and volumes, determined using a validated multispectral image-fusion technique, in Magnetic Resonance Imaging from 672 participants of the Lothian Birth Cohort 1936 and sought explanations for subjects in whom the correlation (Spearman’s ρ) between the total Fazekas score (summed deep and periventricular ratings, 0–6) and WML volume did not concur (z-score difference >1). Infarcts were identified separately. Results: The median WML Fazekas score was 2 [inter-quartile range (IQR): 2], median WML volume 7.7 ml (IQR: 13.6 ml) and median infarct volume (n = 95) 0.98 ml. Score and volume were highly correlated (Spearman’s ρ = 0.78, p < 0.001). Infarcts did not alter the correlation. Minor discordance occurred in 94/672 (14%) subjects, most with total Fazekas score of 1 (n = 20, WML volume = 4.5–14.8 ml) or 2 (n = 50, WML volume = 0.1–34.4 ml). The main reasons were: subtle WML identified visually but omitted from the volume; prominent ventricular caps but thin body lining giving a periventricular score of 1/2 but large WML volume, and small deep focal lesions which increase the score disproportionally when beginning to coalesce with little change in WML volume. Conclusions: WML rating scores and volumes provide near-equivalent estimates of WML burden, therefore either can be used depending on research circumstances. Even closer agreement could result from improved computational detection of subtle WML and modified visual ratings to differentiate prominent ventricular caps from thin periventricular linings, and small non-coalescent from early coalescent deep WML.

[1]  Carole Dufouil,et al.  Antihypertensive Treatment and Change in Blood Pressure Are Associated With the Progression of White Matter Lesion Volumes: The Three-City (3C)–Dijon Magnetic Resonance Imaging Study , 2011, Circulation.

[2]  Maria del C. Valdés Hernández,et al.  Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques. , 2012, Magnetic resonance imaging.

[3]  F. Barkhof,et al.  CT and MRI Rating of White Matter Lesions , 2002, Cerebrovascular Diseases.

[4]  I. Deary,et al.  Brain Aging, Cognition in Youth and Old Age and Vascular Disease in the Lothian Birth Cohort 1936: Rationale, Design and Methodology of the Imaging Protocol* , 2011, International journal of stroke : official journal of the International Stroke Society.

[5]  J. Garcìa,et al.  The significance of cerebral white matter abnormalities 100 years after Binswanger's report. A review. , 1995, Stroke.

[6]  H Lechner,et al.  White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors. , 1988, Stroke.

[7]  W. M. van der Flier,et al.  Reliability and Sensitivity of Visual Scales versus Volumetry for Evaluating White Matter Hyperintensity Progression , 2008, Cerebrovascular Diseases.

[8]  Karen J. Ferguson,et al.  New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images , 2010, European Radiology.

[9]  F. Fazekas,et al.  Simple versus complex assessment of white matter hyperintensities in relation to physical performance and cognition: the LADIS study , 2006, Journal of Neurology.

[10]  O Almkvist,et al.  Visual Rating of Age-Related White Matter Changes on Magnetic Resonance Imaging: Scale Comparison, Interrater Agreement, and Correlations With Quantitative Measurements , 2003, Stroke.

[11]  P M Moodie,et al.  COHORT , 1971, The Medical journal of Australia.

[12]  C. Enzinger,et al.  Progression of cerebral white matter lesions — Clinical and radiological considerations , 2007, Journal of the Neurological Sciences.

[13]  G J Blauw,et al.  Measuring longitudinal white matter changes: comparison of a visual rating scale with a volumetric measurement. , 2006, AJNR. American journal of neuroradiology.

[14]  O Salonen,et al.  Variable agreement between visual rating scales for white matter hyperintensities on MRI. Comparison of 13 rating scales in a poststroke cohort. , 1997, Stroke.

[15]  D. Harvey,et al.  Anatomical Mapping of White Matter Hyperintensities (WMH): Exploring the Relationships Between Periventricular WMH, Deep WMH, and Total WMH Burden , 2005, Stroke.

[16]  P. Scheltens,et al.  Impact of White Matter Hyperintensities Scoring Method on Correlations With Clinical Data: The LADIS Study , 2006, Stroke.

[17]  B J Bedell,et al.  A dual approach for minimizing false lesion classifications on magnetic resonance images , 1997, Magnetic resonance in medicine.

[18]  Karen J. Ferguson,et al.  White matter hyperintensities and rating scales—observer reliability varies with lesion load , 2004, Journal of Neurology.

[19]  P. Visscher,et al.  The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond , 2007, BMC geriatrics.

[20]  Wei Wen,et al.  The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals , 2004, NeuroImage.