Reliability and Sensitivity of Visual Scales versus Volumetry for Evaluating White Matter Hyperintensity Progression

Background: Investigating associations between the change of white matter hyperintensities (WMH) and clinical symptoms over time is crucial for establishing a causal relationship. However, the most suitable method for measuring WMH progression has not been established yet. We compared the reliability and sensitivity of cross-sectional and longitudinal visual scales with volumetry for measuring WMH progression. Methods: Twenty MRI scan pairs (interval 2 years) were included from the Amsterdam center of the LADIS study. Semi-automated volumetry of WMH was performed twice by one rater. Three cross-sectional scales (Fazekas Scale, Age-Related White Matter Changes Scale, Scheltens Scale) and two progression scales (Rotterdam Progression Scale, Schmidt Progression Scale) were scored by 4 and repeated by 2 raters. Results: Mean WMH volume (24.6 ± 27.9 ml at baseline) increased by 4.6 ± 5.1 ml [median volume change (range) = 2.7 (–0.6 to 15.7) ml]. Measuring volumetric change in WMH was reliable (intraobserver:intraclass coefficient = 0.88). All visual scales showed significant change of WMH over time, although the sensitivity was highest for both of the progression scales. Proportional volumetric change of WMH correlated best with the Rotterdam Progression Scale (Spearman’s r = 0.80, p < 0.001) and the Schmidt Progression Scale (Spearman’s r = 0.64, p < 0.01). Although all scales were reliable for assessment of WMH cross-sectionally, WMH progression assessment using visual scales was less reliable, except for the Rotterdam Progression scale which had moderate to good reliability [weighted Cohen’s ĸ = 0.63 (intraobserver), 0.59 (interobserver)]. Conclusion: To determine change in WMH, dedicated progression scales are more sensitive and/or reliable and correlate better with volumetric volume change than cross-sectional scales.

[1]  A. Alavi,et al.  MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. , 1987, AJR. American journal of roentgenology.

[2]  Douglas G. Altman,et al.  Practical statistics for medical research , 1990 .

[3]  Donald Hedeker,et al.  Estimation of Effect Size From a Series of Experiments Involving Paired Comparisons , 1993 .

[4]  P. Scheltens,et al.  A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging , 1993, Journal of the Neurological Sciences.

[5]  F. Fazekas,et al.  MRI white matter hyperintensities , 1999, Neurology.

[6]  A. Hofman,et al.  Cerebral white matter lesions and cognitive function: The Rotterdam scan study , 2000, Annals of neurology.

[7]  Matthijs Oudkerk,et al.  Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. , 2000 .

[8]  A Hofman,et al.  Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study , 2001, Journal of neurology, neurosurgery, and psychiatry.

[9]  R. Baloh,et al.  A prospective study of cerebral white matter abnormalities in older people with gait dysfunction , 2001, Neurology.

[10]  J M Wardlaw,et al.  Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study: the Rotterdam Scan Study , 2001, Journal of neurology, neurosurgery, and psychiatry.

[11]  P. Scheltens,et al.  A New Rating Scale for Age-Related White Matter Changes Applicable to MRI and CT , 2001, Stroke.

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

[13]  F. Fazekas,et al.  Evolution of White Matter Lesions , 2002, Cerebrovascular Diseases.

[14]  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.

[15]  P. Scheltens,et al.  White matter lesion progression , 2004, Neurology.

[16]  P. Scheltens,et al.  Measuring progression of cerebral white matter lesions on MRI , 2004, Neurology.

[17]  P. Scheltens,et al.  Impact of Age-Related Cerebral White Matter Changes on the Transition to Disability – The LADIS Study: Rationale, Design and Methodology , 2004, Neuroepidemiology.

[18]  Chris Frost,et al.  The analysis of repeated ‘direct’ measures of change illustrated with an application in longitudinal imaging , 2004, Statistics in medicine.

[19]  Johan H. C. Reiber,et al.  Fully automatic segmentation of white matter hyperintensities in MR images of the elderly , 2005, NeuroImage.

[20]  J. O'Brien,et al.  White matter hyperintensities and depression—preliminary results from the LADIS study , 2005, International journal of geriatric psychiatry.

[21]  P. Scheltens,et al.  Age, Hypertension, and Lacunar Stroke Are the Major Determinants of the Severity of Age-Related White Matter Changes , 2006, Cerebrovascular Diseases.

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

[23]  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.

[24]  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.