Diffusion magnetic resonance histograms as a surrogate marker and predictor of disease progression in CADASIL: a two-year follow-up study

Abstract

Background and purpose: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a cerebral small vessel disease caused by mutations in the NOTCH3 gene. MRI is sensitive in detecting preclinical involvement and changes over time. However, little is known about correlations between MRI metrics and clinical measures on a longitudinal scale. In this study, we assessed the role of quantitative MRI (T2-lesion volume and diffusion tensor imaging [DTI]-derived metrics) in monitoring and predicting disease progression.

Methods: Sixty-two CADASIL subjects were followed prospectively over a period of 26.3+/-1.2 months. Dual-echo scans, DTI scans, and clinical scales were obtained at baseline and at follow-up. T2-lesion volumes were determined quantitatively, and histograms of mean diffusivity (MD) were produced.

Results: At follow-up, T2-lesion volumes and MD histogram metrics had changed significantly (all P<0.01). Lesion volumes and average MD correlated with clinical scores at baseline. Changes of average MD correlated with changes of the Rankin score, the National Institutes of Health Stroke Scale score, and the structured interview for the diagnosis of Alzheimer dementia and multiinfarct dementia score (all P<0.01). On multivariate analysis, average MD and systolic blood pressure at baseline were predictors of changes of average MD during follow-up. Moreover, average MD was the main predictor of clinical progression. Sample size estimates showed that the number of individuals required to detect a treatment effect in an interventional trial may be reduced when using MD histograms as an end point.

Conclusions: This study establishes correlations between changes of DTI histogram metrics and clinical measures over time. DTI histograms may be used as an adjunct outcome measure in future therapeutic trials. Moreover, DTI histogram metrics predict disease progression in CADASIL.