Abstract
Different measurements of white matter signal abnormalities (WMSA) are often used across studies, which hinders combination of WMSA data from different cohorts. We investigated associations between three commonly used measurements of WMSA, aiming to further understand the association between them and their potential interchangeability: the Fazekas scale, the lesion segmentation tool (LST), and FreeSurfer. We also aimed at proposing cut-off values for estimating low and high Fazekas scale WMSA burden from LST and FreeSurfer WMSA, to facilitate clinical use and interpretation of LST and FreeSurfer WMSA data. A population-based cohort of 709 individuals (all of them 70 years old, 52% female) was investigated. We found a strong association between LST and FreeSurfer WMSA, and an association of Fazekas scores with both LST and FreeSurfer WMSA. The proposed cut-off values were 0.00496 for LST and 0.00321 for FreeSurfer (Total Intracranial volumes (TIV)-corrected values). This study provides data on the association between Fazekas scores, hyperintense WMSA, and hypointense WMSA in a large population-based cohort. The proposed cut-off values for translating LST and FreeSurfer WMSA estimations to low and high Fazekas scale WMSA burden may facilitate the combination of WMSA measurements from different cohorts that used either a FLAIR or a T1-weigthed sequence.
Highlights
The Fazekas scale [1] is a widely used method to visually rate hyperintense white matter signal abnormalities (WMSA) in magnetic resonance imaging (MRI) data, both in clinical practice and research [1,2,3,4]
The paired-sample t-test revealed that lesion segmentation tool (LST) WMSA volumes were significantly larger than FreeSurfer WMSA volumes (t(708)=-15.9;p
Our correlation analyses showed a strong association between LST and FreeSurfer WMSA
Summary
The Fazekas scale [1] is a widely used method to visually rate hyperintense white matter signal abnormalities (WMSA) in magnetic resonance imaging (MRI) data, both in clinical practice and research [1,2,3,4]. In clinical settings high Fazekas WMSA burden has successfully predicted cognitive performance in Alzheimer’s disease patients [6]. The Lesion Segmentation Tool (LST) (https://www.applied-statistics.de/lst.html) [7] is widely used to automatically segment WMSA in the form of hyperintensities in the T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI sequence. The FreeSurfer software (https://surfer.nmr.mgh.harvard.edu/) [8] is increasingly used to automatically segment WMSA in the form of hypointensities in the T1-weighted MRI sequence [9,10,11,12,13]. A limitation of automated methods is that they are complex, time consuming, require quality control, and lack normative data, all of which compromise their clinical use at present
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