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PT  - JOURNAL ARTICLE
AU  - Tahedl, Marlene
AU  - Wiltgen, Tun
AU  - Voon, Cui Ci
AU  - Berthele, Achim
AU  - Kirschke, Jan S.
AU  - Hemmer, Bernhard
AU  - Mühlau, Mark
AU  - Zimmer, Claus
AU  - Wiestler, Benedikt
TI  - Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach
AID  - 10.3174/ajnr.A8064
DP  - 2024 Jan 01
TA  - American Journal of Neuroradiology
PG  - 82--89
VI  - 45
IP  - 1
4099  - http://www.ajnr.org/content/45/1/82.short
4100  - http://www.ajnr.org/content/45/1/82.full
SO  - Am. J. Neuroradiol.2024 Jan 01; 45
AB  - BACKGROUND AND PURPOSE: GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the “mosaic approach,” which was previously suggested as a biomarker for assessing and localizing atrophy.MATERIALS AND METHODS: We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling.RESULTS: The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume.CONCLUSIONS: This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.AIartificial intelligenceCamCANCambridge Center for Ageing and NeuroscienceCISclinically isolated syndromeCThcortical thicknessEDSSExpanded Disability Status ScaleHChealthy controlsIQRinterquartile rangeMAPmosaic approachMuSICMultiple Sclerosis Inventory of CognitionPMSprogressive MSRRMSrelapsing-remitting MSTPFthin patch fractionTUMTechnical University of Munich