TY - JOUR
T1 - Cortical Thin Patch Fraction Reflects Disease Burden in MS
T2 - The Mosaic Approach
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
N1 - Publisher Copyright:
© 2024 American Society of Neuroradiology. All rights reserved.
PY - 2024/1/10
Y1 - 2024/1/10
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85181806297&partnerID=8YFLogxK
U2 - 10.3174/ajnr.A8064
DO - 10.3174/ajnr.A8064
M3 - Article
C2 - 38164526
AN - SCOPUS:85181806297
SN - 0195-6108
VL - 45
SP - 82
EP - 89
JO - American Journal of Neuroradiology
JF - American Journal of Neuroradiology
IS - 1
ER -