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Japanese Journal of Radiology
Erschienen in:

27.02.2024 | Original Article

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification

verfasst von: Xueheng Liang, Zichun Yan, Yongmei Li

Erschienen in: Japanese Journal of Radiology | Ausgabe 6/2024

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Abstract

Purpose

This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes.

Materials and methods

We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment.

Results

The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between.

Conclusions

Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.
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Metadaten
Titel
Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification
verfasst von
Xueheng Liang
Zichun Yan
Yongmei Li
Publikationsdatum
27.02.2024
Verlag
Springer Nature Singapore
Erschienen in
Japanese Journal of Radiology / Ausgabe 6/2024
Print ISSN: 1867-1071
Elektronische ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-024-01535-1

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