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

作者全名："Liang, Xueheng; Yan, Zichun; Li, Yongmei"

作者地址："[Liang, Xueheng; Yan, Zichun; Li, Yongmei] Chongqing Med Univ, Affiliated Hosp 1, Dept Dermatol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Liang, Xueheng] Chongqing Med Univ, Banan Hosp, Dept Radiol, Chongqing, Peoples R China"

通信作者："Li, YM (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Dermatol, 1 Youyi Rd, Chongqing 400016, Peoples R China."

来源：JAPANESE JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001171750400001

JCR分区：Q2

影响因子：2.9

年份：2024

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Multiple sclerosis; Unsupervised machine learning; White matter; Automated fiber quantification; Disability status; Cognitive impairment

摘要："PurposeThis 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 methodsWe 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.ResultsThe 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.ConclusionsSubtyping 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."

基金机构：The Key Project of Technological Innovation and Application Development of Chongqing Science and Technology Bureau

基金资助正文：No Statement Available