Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis.
This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ens...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1062 - 1076 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081739&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081739 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081739 184081739 184081739 10.1007/s10278-024-01235-2 184081739 ppf: 1062 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis. aug: au: Yang, Yifeng Hu, Liangyun Chen, Yang Gu, Weidong Xie, Yuanzhong Nie, Shengdong affil: https://ror.org/00ay9v204 School of Health Science and Engineering, University of Shanghai for Science and Technology, No. 516 Military 21 Road, Yangpu District, 200093, Shanghai, People's Republic of China sug: subj: Parkinson Disease Diagnosis Parkinson Disease Radiography Sex Factors Biological Markers Blood Magnetic Resonance Imaging Methods Machine Learning Human Male Female Middle Age Aged Multicenter Studies Brain Pathology Brain Radiography Algorithms Chi Square Test Mann-Whitney U Test Spearman's Rank Correlation Coefficient Statistical Significance Data Analysis Software Descriptive Statistics Funding Source Middle Aged: 45-64 years Aged: 65+ years Male Female ab: This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ensemble Lasso (EnLasso) method was employed to identify a stable optimal feature subset for each sex-based subgroup. Eight typical classifiers were adopted to construct classification models for PD and HC, respectively, to validate whether models specific to sex subgroups could bolster the precision of PD identification. Finally, statistical analysis and correlation tests were carried out on significant brain region features to identify potential sex-specific imaging biomarkers. The best model (MLP) based on the female subgroup and male subgroup achieved average classification accuracy of 92.83% and 92.11%, respectively, which were better than that of the model based on the overall samples (86.88%) and the overall model incorporating gender factor (87.52%). In addition, the most discriminative feature of PD among males was the lh 6r (FD), but among females, it was the lh PreS (GI). The findings indicate that the sex-specific PD diagnosis model yields a significantly higher classification performance compared to previous models that included all participants. Additionally, the male subgroup exhibited a greater number of brain region changes than the female subgroup, suggesting sex-specific differences in PD risk markers. This study underscore the importance of stratifying data by sex and offer insights into sex-specific variations in PD phenotypes, which could aid in the development of precise and personalized diagnostic approaches in the early stages of the disease. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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