基于放射组学的建模揭示了帕金森病与小脑的关系。

IF 2.7 3区 医学 Q3 NEUROSCIENCES
Yini Chen, Yiwei Qi, Yiying Hu, Tao Qiu, Meichen Liu, Qiqi Jia, Yubing Sun, Xinhui Qiu, Bo Sun, Zhanhua Liang, Weidong Le, Tianbai Li
{"title":"基于放射组学的建模揭示了帕金森病与小脑的关系。","authors":"Yini Chen, Yiwei Qi, Yiying Hu, Tao Qiu, Meichen Liu, Qiqi Jia, Yubing Sun, Xinhui Qiu, Bo Sun, Zhanhua Liang, Weidong Le, Tianbai Li","doi":"10.1007/s12311-025-01797-z","DOIUrl":null,"url":null,"abstract":"<p><p>Emerging pathological and neurophysiological evidence has highlighted the cerebellum's involvement in Parkinson's disease (PD). This study aimed to explore the potential of cerebellum-derived magnetic resonance imaging (MRI) radiomics in distinguishing PD patients from healthy controls (HC). A retrospective analysis was conducted using three-dimensional-T1 MRI data (n= 374) from the Parkinson's Progression Markers Initiative (PPMI) dataset (n= 204) and an independent in-house cohort (n= 170). Radiomic features (n= 883) were extracted from the cerebellar gray and white matter of each individual. Three machine learning models were developed: a cerebellar gray matter model, a cerebellar white matter model, and a combined gray and white matter model, to classify PD patients and HC. The results showed that the cerebellar gray matter model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.931 in the training set, with a sensitivity of 60.8% and specificity of 97.1%, while in the testing set, it obtained an AUC of 0.874, with a sensitivity of 86.1% and specificity of 62.6%. The white matter-based model demonstrated an AUC of 0.846 (sensitivity, 59.8%; specificity, 87.3%) in the training set and an AUC of 0.868 (sensitivity, 81.0%; specificity, 75.8%) in the testing set. Notably, the combined gray and white matter model exhibited superior performance, achieving an AUC of 0.936 (sensitivity, 65.7%; specificity, 96.1%) in the training set and an AUC of 0.881 (sensitivity, 82.3%; specificity, 63.7%) in the testing set. Key radiomic features contributing to PD classification included Gray-level Dependence Matrix, Gray-level Co-occurrence Matrix and First-Order from gray matter, as well as Gray-level Size Zone Matrix from white matter, highlighting significant radiomics changes in the cerebellum associated with PD. In conclusion, this study demonstrates that MRI radiomics of cerebellar gray and white matter can effectively differentiate PD patients from HC, supporting the cerebellum's pivotal role in PD pathology and its potential as an imaging biomarker for PD.</p>","PeriodicalId":50706,"journal":{"name":"Cerebellum","volume":"24 2","pages":"48"},"PeriodicalIF":2.7000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiomics-based Modelling Unveils Cerebellar Involvement in Parkinson's Disease.\",\"authors\":\"Yini Chen, Yiwei Qi, Yiying Hu, Tao Qiu, Meichen Liu, Qiqi Jia, Yubing Sun, Xinhui Qiu, Bo Sun, Zhanhua Liang, Weidong Le, Tianbai Li\",\"doi\":\"10.1007/s12311-025-01797-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Emerging pathological and neurophysiological evidence has highlighted the cerebellum's involvement in Parkinson's disease (PD). This study aimed to explore the potential of cerebellum-derived magnetic resonance imaging (MRI) radiomics in distinguishing PD patients from healthy controls (HC). A retrospective analysis was conducted using three-dimensional-T1 MRI data (n= 374) from the Parkinson's Progression Markers Initiative (PPMI) dataset (n= 204) and an independent in-house cohort (n= 170). Radiomic features (n= 883) were extracted from the cerebellar gray and white matter of each individual. Three machine learning models were developed: a cerebellar gray matter model, a cerebellar white matter model, and a combined gray and white matter model, to classify PD patients and HC. The results showed that the cerebellar gray matter model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.931 in the training set, with a sensitivity of 60.8% and specificity of 97.1%, while in the testing set, it obtained an AUC of 0.874, with a sensitivity of 86.1% and specificity of 62.6%. The white matter-based model demonstrated an AUC of 0.846 (sensitivity, 59.8%; specificity, 87.3%) in the training set and an AUC of 0.868 (sensitivity, 81.0%; specificity, 75.8%) in the testing set. Notably, the combined gray and white matter model exhibited superior performance, achieving an AUC of 0.936 (sensitivity, 65.7%; specificity, 96.1%) in the training set and an AUC of 0.881 (sensitivity, 82.3%; specificity, 63.7%) in the testing set. Key radiomic features contributing to PD classification included Gray-level Dependence Matrix, Gray-level Co-occurrence Matrix and First-Order from gray matter, as well as Gray-level Size Zone Matrix from white matter, highlighting significant radiomics changes in the cerebellum associated with PD. In conclusion, this study demonstrates that MRI radiomics of cerebellar gray and white matter can effectively differentiate PD patients from HC, supporting the cerebellum's pivotal role in PD pathology and its potential as an imaging biomarker for PD.</p>\",\"PeriodicalId\":50706,\"journal\":{\"name\":\"Cerebellum\",\"volume\":\"24 2\",\"pages\":\"48\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cerebellum\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12311-025-01797-z\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cerebellum","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12311-025-01797-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

新出现的病理和神经生理学证据强调了小脑在帕金森病(PD)中的参与。本研究旨在探讨小脑源性磁共振成像(MRI)放射组学在区分PD患者和健康对照(HC)方面的潜力。回顾性分析使用来自帕金森进展标志物倡议(PPMI)数据集(n= 204)和独立内部队列(n= 170)的三维t1 MRI数据(n= 374)进行。从每个个体的小脑灰质和白质中提取放射学特征(n= 883)。开发了三种机器学习模型:小脑灰质模型、小脑白质模型和脑灰质与脑白质组合模型,对PD患者和HC进行分类。结果表明,小脑灰质模型在训练集的受试者工作特征(ROC)曲线下面积(AUC)为0.931,灵敏度为60.8%,特异性为97.1%;在测试集的AUC为0.874,灵敏度为86.1%,特异性为62.6%。基于白质的模型AUC为0.846(灵敏度59.8%;特异性为87.3%),AUC为0.868(敏感性为81.0%;特异性为75.8%)。值得注意的是,灰质和白质联合模型表现出更优异的性能,AUC为0.936(灵敏度为65.7%;特异性为96.1%),AUC为0.881(敏感性为82.3%;特异性为63.7%)。有助于PD分类的关键放射学特征包括来自灰质的灰度依赖矩阵、灰度共生矩阵和一阶矩阵,以及来自白质的灰度大小区矩阵,突出了与PD相关的小脑的显著放射组学变化。总之,本研究表明,小脑灰质和白质的MRI放射组学可以有效地区分PD患者和HC患者,支持小脑在PD病理中的关键作用及其作为PD成像生物标志物的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiomics-based Modelling Unveils Cerebellar Involvement in Parkinson's Disease.

Emerging pathological and neurophysiological evidence has highlighted the cerebellum's involvement in Parkinson's disease (PD). This study aimed to explore the potential of cerebellum-derived magnetic resonance imaging (MRI) radiomics in distinguishing PD patients from healthy controls (HC). A retrospective analysis was conducted using three-dimensional-T1 MRI data (n= 374) from the Parkinson's Progression Markers Initiative (PPMI) dataset (n= 204) and an independent in-house cohort (n= 170). Radiomic features (n= 883) were extracted from the cerebellar gray and white matter of each individual. Three machine learning models were developed: a cerebellar gray matter model, a cerebellar white matter model, and a combined gray and white matter model, to classify PD patients and HC. The results showed that the cerebellar gray matter model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.931 in the training set, with a sensitivity of 60.8% and specificity of 97.1%, while in the testing set, it obtained an AUC of 0.874, with a sensitivity of 86.1% and specificity of 62.6%. The white matter-based model demonstrated an AUC of 0.846 (sensitivity, 59.8%; specificity, 87.3%) in the training set and an AUC of 0.868 (sensitivity, 81.0%; specificity, 75.8%) in the testing set. Notably, the combined gray and white matter model exhibited superior performance, achieving an AUC of 0.936 (sensitivity, 65.7%; specificity, 96.1%) in the training set and an AUC of 0.881 (sensitivity, 82.3%; specificity, 63.7%) in the testing set. Key radiomic features contributing to PD classification included Gray-level Dependence Matrix, Gray-level Co-occurrence Matrix and First-Order from gray matter, as well as Gray-level Size Zone Matrix from white matter, highlighting significant radiomics changes in the cerebellum associated with PD. In conclusion, this study demonstrates that MRI radiomics of cerebellar gray and white matter can effectively differentiate PD patients from HC, supporting the cerebellum's pivotal role in PD pathology and its potential as an imaging biomarker for PD.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cerebellum
Cerebellum 医学-神经科学
CiteScore
6.40
自引率
14.30%
发文量
150
审稿时长
4-8 weeks
期刊介绍: Official publication of the Society for Research on the Cerebellum devoted to genetics of cerebellar ataxias, role of cerebellum in motor control and cognitive function, and amid an ageing population, diseases associated with cerebellar dysfunction. The Cerebellum is a central source for the latest developments in fundamental neurosciences including molecular and cellular biology; behavioural neurosciences and neurochemistry; genetics; fundamental and clinical neurophysiology; neurology and neuropathology; cognition and neuroimaging. The Cerebellum benefits neuroscientists in molecular and cellular biology; neurophysiologists; researchers in neurotransmission; neurologists; radiologists; paediatricians; neuropsychologists; students of neurology and psychiatry and others.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信