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神经影像学在小血管疾病研究中的标准——2013 年以来的进展。

Neuroimaging standards for research into small vessel disease-advances since 2013.

机构信息

Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.

Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands.

出版信息

Lancet Neurol. 2023 Jul;22(7):602-618. doi: 10.1016/S1474-4422(23)00131-X. Epub 2023 May 23.


DOI:10.1016/S1474-4422(23)00131-X
PMID:37236211
Abstract

Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.

摘要

脑小血管病(SVD)在衰老过程中很常见,可表现为卒中、认知功能下降、神经行为症状或功能障碍。SVD 常与神经退行性疾病共存,并可加重认知和其他症状,影响日常生活活动。血管影像学改变报告标准 1(STRIVE-1)对结构 MRI 上可见的 SVD 的多种特征进行了分类和标准化。此后,关于这些已确立的 SVD 标志物以及新的 MRI 序列和影像学特征的新信息不断涌现。随着 SVD 影像学特征综合作用的效果变得更加清晰,定量影像学生物标志物在确定亚可见组织损伤、高场强 MRI 可见的细微异常以及病变-症状模式方面的关键作用也显而易见。这些指标与快速发展的机器学习方法一起,比单独的结构 MRI 特征更全面地捕捉 SVD 对大脑的影响,并作为临床试验和未来常规实践中的中介结果。我们采用与 STRIVE-1 相同的方法,更新了关于衰老和神经退行性疾病研究中血管变化的神经影像学指导意见,创建了 STRIVE-2。

相似文献

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Lancet Neurol. 2023-7

[2]
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[3]
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[4]
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