缓慢扩大的病变与复发型多发性硬化症的疾病活动和灰质丢失有关。

IF 2.3 4区 医学 Q3 CLINICAL NEUROLOGY Journal of Neuroimaging Pub Date : 2024-10-10 DOI:10.1111/jon.13243
Hiroaki Yokote, Yusei Miyazaki, Juichi Fujimori, Yoichiro Nishida, Shuta Toru, Masaaki Niino, Ichiro Nakashima, Yoshiharu Miura, Takanori Yokota
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引用次数: 0

摘要

背景和目的:缓慢扩展病灶(SEL)被认为是多发性硬化症(MS)慢性活动性病灶的新型磁共振成像标记物。然而,SEL影响多发性硬化症患者脑容量损失的机制仍不清楚。此外,SELs在亚洲多发性硬化症患者中的发病率和意义仍不清楚。本研究旨在调查日本患者中SEL与无疾病活动证据(NEDA)-3状态以及脑容量损失之间的关系:方法:对99名复发型多发性硬化症患者进行了回顾性评估。方法:我们对 99 名复发多发性硬化症患者进行了回顾性评估,并根据纵向连续扫描时的局部变形在脑部 MRI 上识别出 SEL。我们建立了逻辑回归模型和广义线性混合模型(GLMMs)来评估SEL数量与疾病活动和脑容量变化之间的关联:在观察期间(2.0 ± 0.22 年),35 名患者至少出现了一次 SEL。多变量逻辑回归分析显示,≥2次SEL与达到NEDA-3状态的0.2倍风险相关。GLMMs显示,经多重比较校正后,SEL的数量与皮层(p = .00169)和皮层下灰质(p = .00964)的体积变化呈负相关:结论:在两年的观察期内,日本多发性硬化症患者中发现了SEL。结论:在日本多发性硬化症患者的两年观察期内发现了SELs,SELs的数量与疾病活动性和脑容量损失有关,这表明SELs的数量可能是多发性硬化症疾病活动性的生物标志物。
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Slowly expanding lesions are associated with disease activity and gray matter loss in relapse-onset multiple sclerosis.

Background and purpose: Slowly expanding lesions (SELs) have been proposed as novel MRI markers of chronic active lesions in multiple sclerosis (MS). However, the mechanism through which SELs affect brain volume loss in patients with MS remains unknown. Additionally, the prevalence and significance of SELs in Asian patients with MS remain unclear. This study aimed to investigate the association between SELs and no evidence of disease activity (NEDA)-3 status as well as brain volume loss in Japanese patients.

Methods: A total of 99 patients with relapse-onset MS were retrospectively evaluated. SELs were identified on brain MRI based on local deformation when consecutive scans were registered longitudinally. We developed a logistic regression model and generalized linear mixed models (GLMMs) to evaluate the association between the number of SELs and disease activity and changes in brain volume.

Results: During the observation period (2.0 ± 0.22 years), 35 patients developed at least one SEL. Multivariable logistic regression analysis showed that ≥2 SELs were associated with 0.2 times the risk of achieving a NEDA-3 status. GLMMs revealed that the number of SELs was negatively associated with volume changes in the cortex (p = .00169) and subcortical gray matter (p = .00964) after correction for multiple comparisons.

Conclusion: SELs were identified in Japanese patients with MS during the 2-year observation period. The number of SELs is associated with disease activity and brain volume loss, suggesting that the number of SELs could be a biomarker of disease activity in MS.

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来源期刊
Journal of Neuroimaging
Journal of Neuroimaging 医学-核医学
CiteScore
4.70
自引率
0.00%
发文量
117
审稿时长
6-12 weeks
期刊介绍: Start reading the Journal of Neuroimaging to learn the latest neurological imaging techniques. The peer-reviewed research is written in a practical clinical context, giving you the information you need on: MRI CT Carotid Ultrasound and TCD SPECT PET Endovascular Surgical Neuroradiology Functional MRI Xenon CT and other new and upcoming neuroscientific modalities.The Journal of Neuroimaging addresses the full spectrum of human nervous system disease, including stroke, neoplasia, degenerating and demyelinating disease, epilepsy, tumors, lesions, infectious disease, cerebral vascular arterial diseases, toxic-metabolic disease, psychoses, dementias, heredo-familial disease, and trauma.Offering original research, review articles, case reports, neuroimaging CPCs, and evaluations of instruments and technology relevant to the nervous system, the Journal of Neuroimaging focuses on useful clinical developments and applications, tested techniques and interpretations, patient care, diagnostics, and therapeutics. Start reading today!
期刊最新文献
Multicenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis. Neuromuscular ultrasound for the differential diagnosis of radial tunnel syndrome and lateral epicondylitis. Quantification of porcine lower thoracic spinal cord morphology with intact dura mater using high-resolution μCT. Slowly expanding lesions are associated with disease activity and gray matter loss in relapse-onset multiple sclerosis. Deep learning from head CT scans to predict elevated intracranial pressure.
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