Significant enhancement of occluded segment on magnetic resonance imaging predicts severe stenosis in atherosclerotic occlusion

Chen Cao , Jing Lei , Yan Gong , Jiwei Wang , Bo Wang , Gemuer Wu , Lei Ren , Song Liu , Jinxia Zhu , Ming Wei , Song Jin , Shuang Xia
{"title":"Significant enhancement of occluded segment on magnetic resonance imaging predicts severe stenosis in atherosclerotic occlusion","authors":"Chen Cao ,&nbsp;Jing Lei ,&nbsp;Yan Gong ,&nbsp;Jiwei Wang ,&nbsp;Bo Wang ,&nbsp;Gemuer Wu ,&nbsp;Lei Ren ,&nbsp;Song Liu ,&nbsp;Jinxia Zhu ,&nbsp;Ming Wei ,&nbsp;Song Jin ,&nbsp;Shuang Xia","doi":"10.1016/j.metrad.2023.100021","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>The difficulty of recanalization for intracranial atherosclerosis–related large vessel occlusion (ICAS-LVO) is closely related to the severity of stenosis. This study sought to investigate the characteristics of enhancement based on high-resolution magnetic resonance imaging (HR-MRI) so as to judge the severity of stenosis.</p></div><div><h3>Methods</h3><p>Sixty-two patients with symptomatic ICAS-LVOs who underwent endovascular treatment were prospectively recruited for HR-MRI (33 patients with severe stenosis and 29 without). The diagnostic agreements in locating occlusion segments were assessed between HR-MRI and angiographic assessment. The severity of atherosclerotic stenosis was evaluated by enhancement grade and quantitative enhancement index. Univariate and multivariate analyses were used to identify the parameters associated with the severity of stenosis.</p></div><div><h3>Results</h3><p>HR-MRI showed good agreement with angiographic assessments for evaluating the occlusion site (κ ​= ​0.97) and length (concordance correlation coefficient ​= ​0.70). Compared with patients without severe stenosis, patients with severe stenosis exhibited higher enhancement index (0.69 versus 0.19; <em>p</em> ​&lt; ​0.001) of occlusion segments. In multivariate analysis, the enhancement index was an independent factor associated with the severity of stenosis (OR ​= ​2.92; 95% CI, 1.60–5.34, <em>p</em> ​&lt; ​0.001). The enhancement index had an AUC of 0.89, with a sensitivity of 76.0% and a specificity of 86.0%. The model fit improved when including the enhancement index (AUC ​= ​0.93 versus 0.72). All of patients with severe stenosis required additional rescue treatments, which have a longer procedural time (104.0 versus 91.0 ​min; <em>p</em> ​= ​0.002).</p></div><div><h3>Conclusion</h3><p>Higher enhancement index of occlusion segments was associated with the severe atherosclerotic stenosis.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"1 2","pages":"Article 100021"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meta-Radiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950162823000218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose

The difficulty of recanalization for intracranial atherosclerosis–related large vessel occlusion (ICAS-LVO) is closely related to the severity of stenosis. This study sought to investigate the characteristics of enhancement based on high-resolution magnetic resonance imaging (HR-MRI) so as to judge the severity of stenosis.

Methods

Sixty-two patients with symptomatic ICAS-LVOs who underwent endovascular treatment were prospectively recruited for HR-MRI (33 patients with severe stenosis and 29 without). The diagnostic agreements in locating occlusion segments were assessed between HR-MRI and angiographic assessment. The severity of atherosclerotic stenosis was evaluated by enhancement grade and quantitative enhancement index. Univariate and multivariate analyses were used to identify the parameters associated with the severity of stenosis.

Results

HR-MRI showed good agreement with angiographic assessments for evaluating the occlusion site (κ ​= ​0.97) and length (concordance correlation coefficient ​= ​0.70). Compared with patients without severe stenosis, patients with severe stenosis exhibited higher enhancement index (0.69 versus 0.19; p ​< ​0.001) of occlusion segments. In multivariate analysis, the enhancement index was an independent factor associated with the severity of stenosis (OR ​= ​2.92; 95% CI, 1.60–5.34, p ​< ​0.001). The enhancement index had an AUC of 0.89, with a sensitivity of 76.0% and a specificity of 86.0%. The model fit improved when including the enhancement index (AUC ​= ​0.93 versus 0.72). All of patients with severe stenosis required additional rescue treatments, which have a longer procedural time (104.0 versus 91.0 ​min; p ​= ​0.002).

Conclusion

Higher enhancement index of occlusion segments was associated with the severe atherosclerotic stenosis.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
磁共振成像上闭塞段的显著增强预示着动脉粥样硬化闭塞的严重狭窄
目的颅内动脉粥样硬化相关大血管闭塞(ICAS-LVO)再通困难与狭窄程度密切相关。本研究旨在探讨基于高分辨率磁共振成像(HR-MRI)的增强特征,以判断狭窄的严重程度。方法前瞻性招募62例接受血管内治疗的症状性ICAS LVO患者(33例重度狭窄,29例无)进行HR-MRI。HR-MRI和血管造影评估在定位闭塞节段方面的诊断一致性。通过增强分级和定量增强指数评价动脉粥样硬化性狭窄的严重程度。单变量和多变量分析用于确定与狭窄严重程度相关的参数。结果HR MRI与血管造影评估结果吻合良好(κ​=​0.97)和长度(一致性相关系数​=​0.70)。与没有严重狭窄的患者相比,严重狭窄患者表现出更高的增强指数(0.69对0.19;p​<;​0.001)。在多变量分析中,增强指数是与狭窄严重程度相关的独立因素(OR​=​2.92;95%置信区间,1.60–5.34,p​<;​0.001)。增强指数的AUC为0.89,敏感性为76.0%,特异性为86.0%。当包括增强指数(AUC​=​0.93对0.72)。所有严重狭窄的患者都需要额外的抢救治疗,手术时间更长(104.0对91.0​min;p​=​0.002)。结论闭塞节段强化指数越高,动脉粥样硬化性狭窄越严重。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Advancements in the application of deep learning for coronary artery calcification Rethinking the studies of diagnostic biomarkers for mental disorders One scan, multiple insights: A review of AI-Driven biomarker imaging and composite measure detection in lung cancer screening A systematic evaluation of GPT-4V's multimodal capability for chest X-ray image analysis Integrating AI in college education: Positive yet mixed experiences with ChatGPT
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1