[1]宋心雨,孙 正,李跃华.比较基于CT图像的纹理分析与临床评分在预测急性缺血性脑卒中出血性转化中的价值[J].介入放射学杂志,2024,33(03):230-235.
 SONG Xinyu,SUN Zheng,LI Yuehua.. CT images texture analysis versus clinical scores in predicting hemorrhagic transformation of acute ischemic stroke[J].journal interventional radiology,2024,33(03):230-235.
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比较基于CT图像的纹理分析与临床评分在预测急性缺血性脑卒中出血性转化中的价值()

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《介入放射学杂志》[ISSN:1008-794X/CN:31-1796/R]

卷:
33
期数:
2024年03
页码:
230-235
栏目:
神经介入
出版日期:
2024-03-27

文章信息/Info

Title:
 CT images texture analysis versus clinical scores in predicting hemorrhagic transformation of acute ischemic stroke
作者:
宋心雨 孙 正 李跃华
Author(s):
SONG Xinyu SUN Zheng LI Yuehua.
Department of Interventional Radiology, Affiliated Sixth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200233, China
关键词:
【关键词】 缺血性脑卒中 出血性转化 纹理分析 临床评分 预测
文献标志码:
A
摘要:
【摘要】 目的 本研究试图利用CT图像的纹理特征预测缺血性脑卒中出血性转化(hemorrhagic transformation,HT)的发生,并与传统的临床预测评分进行比较。方法 本研究共纳入73例急性前循环缺血性脑卒中患者,所有患者都进行再灌注治疗。根据随访ADC图像弥散受限区勾画出梗死区感兴趣区域(region of interesting,ROI),并匹配到计算机断层血管造影(computed tomographic angiography,CTA)和平扫CT (non- contrast CT,NCCT)相应的缺血区域。在所有患者中随机抽取5个HT和5个非HT组成测试组,余下组成训练组。分别在CTA和NCCT训练组中选取最有预测价值的6个纹理特征,然后将这些特征带入不同分类器进行5倍交叉验证训练,最后根据训练后的分类器对测试组进行预测评估。此外,对所有训练组患者进行4项临床评分(HAT、SEDAN、HIAT2、THRIVE- c)。结果 训练后的分类器模型在CTA和NCCT中都表现出明显的预测价值。在CTA预测模型中,线性支持向量机(linear SVM)分类器预测效能最好,其在5倍交叉验证中的平均预测准确度为0.816,AUC值为0.890;在测试组中预测准确度为0.800,敏感性为0.600,特异性为1.000。逻辑回归(logistic regression,LR)为NCCT中表现最好的分类器,但NCCT模型HT的预测性能稍逊于CTA模型,其在训练组中预测准确度为0.697,AUC值为0.763。NCCT测试组的预测准确度为0.700,敏感性为0.600,特异性为0.800。相比于纹理分析模型,4种临床评分预测表现较差,AUC值均小于0.700。结论 基于CT图像脑缺血区的纹理分析(CTA和NCCT)具有预测AIS患者再灌注治疗后HT的能力,预测效能优于传统的临床评分方法。

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备注/Memo

备注/Memo:
(收稿日期:2023- 06- 06)
(本文编辑:茹 实)
更新日期/Last Update: 2024-03-27