基于深度学习的CT白质病变体积与急性缺血性中风后的结果有关
Henk van Voorst1,2, Johanna Pitkänen3, Laura van Poppel4,5
1Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. h.vanvoorst@amsterdamumc.nl.
European radiology
|January 29, 2024
概括
对于白质病变 (DL-WML) 的深度学习和Fazekas尺度也类似地预测了在EVT之前接受IVT的急性缺血性中风患者的结果. 需要进行进一步的研究,以根据白质病变的严重程度来优化IVT的使用.
科学领域:
- 神经学 神经学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 在急性缺血性中风的内血管治疗 (EVT) 前进行静脉内血栓溶解 (IVT) 会带来脑内出血的风险.
- 在CT上精确测量白质病变 (WML) 对于安全的IVT施用至关重要.
- 深度学习 (DL) 为量化WML体积提供了一个潜在的方法.
研究的目的:
- 开发和验证用于CT上的WML体积测量的DL模型.
- 为了比较DL-WML体积的预测值与Fazekas尺度 (WML-Faz) 对症状性脑内出血 (sICH) 和功能性结局的预测值.
- 评估WML措施是否会改变IVT在接受EVT的患者的治疗效果.
主要方法:
- 为CT上的WML细分开发了DL模型并进行了验证.
- 对MR CLEAN No-IV试验进行了后期分析.
- 使用回归模型分析了DL-WML体积,WML-Faz,sICH和90天功能结果 (mRS) 之间的关联.
主要成果:
- DL-WML体积和WML-Faz都与sICH和mRS相关.
- DL-WML体积显示,sICH的调整几率比 (acOR) 为1.78,mRS的调整几率比为0.70.
- WML-Faz显示sICH的acOR为1.53,mRS的acOR为0.73;对于任何一项测量都没有观察到对mRS的IVT效应显著的修改.
结论:
- 在急性缺血性中风患者中,DL-WML体积和WML-Faz对sICH和功能结果具有相似的预测能力.
- 虽然增加WML严重程度,特别是WML-Faz,可能与IVT后更病的相关,但它并没有导致更糟糕的功能结果.
- 需要进一步的研究,包括患者层面的元分析,以探索WML严重性导向IVT选择在EVT之前的好处.
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