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机器学习模型以CT为基础对缺血性中风发病时间在4.5小时内或超过4.5小时的基于CT的分类:方法的比较

Laura M van Poppel^1,2, Lucas de Vries^3,4,5, Mahsa Mojtahedi^3,4

¹Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands. l.m.vanpoppel@amsterdamumc.nl.

European radiology

|December 8, 2025

概括

使用放射学特征的机器学习模型准确地将缺血性中风发病时间分类在4.5小时内或超出4.5小时. 这些先进的模型的性能优于净吸水量 (NWU) 的测量,为更广泛的处理资格提供了潜力.

科学领域:

人工智能在医学中的应用
神经成像和中风分类技术
机器学习用于临床决策支持

背景情况:

准确确定开始到成像 (OTI) 时间对于缺血性中风治疗决策至关重要.
目前的分类方法可能无法充分利用成像数据来准确估计OTI.
机器学习为使用各种患者变量改进OTI时间分类提供了潜力.

研究的目的:

将机器学习模型用于将缺血性中风患者分类为OTI时间窗口 (在4.5小时内或之后) 的比较.
用临床,放射和CT衍生的成像变量组合来评估模型的性能.
为了确定放射学特征是否提高OTI时间分类准确性,与其他指标比如净水吸收 (NWU) 相比.

主要方法:

从MR CLEAN Registry和LATE试验中分析了993名患有已知OTI时间的缺血性中风患者.
机器学习模型的开发,使用临床放射学数据和CT衍生变量 (NWU,损伤体积,放射学) 的各种组合.
使用接收器操作特征曲线 (AUC) 下面的面积进行性能评估.

主要成果:

基于放射学的机器学习模型实现了最高的分类精度,AUC为0.81.
纳入NWU和病变体积或临床评分的模型显示中等性能 (AUC 0.70-0.75).
仅使用临床分数或单独使用NWU的模型的性能较低 (AUC为0.65).

结论:

基于放射学的模型在分类中风OTI时间方面明显优于基于NWU的模型.
通过放射学捕获的缺血性损伤纹理,形状和强度模式,提供了关于损伤年龄的更丰富信息.
基于CT的自动化放射学模型为中风OTI分类提供了一个有希望的,非MRI依赖的方法,可能扩大治疗机会.

关键词:

脑子脑子脑子脑子机器学习机器学习无线电学 (Radiomics) 是一种辐射学.一次性中风,中风.断层扫描 (X射线计算) 的使用

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