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血动力学MRI参数,以预测无症状单侧动脉狭窄,随机森林机器学习

Carina Gleißner¹, Stephan Kaczmarz^1,2,3, Jan Kufer^1,3

¹Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.

Frontiers in neuroimaging

|August 9, 2023

概括

这项研究使用机器学习和MRI来确定内动脉狭窄症 (ICAS) 的关键指标. 专注于单个流域 (iWSAs) 改善了识别严重狭窄症患者的预测准确性.

科学领域:

神经成像是一种神经成像.
血管神经学血管神经学
机器学习在医学中的应用

背景情况:

内动脉狭窄 (ICAS) 是中风和认知衰退的重要危险因素.
血液动力学损伤,特别是在单个流域 (iWSAs),是疾病严重程度的关键指标.
血液动力氧化敏感的MRI为评估这些损伤和识别有风险的患者提供了潜在的方法.

研究的目的:

用随机森林机器学习来确定最敏感的MRI参数和感兴趣的体积 (VOI) 以预测高等级的ICAS.
与标准灰色和白色物质VOI相比,评估结合iWSAs的模型的预测性能.
调查准确的ICAS分类与认知表现之间的相关性.

主要方法:

24名高度ICAS患者和24名对照患者接受了多参数MRI,包括pCASL,fMRI,DSC和定量映射.
从iWSA内部和外部的灰质 (GM) 和白质 (WM) 中提取特征,生成96个参数.
随机森林分类器被训练并使用不同的VOI策略和特征选择进行比较.

主要成果:

在iWSA中,时间到峰值 (TTP),脑血流 (CBF) 和脑血管反应 (CVR) 是最敏感的预测因素.
结合iWSA和特征选择的模型实现了比标准VOI (AUC:0.84) 更高的预测准确度 (AUC:0.90).
根据模型正确分类的患者与错误分类的患者相比,表现较差的认知表现.

结论:

多参数MRI与随机森林分析相结合,有效地识别了用于预测ICAS的关键参数和VOI.
包括iWSA在内显著提高了ICAS预测的准确性.
这种方法有望改善ICAS患者的个性化治疗策略.

关键词:

无症状的动脉狭窄症.血液动力学血液动力学个别的流域水域.机器学习是机器学习.磁共振成像 (MRI) 的使用.随机森林整体分类器

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