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在心脏PET/CT成像的生存分析中混合机器学习

Luis Eduardo Juarez-Orozco^1,2,3, Mikael Niemi⁴, Ming Wai Yeung⁵

¹Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. l.e.juarez.orozco@gmail.com.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

|September 1, 2023

概括

一种混合机器学习 (ML) 和对心脏PET/CT数据的生存分析改善了心肌梗塞 (MI) 或死亡的预测. ML-CCTA评分是最强的预测因素,表现优于专家和评分模型.

科学领域:

心血管成像和人工智能
医学数据分析和预测建模.

背景情况:

心脏PET/CT产生了许多变量,而传统的生存分析使用的预后输入较少.
机器学习 (ML) 可以整合复杂的成像数据,为提高预后准确性提供潜力.

研究的目的:

开发和评估一种混合ML生存模型,使用多模式PET/CT数据来预测长期心肌梗塞 (MI) 或死亡.
将混合模型的性能与专家解释和基于分数的模型进行比较.

主要方法:

一种混合方法结合了冠状动脉CT血管学 (CCTA) 和15O-水-PET输液的75个成像变量的ML集成与Cox回归生存分析.
分析了739名中等风险患者的数据,以分析心脏病发作和全因死亡率.
来自ML的CCTA (ML-CCTA) 和PET (ML-PET) 评分与临床变量进行了整合.

主要成果:

混合ML生存模型表现出优异的性能 (CI .81) 与基于专家 (.71) 和基于分数 (.64) 的模型相比.
ML-CCTA得分成为不良心血管结果最重要的预测指标.

结论:

将ML衍生成像得分与传统的生存分析相结合,可以改善心脏PET/CT中长期不良事件的预后建模.
这种混合方法为风险分层的传统专家解释提供了一个强大的替代方案.

关键词:

机器学习机器学习聚乙烯/聚乙烯/聚乙烯心血管事件的发生.混合成像技术混合成像技术生存分析,生存分析.

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