使用基于注意力的多重实例学习量化和分子特征化
Francesco Cisternino1, Yipei Song2,3, Tim S Peters4
1Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy.
medRxiv : the preprint server for health sciences
|March 17, 2025
概括
动脉硬性斑块内血块 (IPH) 检测是使用机器学习自动化,改善心血管事件预测. 这种数字病理学方法准确量化IPH,揭示斑块不稳定性和主要心血管不良事件的分子驱动因素.
科学领域:
- 心血管病理学心血管病理学
- 数字病理学数字病理学
- 机器学习在医学中的应用
背景情况:
- 内血块出血 (IPH) 是动脉样硬化斑块脆弱性和心血管不良事件的关键指标.
- 在组织学图像中手动量化IPH是主观的,容易发生观察者之间的变化.
- 准确的IPH评估对于理解斑块不稳定性和预测患者的结果至关重要.
研究的目的:
- 开发和验证基于机器学习的自动化框架,用于在动脉样硬化斑块中检测和量化IPH.
- 为了比较IPH量化不同组织学染料的性能.
- 将数字病理与分子数据相结合,以描述IPH及其与临床结果的关联.
主要方法:
- 一个基于注意力的附加多重实例学习 (MIL) 框架是使用来自Athero-Express生物库 (2595名患者) 的全幻灯片图像开发的.
- 九种不同的组织学染色,包括血素和乙 (H&E),被评估用于IPH检测.
- 研究了组合模型,将H&E与CD68或Verhoeff-Van Gieson (EVG) 弹性纤维染色相结合.
- IPH区域来自MIL衍生的注意力得分,并使用单细胞转录学分析分子途径.
主要成果:
- 开发的MIL框架准确地检测和量化IPH,优于手动评分.
- 血素和欧 (H&E) 染色显示出高性能 (AUROC = 0.86),通过结合CD68或EVG (AUROC = 0.92) 显著改善.
- IPH的存在和区域被确定为术前症状和主要不良心血管事件 (MACE) 的最强预测因素.
- 确定了与IPH相关的关键分子通路,包括TNF-α信号传递和泡细胞存在.
结论:
- 使用数字病理学和机器学习的自动IPH量化提供了一个可扩展,可复制和可解释的斑块表型化方法.
- 这种方法提高了心血管事件的预测,并为IPH驱动的斑块不稳定性背后的分子机制提供了新的见解.
- 这些发现有助于更深入地了解IPH如何导致症状和MACE,为改善患者管理铺平了道路.
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