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图表神经网络在抗体介导排斥的病学诊断中的作用

Israel Mateos-Aparicio-Ruiz¹, Anibal Pedraza¹, Nicola Altini²

¹VISILAB Group, Universidad de Castilla-La Mancha, Ciudad Real, 13071, Ciudad Real, Spain.

Computational and structural biotechnology journal

|November 26, 2025

概括

基于图形的深度学习模型显著改善了移植中的抗体中介排斥诊断. 这些先进的模型提高了准确性和可重复性,为病理学家提供了可扩展的替代传统方法.

科学领域:

计算病理学计算病理学
医学中的人工智能
移植免疫学移植免疫学

背景情况:

抗体介导排斥 (AMR) 是移植失败的主要原因.
对AMR进行准确的组织病理学评估对于诊断和患者管理至关重要.
当前的诊断方法在准确性和可重复性方面可能面临挑战.

研究的目的:

评估基于图形的深度学习模型,用于对移植中的AMR进行分类.
用整个幻灯片图像 (WSIs) 提高诊断准确性和可重现性.
将图形神经网络 (GNN) 的性能与传统的补丁智能分类器进行比较.

主要方法:

利用了来自348名脏移植患者的1193名PAS染色WSI的多机构数据集.
细分了关键的 compartments (球粒,动脉,皮质) 并将其表示为图形结构.
采用了各种GNN架构 (图形转换器,SimpleGCN,DenseGCN,SimpleGAT) 和特征提取方法.

主要成果:

GNN模型显著优于补丁智能的基线分类器.
一个仅含质细胞的GNN实现了71.00%的WSI水平准确度,比最佳基线提高了5.34%.
结合多个部分 (动脉,皮质) 提高了WSI水平的准确性,达到86.97%和活检水平达到89.53%.

结论:

基于图形的深度学习比AMR诊断的传统方法提供了实质性的进步.
这些模型提供了可扩展,可重复和具有成本效益的工作流程,减少了专家的投入.
网显示出有很大的潜力,可以帮助病理学家实现更一致和可靠的诊断.

关键词:

通过抗体介导的排斥.深度学习是一种深度学习.数字病理学数字病理学图形神经网络是一个神经网络.腎病理學腎病理學整个幻灯片成像成像系统.

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