Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of proteins in tissue. However, many immunohistochemical results are complex because the protein of interest occurs in multiple locations (in different cells and also extracellularly). We have recently established an artificial intelligence framework, PathoFusion which utilises a bifocal convolutional neural network (BCNN) model for detecting and counting arbitrarily definable morphological structures. We have now complemented this model by adding an attention-based graph neural network (abGCN) for the advanced analysis and automated interpretation of such data. Classical convolutional neural network (CNN) models suffer from limitations when handling global information. In contrast, our abGCN is capable of creating a graph representation of cellular detail from entire WSIs. This abGCN method combines attention learning with visualisation techniques that pinpoint the location of informative cells and highlight cell–cell interactions. We have analysed cellular labelling for CD276, a protein of great interest in cancer immunology and a potential marker of malignant glioma cells/putative glioma stem cells (GSCs). We are especially interested in the relationship between CD276 expression and prognosis. The graphs permit predicting individual patient survival on the basis of GSC community features. Our experiments lay a foundation for the use of the BCNN-abGCN tool chain in automated diagnostic prognostication using immunohistochemically labelled histological slides, but the method is essentially generic and potentially a widely usable tool in medical research and AI based healthcare applications.
已开发出计算机化方法，可对全切片图像（如免疫组织化学染色切片）进行定量形态学分析。免疫组化技术因其能提供组织中蛋白质分布的高分辨率数据而备受青睐。然而，许多免疫组化结果具有复杂性，因为目标蛋白可能存在于多个位置（不同细胞内及细胞外）。我们近期建立了人工智能框架PathoFusion，该框架采用双焦点卷积神经网络模型，用于检测和计数可自定义的形态结构。现通过引入基于注意力的图神经网络对该模型进行升级，实现了对此类数据的高级分析与自动化解读。传统卷积神经网络模型在处理全局信息时存在局限性，而我们的注意力图神经网络能够从完整全切片图像中构建细胞细节的图表示。该方法将注意力学习与可视化技术相结合，可精确定位信息细胞的位置并突出细胞间相互作用。我们以癌症免疫学重点蛋白CD276（恶性胶质瘤细胞/推定胶质瘤干细胞潜在标志物）的细胞标记为例展开分析，特别关注CD276表达与预后的关联性。通过图神经网络提取的胶质瘤干细胞群落特征，可实现对个体患者生存期的预测。本实验为BCNN-abGCN工具链在免疫组化标记组织切片的自动化诊断预后中的应用奠定基础，该方法具有通用性，有望成为医学研究和人工智能医疗应用中广泛适用的工具。
肿瘤(癌症)患者之家