Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel-sized WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogeneous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers and validated it by predicting the prognosis of 3,950 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks.

用于分析全切片图像（WSI）的计算病理学方法通常不考虑肿瘤微环境的组织病理学特征。在这里，我们展示了一个图深度神经网络，它以半监督的方式考虑十亿像素大小的 WSI 中的此类上下文特征，可以提供可解释的预后生物标志物。我们设计了一个神经网络模型，利用注意力技术从高度相关的图像块聚合的内存有效表示中学习异质肿瘤微环境的特征。我们使用肾癌、乳腺癌、肺癌和子宫癌的 WSI 来训练该模型，并通过预测 3,950 名患有这四种不同类型癌症的患者的预后来验证该模型。我们还表明，该模型提供了透明细胞肾细胞癌的可解释的背景特征，允许对 1,333 名患者进行基于风险的回顾性分层。从 WSI 中导出上下文组织病理学特征的深度图神经网络可能有助于诊断和预后任务。