Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readily available as a graph, most existing methods either manually define a patient graph, or learn a latent graph based on pairwise similarities between the patients. There are also hypergraph neural network (HGNN)-based methods that were introduced recently to exploit potential higher order associations between the patients by representing them as a hypergraph. In this work, we propose a patient hypergraph network (PHGN), which has been investigated in an inductive learning setup for binary outcome prediction in oropharyngeal cancer (OPC) patients using computed tomography (CT)-based radiomic features for the first time. Additionally, the proposed model was extended to perform time-to-event analyses, and compared with GNN and baseline linear models.

翻译：图神经网络（GNNs）在医学领域的疾病分类和预后预测任务中日益流行。由于患者数据通常无法直接以图结构形式获取，现有方法要么手动定义患者图结构，要么基于患者间的成对相似性学习潜在图结构。近年来还出现了基于超图神经网络（HGNN）的方法，通过将患者表示为超图来挖掘患者间潜在的高阶关联。本研究提出了一种患者超图网络（PHGN），首次在归纳学习框架下利用基于计算机断层扫描（CT）的影像组学特征对口咽癌（OPC）患者进行二分类预后预测。此外，我们将该模型扩展用于时间-事件分析，并与GNN及基线线性模型进行了比较。