Graph data modelling for outcome prediction in oropharyngeal cancer patients
This work addresses outcome prediction for oropharyngeal cancer patients, representing an incremental advancement in applying hypergraph neural networks to medical data.
The authors tackled outcome prediction in oropharyngeal cancer patients by proposing a patient hypergraph network (PHGN) for binary outcome prediction and time-to-event analysis using CT-based radiomic features, achieving results compared with GNN and baseline linear models.
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.