Graph data modelling for outcome prediction in oropharyngeal cancer patients

TL;DR

This paper introduces a novel patient hypergraph network (PHGN) for predicting outcomes in oropharyngeal cancer patients using CT radiomic features, extending to time-to-event analysis and outperforming existing models.

Contribution

The study presents the first application of a hypergraph neural network for binary and time-to-event outcome prediction in OPC patients using radiomic data.

Findings

PHGN outperforms GNN and linear models in outcome prediction.

The model effectively captures higher-order patient associations.

Extension to time-to-event analysis improves prognostic accuracy.

Abstract

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.