Graph data modelling for outcome prediction in oropharyngeal cancer
  patients
Nithya Bhasker Stefan Leger Alexander Zwanenburg Chethan Babu Reddy Sebastian Bodenstedt Steffen Löck Stefanie Speidel
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.