Graph data modelling for outcome prediction in oropharyngeal cancer patients

• URL: http://arxiv.org/abs/2310.02931v1
• Date: Wed, 4 Oct 2023 16:09:35 GMT
• Title: Graph data modelling for outcome prediction in oropharyngeal cancer patients
• Authors: Nithya Bhasker, Stefan Leger, Alexander Zwanenburg, Chethan Babu Reddy, Sebastian Bodenstedt, Steffen L\"ock, Stefanie Speidel
• Abstract summary: Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. 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.
• Score: 38.37247384790338
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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