Neural Graph Modelling of Whole Slide Images for Survival RankingDownload PDF

Published: 24 Nov 2022, Last Modified: 05 May 2023LoG 2022 PosterReaders: Everyone
Keywords: Survival Analysis, Survival Ranking, Graph Neural Networks, Breast Cancer, Graph Representation, Whole Slide Image
TL;DR: An analysis of a graph neural network model's ability to rank the survival of breast cancer patients from a graph representations of whole slide images.
Abstract: Evaluation of a cancer patient's prognostic outlook is an essential step in the clinical decision-making process, involving the assessment of complex tissue structures in multi-gigapixel whole slide images (WSIs). Effective risk stratification of patients from WSIs has proven challenging despite several approaches across the literature due to their large size and inability of existing approaches to effectively model inter-relationships between different tissue components. We propose a graph neural network (GNN) model that performs pairwise ranking of graph representations of WSIs based on survival scores. The proposed approach translates spatially-localised deep features along with their spatial context to a graph neural network to produce survival scores. Analysis over breast cancer patients from The Cancer Genome Atlas (TCGA) shows that the proposed GNN approach is able to rank patients with respect to their disease-specific survival times with a concordance index of $0.672 \pm 0.058$. This is a significant improvement over existing state of the art and paves the way for neural graph modelling of WSI data for survival prediction for other cancer types.
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