  Cancer prognostication is a challenging task in computational pathology that
requires context-aware representations of histology features to adequately
infer patient survival. Despite the advancements made in weakly-supervised deep
learning, many approaches are not context-aware and are unable to model
important morphological feature interactions between cell identities and tissue
types that are prognostic for patient survival. In this work, we present
Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional
network that hierarchically aggregates instance-level histology features to
model local- and global-level topological structures in the tumor
microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five
different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate
that Patch-GCN outperforms all prior weakly-supervised approaches by
3.58-9.46%. Our code and corresponding models are publicly available at
https://github.com/mahmoodlab/Patch-GCN.
