  Human epidermal growth factor receptor 2 (HER2) is an important prognostic
and predictive factor which is overexpressed in 15-20% of breast cancer (BCa).
The determination of its status is a key clinical decision making step for
selection of treatment regimen and prognostication. HER2 status is evaluated
using transcroptomics or immunohistochemistry (IHC) through situ hybridisation
(ISH) which require additional costs and tissue burden in addition to
analytical variabilities in terms of manual observational biases in scoring. In
this study, we propose a novel graph neural network (GNN) based model (termed
SlideGraph+) to predict HER2 status directly from whole-slide images of routine
Haematoxylin and Eosin (H&E) slides. The network was trained and tested on
slides from The Cancer Genome Atlas (TCGA) in addition to two independent test
datasets. We demonstrate that the proposed model outperforms the
state-of-the-art methods with area under the ROC curve (AUC) values > 0.75 on
TCGA and 0.8 on independent test sets. Our experiments show that the proposed
approach can be utilised for case triaging as well as pre-ordering diagnostic
tests in a diagnostic setting. It can also be used for other weakly supervised
prediction problems in computational pathology. The SlideGraph+ code is
available at https://github.com/wenqi006/SlideGraph.

Computer Vision and Pattern Recognition;Machine Learning