Lu, Wenqi, Toss, Michael, Dawood, Muhammad, Rakha, Emad, Rajpoot, Nasir and Minhas, Fayyaz (2022) SlideGraph+ : whole slide image level graphs to predict HER2 status in breast cancer. Medical Image Analysis, 80 . 102486. doi:10.1016/j.media.2022.102486 ISSN 1361-8415.

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Abstract

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 transcriptomics or immunohistochemistry (IHC) through in-situ hybridisation (ISH) which incurs additional costs and tissue burden and is prone to analytical variabilities in terms of manual observational biases in scoring. In this study, we propose a novel graph neural network (GNN) based model (SlideGraph) to predict HER2 status directly from whole-slide images of routine Haematoxylin and Eosin (H&E) stained 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 0.80 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 repository is available at https://github.com/wenqi006/SlideGraph along with an IPython notebook showing an end-to-end use case at https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/full-pipelines/slide-graph.ipynb.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RB Pathology R Medicine > RC Internal medicine
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Pathology -- Data processing, Breast -- Cancer -- Research, Epidermal growth factor -- Receptors, Neural networks (Computer science), Graph theory
Journal or Publication Title:	Medical Image Analysis
Publisher:	Elsevier Science BV
ISSN:	1361-8415
Official Date:	August 2022
Dates:	Date Event August 2022 Published 28 May 2022 Available 20 May 2022 Accepted 8 October 2021 Submitted
Volume:	80
Number of Pages:	13
Article Number:	102486
DOI:	10.1016/j.media.2022.102486
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	6 September 2022
Date of first compliant Open Access:	6 September 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 19/SC/0363 Innovate UK http://dx.doi.org/10.13039/501100006041

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