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A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.


Yasmine Makhlouf

Stephanie G. Craig

Matthew P. Humphries

Maurice Loughrey

Jacqueline A. James

Manuel Salto-Tellez


Perry Maxwell


Biomarkers identify patient response to therapy. The potential immune?checkpoint bi-omarker, Inducible T?cell COStimulator (ICOS), expressed on regulating T?cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI?based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user?friendly tool that can interact with1 other open?source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell?based segmentation/detection to quantify and analyse the trade?offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.


SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2021. A means of assessing deep learning-based detection of ICOS protein expression in colon cancer. Cancers [online], 13(15): machine learning techniques in cancer, article 3825. Available from:

Journal Article Type Article
Acceptance Date Jul 23, 2021
Online Publication Date Jul 29, 2021
Publication Date Aug 1, 2021
Deposit Date Dec 2, 2021
Publicly Available Date Dec 9, 2021
Journal Cancers
Electronic ISSN 2072-6694
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 13
Issue 15
Article Number 3825
Pages 3825
Keywords Colorectal cancer; Immunohistochemistry; Biomarkers; ICOS; Artificial intelligence; Deep learning
Public URL
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