A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer

Cancers

Maurice Loughrey¹  Yasmine Makhlouf²  Manuel Salto-Tellez²  Matthew P. Humphries²  Stephanie G. Craig²  Paul O’Reilly²  Md Mostafa Kamal Sarker²  Jacqueline A. James²  Perry Maxwell² 

[1] Cellular Pathology, Belfast Health and Social Care Trust, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, UK;Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK;

关键词: colorectal cancer;    immunohistochemistry;    biomarkers;    ICOS;    artificial intelligence;    deep learning;   

DOI  :  10.3390/cancers13153825

来源: DOAJ

【 摘 要 】

Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, 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 pathology, 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.

【 授权许可】

Unknown