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

Simple Summary In this study, we propose a general artificial intelligence (AI) based workflow for applying deep learning to the problem of cell identification in immunohistochemistry-stained slides as a basis for quantifying nuclear staining biomarkers. Our approach consists of two main parts: a simplified but robust annotation process, and the application of cell identification models. This results in an optimised process with a new user-friendly tool that can interact with 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 identification to quantify and analyse the trade-offs between different deep learning architectures, providing a more accurate and less time-consuming tool than using traditional methods. This approach can identify the best tool to deliver AI tools for clinical utility. Abstract 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.