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 s...
| Publicado en: | Cancers Vol. 13; no. 15; pp. 3825 - 3826 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151787833&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151787833 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Aug2021 vid: 13 iid: 15 pid: 97109 pub: MDPI artinfo: ui: 151787833 151787833 151787833 10.3390/cancers13153825 151787833 ppf: 3825 ppct: 1 formats: tig: atl: A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer. aug: au: Sarker, Md Mostafa Kamal Makhlouf, Yasmine Craig, Stephanie G. Humphries, Matthew P. Loughrey, Maurice James, Jacqueline A. Salto-Tellez, Manuel O'Reilly, Paul Maxwell, Perry affil: Precision Medicine Centre of Excellence, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK sug: subj: Deep Learning Evaluation Tumor Markers, Biological Metabolism T Lymphocytes Metabolism Proteins Metabolism Gene Expression Colonic Neoplasms Human Image Processing, Computer Assisted Algorithms Artificial Intelligence Workflow Staining and Labeling Immunohistochemistry ab: 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. 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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