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Effective adoption, however, depends on computational researchers grasping core histopathology concepts. This article introduces essential terminology and understanding for AI developers, covering major cell types, malignancy and precursor lesions, and the tumour microenvironment. It also presents foundational techniques such as hematoxylin and eosin staining, immunohistochemistry, and multiplexed antibody staining to support faster algorithm development in oncology.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-tumour-histopathology-glossary-for-ai-developers-read-online-free/372089/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-tumour-histopathology-glossary-for-ai-developers-read-online-free/372089.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the article address for AI developers in cancer research?","Question",{"text":112,"@type":113},"It addresses the gap in basic histopathology knowledge needed for effective translation of computational AI methods into practical cancer research workflows and clinical context.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which histopathology concepts are covered to support AI development?",{"text":117,"@type":113},"The article covers defining features of epithelial, stromal, and immune cell types, as well as malignancy, precursor lesions, and the tumour microenvironment (TME).",{"name":119,"@type":110,"acceptedAnswer":120},"What histopathology techniques are introduced in the work?",{"text":121,"@type":113},"It introduces conventional hematoxylin and eosin (HE) staining, antibody staining by immunohistochemistry, and multiplexed antibody staining methods.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},372089,1790786962,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","PLOS COMPUTATIONAL BIOLOGY  \nOPEN ACCESS  \nCitation: Mandal S, Baker A-M, Graham TA, Bra¨utigam K (2025) The tumour histopathology“glossary” for AI developers. PLoS Comput Biol 21(1): e1012708 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pcbi.1012708  \nEditor: B. F. Francis Ouellette, Origin Bioinformatics, CANADA  \nPublished: January 23, 2025  \nCopyright: © 2025 Mandal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nFunding: TAG and AMB are funded by Cancer Research UK (DRCNPG-May21_ 100001) . KB was funded by the Swiss National Science Foundation (P500PM_ 217647 / 1) . SM acknowledges funding from the Institute of Cancer Research’s Data Science Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nCompeting interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: TAG and AMB are named as coinventors on a patent application that describe a method for TCR sequencing (GB2305655 .9), and TAG is named on a method to measure evolutionary dynamics in cancers using  \nEDUCATION  \nThe tumour histopathology “glossary” for AI developers  \nSoham Mandal1,2 *, Ann-Marie Baker1, Trevor A. Graham1, Konstantin Br¨autigam1 *  \n1 Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom, 2 Data Science Team, Institute of Cancer Research, London, United Kingdom  \n* [soham.mandal@icr.ac.uk](soham.mandal@icr.ac.uk) (SM); [konstantin.braeutigam@icr.ac.uk](konstantin.braeutigam@icr.ac.uk) (KB)  \nAbstract  \nThe applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research. We cover the defining features of key cell types, including epithelial, stromal, and immune cells. The concepts of malignancy, precursor lesions, and the tumour microenvironment (TME) are discussed and illustrated. To enhance understanding, we also introduce foundational histopathology techniques, such as conventional staining with hematoxylin and eosin (HE), antibody staining by immunohistochemistry, and including the new multiplexed antibody staining methods. By providing this essential knowledge to the computational community, we aim to accelerate the development of AI algorithms for cancer research.  \nIntroduction  \nHistopathology—the microscopic analysis of tissue samples to diagnose and study diseases—is the mainstay of cancer diagnosis, and most clinical practices revolve around expert human pathologists examining very thin tissue slices (“sections”) mounted on glass slides under traditional light microscopes.  \nThe last decades have experienced a huge rise in the application of bioinformatics, artificial intelligence (AI), and machine learning (ML) in cancer research [ 1–3] . A plethora of AI tools have been developed specifically for histopathology, which aim to improve its diagnostic reliability and accuracy [4–6] . Tissue, i.e., the sum and interplay of cells, and cellular morphologies are the key variables in histopathology [7] and are the foundation for any supervised modelling attempt. Thus, we believe that successful AI development requires an understanding of tumour histology.  \nBuilding accurate AI models for medicine is an interdisciplinary task [8] and requires a complement of different expertise, i.e., computational and mathematical ","cbCaivLSlvxqWlrk","https://ap.wps.com/l/cbCaivLSlvxqWlrk","pdf",4344027,18,"English","# Introduction\n## Histopathology foundations\n## Challenges in interdisciplinary AI development\n## Explainability and clinical trust\n# Abstract\n## AI and deep learning in cancer research\n## Bridging histopathology knowledge for developers\n# Key histopathology concepts\n## Cell types, malignancy, and tumour microenvironment\n# Foundational histopathology techniques\n## H&E staining and immunohistochemistry\n## Multiplexed antibody staining","[{\"question\":\"What problem does the article address for AI developers in cancer research?\",\"answer\":\"It addresses the gap in basic histopathology knowledge needed for effective translation of computational AI methods into practical cancer research workflows and clinical context.\"},{\"question\":\"Which histopathology concepts are covered to support AI development?\",\"answer\":\"The article covers defining features of epithelial, stromal, and immune cell types, as well as malignancy, precursor lesions, and the tumour microenvironment (TME).\"},{\"question\":\"What histopathology techniques are introduced in the work?\",\"answer\":\"It introduces conventional hematoxylin and eosin (HE) staining, antibody staining by immunohistochemistry, and multiplexed antibody staining methods.\"}]","The tumour histopathology “glossary” for AI developers - read online free | PDF",1790193680,45]