The tumour histopathology "glossary" for AI developers.
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Embargo End Date
ICR Authors
Authors
Mandal, S
Baker, A-M
Graham, TA
Bräutigam, K
Baker, A-M
Graham, TA
Bräutigam, K
Document Type
Journal Article
Date
2025-01-01
Date Accepted
2025-01-01
Abstract
The 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.
Citation
PLoS Computational Biology, 2025, 21 (1), pp. e1012708 -
Source Title
PLoS Computational Biology
Publisher
PUBLIC LIBRARY SCIENCE
ISSN
1553-734X
eISSN
1553-7358
Collections
Research Team
Genomics & evolut dynam