The tumour histopathology "glossary" for AI developers.
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 com...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012708 |
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| author | Soham Mandal Ann-Marie Baker Trevor A Graham Konstantin Bräutigam |
| author_facet | Soham Mandal Ann-Marie Baker Trevor A Graham Konstantin Bräutigam |
| author_sort | Soham Mandal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a17fb61f8c44bf6a5fce52007be6109 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-9a17fb61f8c44bf6a5fce52007be61092025-02-05T05:30:38ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101270810.1371/journal.pcbi.1012708The tumour histopathology "glossary" for AI developers.Soham MandalAnn-Marie BakerTrevor A GrahamKonstantin BräutigamThe 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.https://doi.org/10.1371/journal.pcbi.1012708 |
| spellingShingle | Soham Mandal Ann-Marie Baker Trevor A Graham Konstantin Bräutigam The tumour histopathology "glossary" for AI developers. PLoS Computational Biology |
| title | The tumour histopathology "glossary" for AI developers. |
| title_full | The tumour histopathology "glossary" for AI developers. |
| title_fullStr | The tumour histopathology "glossary" for AI developers. |
| title_full_unstemmed | The tumour histopathology "glossary" for AI developers. |
| title_short | The tumour histopathology "glossary" for AI developers. |
| title_sort | tumour histopathology glossary for ai developers |
| url | https://doi.org/10.1371/journal.pcbi.1012708 |
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