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Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence
In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994759/ https://www.ncbi.nlm.nih.gov/pubmed/36888621 http://dx.doi.org/10.1371/journal.pone.0282577 |
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author | Fell, Christina Mohammadi, Mahnaz Morrison, David Arandjelović, Ognjen Syed, Sheeba Konanahalli, Prakash Bell, Sarah Bryson, Gareth Harrison, David J. Harris-Birtill, David |
author_facet | Fell, Christina Mohammadi, Mahnaz Morrison, David Arandjelović, Ognjen Syed, Sheeba Konanahalli, Prakash Bell, Sarah Bryson, Gareth Harrison, David J. Harris-Birtill, David |
author_sort | Fell, Christina |
collection | PubMed |
description | In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with “malignant” and “other or benign” areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being “malignant” or “other or benign”. Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either “malignant”, “other or benign” or “insufficient”. The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists’ workload. |
format | Online Article Text |
id | pubmed-9994759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99947592023-03-09 Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence Fell, Christina Mohammadi, Mahnaz Morrison, David Arandjelović, Ognjen Syed, Sheeba Konanahalli, Prakash Bell, Sarah Bryson, Gareth Harrison, David J. Harris-Birtill, David PLoS One Research Article In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either “malignant”, “other or benign” or “insufficient”. An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with “malignant” and “other or benign” areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being “malignant” or “other or benign”. Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either “malignant”, “other or benign” or “insufficient”. The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists’ workload. Public Library of Science 2023-03-08 /pmc/articles/PMC9994759/ /pubmed/36888621 http://dx.doi.org/10.1371/journal.pone.0282577 Text en © 2023 Fell et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fell, Christina Mohammadi, Mahnaz Morrison, David Arandjelović, Ognjen Syed, Sheeba Konanahalli, Prakash Bell, Sarah Bryson, Gareth Harrison, David J. Harris-Birtill, David Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title | Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title_full | Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title_fullStr | Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title_full_unstemmed | Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title_short | Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
title_sort | detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9994759/ https://www.ncbi.nlm.nih.gov/pubmed/36888621 http://dx.doi.org/10.1371/journal.pone.0282577 |
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