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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...

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Autores principales: Fell, Christina, Mohammadi, Mahnaz, Morrison, David, Arandjelović, Ognjen, Syed, Sheeba, Konanahalli, Prakash, Bell, Sarah, Bryson, Gareth, Harrison, David J., Harris-Birtill, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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