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A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections
In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 hour) ca...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050807/ https://www.ncbi.nlm.nih.gov/pubmed/34963688 http://dx.doi.org/10.1038/s41374-021-00718-y |
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author | Falahkheirkhah, Kianoush Guo, Tao Hwang, Michael Tamboli, Pheroze Wood, Christopher G Karam, Jose A Sircar, Kanishka Bhargava, Rohit |
author_facet | Falahkheirkhah, Kianoush Guo, Tao Hwang, Michael Tamboli, Pheroze Wood, Christopher G Karam, Jose A Sircar, Kanishka Bhargava, Rohit |
author_sort | Falahkheirkhah, Kianoush |
collection | PubMed |
description | In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images (“virtual FFPE”) from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort. |
format | Online Article Text |
id | pubmed-9050807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-90508072022-06-28 A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections Falahkheirkhah, Kianoush Guo, Tao Hwang, Michael Tamboli, Pheroze Wood, Christopher G Karam, Jose A Sircar, Kanishka Bhargava, Rohit Lab Invest Article In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images (“virtual FFPE”) from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort. 2022-05 2021-12-28 /pmc/articles/PMC9050807/ /pubmed/34963688 http://dx.doi.org/10.1038/s41374-021-00718-y Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms |
spellingShingle | Article Falahkheirkhah, Kianoush Guo, Tao Hwang, Michael Tamboli, Pheroze Wood, Christopher G Karam, Jose A Sircar, Kanishka Bhargava, Rohit A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title | A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title_full | A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title_fullStr | A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title_full_unstemmed | A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title_short | A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
title_sort | generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050807/ https://www.ncbi.nlm.nih.gov/pubmed/34963688 http://dx.doi.org/10.1038/s41374-021-00718-y |
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