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Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology
Deep myxoid soft tissue lesions have posed a diagnostic challenge for pathologists due to significant histological overlap and regional heterogeneity, especially when dealing with small biopsies which have profoundly low accuracy. However, accurate diagnosis is important owing to difference in biolo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051062/ https://www.ncbi.nlm.nih.gov/pubmed/35484289 http://dx.doi.org/10.1038/s41598-022-11009-x |
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author | Yeung, Maximus C. F. Cheng, Ivy S. Y. |
author_facet | Yeung, Maximus C. F. Cheng, Ivy S. Y. |
author_sort | Yeung, Maximus C. F. |
collection | PubMed |
description | Deep myxoid soft tissue lesions have posed a diagnostic challenge for pathologists due to significant histological overlap and regional heterogeneity, especially when dealing with small biopsies which have profoundly low accuracy. However, accurate diagnosis is important owing to difference in biological behaviors and response to adjuvant therapy, that will guide the extent of surgery and the need for neo-adjuvant therapy. Herein, we trained two convolutional neural network models based on a total of 149,130 images representing diagnoses of extra skeletal myxoid chondrosarcoma, intramuscular myxoma, low-grade fibromyxoid sarcoma, myxofibrosarcoma and myxoid liposarcoma. Both AI models outperformed all the pathologists, with a significant improvement of accuracy up to 97% compared to average pathologists of 69.7% (p < 0.00001), corresponding to 90% reduction in error rate. The area under curve of the best AI model was on average 0.9976. It could assist pathologists in clinical practice for accurate diagnosis of deep soft tissue myxoid lesions, and guide clinicians for precise and optimal treatment for patients. |
format | Online Article Text |
id | pubmed-9051062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90510622022-04-30 Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology Yeung, Maximus C. F. Cheng, Ivy S. Y. Sci Rep Article Deep myxoid soft tissue lesions have posed a diagnostic challenge for pathologists due to significant histological overlap and regional heterogeneity, especially when dealing with small biopsies which have profoundly low accuracy. However, accurate diagnosis is important owing to difference in biological behaviors and response to adjuvant therapy, that will guide the extent of surgery and the need for neo-adjuvant therapy. Herein, we trained two convolutional neural network models based on a total of 149,130 images representing diagnoses of extra skeletal myxoid chondrosarcoma, intramuscular myxoma, low-grade fibromyxoid sarcoma, myxofibrosarcoma and myxoid liposarcoma. Both AI models outperformed all the pathologists, with a significant improvement of accuracy up to 97% compared to average pathologists of 69.7% (p < 0.00001), corresponding to 90% reduction in error rate. The area under curve of the best AI model was on average 0.9976. It could assist pathologists in clinical practice for accurate diagnosis of deep soft tissue myxoid lesions, and guide clinicians for precise and optimal treatment for patients. Nature Publishing Group UK 2022-04-28 /pmc/articles/PMC9051062/ /pubmed/35484289 http://dx.doi.org/10.1038/s41598-022-11009-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Yeung, Maximus C. F. Cheng, Ivy S. Y. Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title | Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title_full | Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title_fullStr | Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title_full_unstemmed | Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title_short | Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
title_sort | artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051062/ https://www.ncbi.nlm.nih.gov/pubmed/35484289 http://dx.doi.org/10.1038/s41598-022-11009-x |
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