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Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning
Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858188/ https://www.ncbi.nlm.nih.gov/pubmed/36673073 http://dx.doi.org/10.3390/diagnostics13020263 |
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author | Che, Yuxuan Ren, Fei Zhang, Xueyuan Cui, Li Wu, Huanwen Zhao, Ze |
author_facet | Che, Yuxuan Ren, Fei Zhang, Xueyuan Cui, Li Wu, Huanwen Zhao, Ze |
author_sort | Che, Yuxuan |
collection | PubMed |
description | Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation criteria mainly includes three indexes: staining intensity, circumferential membrane staining pattern, and proportion of positive cells. Manually scoring HER2 IHC images is an error-prone, variable, and time-consuming work. To solve these problems, this study proposes an automated predictive method for scoring whole-slide images (WSI) of HER2 slides based on a deep learning network. A total of 95 HER2 pathological slides from September 2021 to December 2021 were included. The average patch level precision and f1 score were 95.77% and 83.09%, respectively. The overall accuracy of automated scoring for slide-level classification was 97.9%. The proposed method showed excellent specificity for all IHC 0 and 3+ slides and most 1+ and 2+ slides. The evaluation effect of the integrated method is better than the effect of using the staining result only. |
format | Online Article Text |
id | pubmed-9858188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98581882023-01-21 Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning Che, Yuxuan Ren, Fei Zhang, Xueyuan Cui, Li Wu, Huanwen Zhao, Ze Diagnostics (Basel) Article Breast cancer is one of the common malignant tumors in women. It seriously endangers women’s life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation criteria mainly includes three indexes: staining intensity, circumferential membrane staining pattern, and proportion of positive cells. Manually scoring HER2 IHC images is an error-prone, variable, and time-consuming work. To solve these problems, this study proposes an automated predictive method for scoring whole-slide images (WSI) of HER2 slides based on a deep learning network. A total of 95 HER2 pathological slides from September 2021 to December 2021 were included. The average patch level precision and f1 score were 95.77% and 83.09%, respectively. The overall accuracy of automated scoring for slide-level classification was 97.9%. The proposed method showed excellent specificity for all IHC 0 and 3+ slides and most 1+ and 2+ slides. The evaluation effect of the integrated method is better than the effect of using the staining result only. MDPI 2023-01-10 /pmc/articles/PMC9858188/ /pubmed/36673073 http://dx.doi.org/10.3390/diagnostics13020263 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Che, Yuxuan Ren, Fei Zhang, Xueyuan Cui, Li Wu, Huanwen Zhao, Ze Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title | Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title_full | Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title_fullStr | Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title_full_unstemmed | Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title_short | Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning |
title_sort | immunohistochemical her2 recognition and analysis of breast cancer based on deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858188/ https://www.ncbi.nlm.nih.gov/pubmed/36673073 http://dx.doi.org/10.3390/diagnostics13020263 |
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