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HAHNet: a convolutional neural network for HER2 status classification of breast cancer

OBJECTIVE: Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of...

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Autores principales: Wang, Jiahao, Zhu, Xiaodong, Chen, Kai, Hao, Lei, Liu, Yuanning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512620/
https://www.ncbi.nlm.nih.gov/pubmed/37730567
http://dx.doi.org/10.1186/s12859-023-05474-y
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author Wang, Jiahao
Zhu, Xiaodong
Chen, Kai
Hao, Lei
Liu, Yuanning
author_facet Wang, Jiahao
Zhu, Xiaodong
Chen, Kai
Hao, Lei
Liu, Yuanning
author_sort Wang, Jiahao
collection PubMed
description OBJECTIVE: Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists. RESULTS: This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods. CONCLUSIONS: According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.
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spelling pubmed-105126202023-09-22 HAHNet: a convolutional neural network for HER2 status classification of breast cancer Wang, Jiahao Zhu, Xiaodong Chen, Kai Hao, Lei Liu, Yuanning BMC Bioinformatics Research OBJECTIVE: Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists. RESULTS: This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods. CONCLUSIONS: According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients. BioMed Central 2023-09-20 /pmc/articles/PMC10512620/ /pubmed/37730567 http://dx.doi.org/10.1186/s12859-023-05474-y Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Jiahao
Zhu, Xiaodong
Chen, Kai
Hao, Lei
Liu, Yuanning
HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title_full HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title_fullStr HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title_full_unstemmed HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title_short HAHNet: a convolutional neural network for HER2 status classification of breast cancer
title_sort hahnet: a convolutional neural network for her2 status classification of breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512620/
https://www.ncbi.nlm.nih.gov/pubmed/37730567
http://dx.doi.org/10.1186/s12859-023-05474-y
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