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Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method
PURPOSE: The expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images. METHODS: The data from 1...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889619/ https://www.ncbi.nlm.nih.gov/pubmed/35251999 http://dx.doi.org/10.3389/fonc.2022.829041 |
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author | Xu, Zilong Yang, Qiwei Li, Minghao Gu, Jiabing Du, Changping Chen, Yang Li, Baosheng |
author_facet | Xu, Zilong Yang, Qiwei Li, Minghao Gu, Jiabing Du, Changping Chen, Yang Li, Baosheng |
author_sort | Xu, Zilong |
collection | PubMed |
description | PURPOSE: The expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images. METHODS: The data from 144 breast cancer patients with preoperative ultrasound images and clinical information were retrospectively collected from the Shandong Province Tumor Hospital. An end-to-end 3-block-DenseNet deep learning classifier was built to predict the expression of human epidermal growth factor receptor 2 by ultrasound images. The patients were randomly divided into a training (n = 108) and a validation set (n = 36). RESULTS: Our proposed deep learning model achieved an encouraging predictive performance in the training set (accuracy = 85.79%, AUC = 0.87) and the validation set (accuracy = 80.56%, AUC = 0.84). The effectiveness of our model significantly exceeded the clinical model and the radiomics model. The score of the proposed model showed significant differences between HER2-positive and -negative expression (p < 0.001). CONCLUSIONS: These results demonstrate that ultrasound images are predictive of HER2 expression through a deep learning classifier. Our method provides a non-invasive, simple, and feasible method for the prediction of HER2 expression without the manual delineation of the regions of interest (ROI). The performance of our deep learning model significantly exceeded the traditional texture analysis based on the radiomics model. |
format | Online Article Text |
id | pubmed-8889619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88896192022-03-03 Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method Xu, Zilong Yang, Qiwei Li, Minghao Gu, Jiabing Du, Changping Chen, Yang Li, Baosheng Front Oncol Oncology PURPOSE: The expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images. METHODS: The data from 144 breast cancer patients with preoperative ultrasound images and clinical information were retrospectively collected from the Shandong Province Tumor Hospital. An end-to-end 3-block-DenseNet deep learning classifier was built to predict the expression of human epidermal growth factor receptor 2 by ultrasound images. The patients were randomly divided into a training (n = 108) and a validation set (n = 36). RESULTS: Our proposed deep learning model achieved an encouraging predictive performance in the training set (accuracy = 85.79%, AUC = 0.87) and the validation set (accuracy = 80.56%, AUC = 0.84). The effectiveness of our model significantly exceeded the clinical model and the radiomics model. The score of the proposed model showed significant differences between HER2-positive and -negative expression (p < 0.001). CONCLUSIONS: These results demonstrate that ultrasound images are predictive of HER2 expression through a deep learning classifier. Our method provides a non-invasive, simple, and feasible method for the prediction of HER2 expression without the manual delineation of the regions of interest (ROI). The performance of our deep learning model significantly exceeded the traditional texture analysis based on the radiomics model. Frontiers Media S.A. 2022-02-16 /pmc/articles/PMC8889619/ /pubmed/35251999 http://dx.doi.org/10.3389/fonc.2022.829041 Text en Copyright © 2022 Xu, Yang, Li, Gu, Du, Chen and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Xu, Zilong Yang, Qiwei Li, Minghao Gu, Jiabing Du, Changping Chen, Yang Li, Baosheng Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title | Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title_full | Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title_fullStr | Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title_full_unstemmed | Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title_short | Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method |
title_sort | predicting her2 status in breast cancer on ultrasound images using deep learning method |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889619/ https://www.ncbi.nlm.nih.gov/pubmed/35251999 http://dx.doi.org/10.3389/fonc.2022.829041 |
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