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Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model
The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952258/ https://www.ncbi.nlm.nih.gov/pubmed/32038869 http://dx.doi.org/10.1049/htl.2019.0072 |
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author | Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen |
author_facet | Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen |
author_sort | Chen, Fang |
collection | PubMed |
description | The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives. |
format | Online Article Text |
id | pubmed-6952258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-69522582020-02-07 Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen Healthc Technol Lett Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives. The Institution of Engineering and Technology 2019-11-26 /pmc/articles/PMC6952258/ /pubmed/32038869 http://dx.doi.org/10.1049/htl.2019.0072 Text en http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
spellingShingle | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title | Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title_full | Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title_fullStr | Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title_full_unstemmed | Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title_short | Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
title_sort | probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model |
topic | Special Issue: Papers from the 13th Workshop on Augmented Environments for Computer Assisted Interventions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952258/ https://www.ncbi.nlm.nih.gov/pubmed/32038869 http://dx.doi.org/10.1049/htl.2019.0072 |
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