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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...

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Detalles Bibliográficos
Autores principales: Chen, Fang, Liu, Jia, Zhang, Xinran, Liao, Hongen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Institution of Engineering and Technology 2019
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.
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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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