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Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring...

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Autores principales: Pato, Matilde, Eleutério, Ricardo, Conceição, Raquel C., Godinho, Daniela M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920014/
https://www.ncbi.nlm.nih.gov/pubmed/36772536
http://dx.doi.org/10.3390/s23031496
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author Pato, Matilde
Eleutério, Ricardo
Conceição, Raquel C.
Godinho, Daniela M.
author_facet Pato, Matilde
Eleutério, Ricardo
Conceição, Raquel C.
Godinho, Daniela M.
author_sort Pato, Matilde
collection PubMed
description Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to [Formula: see text] dB, a Signal-to-Mean Ratio of up to [Formula: see text] dB and a Location Error of [Formula: see text] mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.
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spelling pubmed-99200142023-02-12 Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging Pato, Matilde Eleutério, Ricardo Conceição, Raquel C. Godinho, Daniela M. Sensors (Basel) Article Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to [Formula: see text] dB, a Signal-to-Mean Ratio of up to [Formula: see text] dB and a Location Error of [Formula: see text] mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI. MDPI 2023-01-29 /pmc/articles/PMC9920014/ /pubmed/36772536 http://dx.doi.org/10.3390/s23031496 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
Pato, Matilde
Eleutério, Ricardo
Conceição, Raquel C.
Godinho, Daniela M.
Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title_full Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title_fullStr Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title_full_unstemmed Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title_short Evaluating the Performance of Algorithms in Axillary Microwave Imaging towards Improved Breast Cancer Staging
title_sort evaluating the performance of algorithms in axillary microwave imaging towards improved breast cancer staging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920014/
https://www.ncbi.nlm.nih.gov/pubmed/36772536
http://dx.doi.org/10.3390/s23031496
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