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Delineation and detection of breast cancer using novel label-free fluorescence
BACKGROUND: Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS: Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for no...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505331/ https://www.ncbi.nlm.nih.gov/pubmed/37716994 http://dx.doi.org/10.1186/s12880-023-01095-2 |
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author | Mahmoud, Alaaeldin El-Sharkawy, Yasser H. |
author_facet | Mahmoud, Alaaeldin El-Sharkawy, Yasser H. |
author_sort | Mahmoud, Alaaeldin |
collection | PubMed |
description | BACKGROUND: Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS: Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440–460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS: Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS: This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01095-2. |
format | Online Article Text |
id | pubmed-10505331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105053312023-09-18 Delineation and detection of breast cancer using novel label-free fluorescence Mahmoud, Alaaeldin El-Sharkawy, Yasser H. BMC Med Imaging Research BACKGROUND: Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS: Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440–460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS: Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS: This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-023-01095-2. BioMed Central 2023-09-16 /pmc/articles/PMC10505331/ /pubmed/37716994 http://dx.doi.org/10.1186/s12880-023-01095-2 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 Mahmoud, Alaaeldin El-Sharkawy, Yasser H. Delineation and detection of breast cancer using novel label-free fluorescence |
title | Delineation and detection of breast cancer using novel label-free fluorescence |
title_full | Delineation and detection of breast cancer using novel label-free fluorescence |
title_fullStr | Delineation and detection of breast cancer using novel label-free fluorescence |
title_full_unstemmed | Delineation and detection of breast cancer using novel label-free fluorescence |
title_short | Delineation and detection of breast cancer using novel label-free fluorescence |
title_sort | delineation and detection of breast cancer using novel label-free fluorescence |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505331/ https://www.ncbi.nlm.nih.gov/pubmed/37716994 http://dx.doi.org/10.1186/s12880-023-01095-2 |
work_keys_str_mv | AT mahmoudalaaeldin delineationanddetectionofbreastcancerusingnovellabelfreefluorescence AT elsharkawyyasserh delineationanddetectionofbreastcancerusingnovellabelfreefluorescence |