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Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine

A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced c...

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Autores principales: Giró Benet, Judit, Seo, Minjun, Khine, Michelle, Gumà Padró, Josep, Pardo Martnez, Antonio, Kurdahi, Fadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435419/
https://www.ncbi.nlm.nih.gov/pubmed/36050339
http://dx.doi.org/10.1038/s41598-022-17795-8
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author Giró Benet, Judit
Seo, Minjun
Khine, Michelle
Gumà Padró, Josep
Pardo Martnez, Antonio
Kurdahi, Fadi
author_facet Giró Benet, Judit
Seo, Minjun
Khine, Michelle
Gumà Padró, Josep
Pardo Martnez, Antonio
Kurdahi, Fadi
author_sort Giró Benet, Judit
collection PubMed
description A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog’s olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC–MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.
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spelling pubmed-94354192022-09-01 Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine Giró Benet, Judit Seo, Minjun Khine, Michelle Gumà Padró, Josep Pardo Martnez, Antonio Kurdahi, Fadi Sci Rep Article A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog’s olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC–MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%. Nature Publishing Group UK 2022-09-01 /pmc/articles/PMC9435419/ /pubmed/36050339 http://dx.doi.org/10.1038/s41598-022-17795-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) .
spellingShingle Article
Giró Benet, Judit
Seo, Minjun
Khine, Michelle
Gumà Padró, Josep
Pardo Martnez, Antonio
Kurdahi, Fadi
Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title_full Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title_fullStr Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title_full_unstemmed Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title_short Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine
title_sort breast cancer detection by analyzing the volatile organic compound (voc) signature in human urine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9435419/
https://www.ncbi.nlm.nih.gov/pubmed/36050339
http://dx.doi.org/10.1038/s41598-022-17795-8
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