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Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis

More than one million new cases of prostate cancer (PCa) were reported worldwide in 2020, and a significant increase of PCa incidence up to 2040 is estimated. Despite potential treatability in early stages, PCa diagnosis is challenging because of late symptoms’ onset and limits of current screening...

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Autores principales: Capelli, Laura, Bax, Carmen, Grizzi, Fabio, Taverna, Gianluigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536694/
https://www.ncbi.nlm.nih.gov/pubmed/34686703
http://dx.doi.org/10.1038/s41598-021-00033-y
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author Capelli, Laura
Bax, Carmen
Grizzi, Fabio
Taverna, Gianluigi
author_facet Capelli, Laura
Bax, Carmen
Grizzi, Fabio
Taverna, Gianluigi
author_sort Capelli, Laura
collection PubMed
description More than one million new cases of prostate cancer (PCa) were reported worldwide in 2020, and a significant increase of PCa incidence up to 2040 is estimated. Despite potential treatability in early stages, PCa diagnosis is challenging because of late symptoms’ onset and limits of current screening procedures. It has been now accepted that cell transformation leads to release of volatile organic compounds in biologic fluids, including urine. Thus, several studies proposed the possibility to develop new diagnostic tools based on urine analysis. Among these, electronic noses (eNoses) represent one of the most promising devices, because of their potential to provide a non-invasive diagnosis. Here we describe the approach aimed at defining the experimental protocol for eNose application for PCa diagnosis. Our research investigates effects of sample preparation and analysis on eNose responses and repeatability. The dependence of eNose diagnostic performance on urine portion analysed, techniques involved for extracting urine volatiles and conditioning temperature were analysed. 192 subjects (132 PCa patients and 60 controls) were involved. The developed experimental protocol has resulted in accuracy, sensitivity and specificity of 83% (CI(95%) 77–89), 82% (CI(95%) 73–88) and 87% (CI(95%) 75–94), respectively. Our findings define eNoses as valuable diagnostic tool allowing rapid and non-invasive PCa diagnosis.
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spelling pubmed-85366942021-10-25 Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis Capelli, Laura Bax, Carmen Grizzi, Fabio Taverna, Gianluigi Sci Rep Article More than one million new cases of prostate cancer (PCa) were reported worldwide in 2020, and a significant increase of PCa incidence up to 2040 is estimated. Despite potential treatability in early stages, PCa diagnosis is challenging because of late symptoms’ onset and limits of current screening procedures. It has been now accepted that cell transformation leads to release of volatile organic compounds in biologic fluids, including urine. Thus, several studies proposed the possibility to develop new diagnostic tools based on urine analysis. Among these, electronic noses (eNoses) represent one of the most promising devices, because of their potential to provide a non-invasive diagnosis. Here we describe the approach aimed at defining the experimental protocol for eNose application for PCa diagnosis. Our research investigates effects of sample preparation and analysis on eNose responses and repeatability. The dependence of eNose diagnostic performance on urine portion analysed, techniques involved for extracting urine volatiles and conditioning temperature were analysed. 192 subjects (132 PCa patients and 60 controls) were involved. The developed experimental protocol has resulted in accuracy, sensitivity and specificity of 83% (CI(95%) 77–89), 82% (CI(95%) 73–88) and 87% (CI(95%) 75–94), respectively. Our findings define eNoses as valuable diagnostic tool allowing rapid and non-invasive PCa diagnosis. Nature Publishing Group UK 2021-10-22 /pmc/articles/PMC8536694/ /pubmed/34686703 http://dx.doi.org/10.1038/s41598-021-00033-y Text en © The Author(s) 2021 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/) .
spellingShingle Article
Capelli, Laura
Bax, Carmen
Grizzi, Fabio
Taverna, Gianluigi
Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title_full Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title_fullStr Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title_full_unstemmed Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title_short Optimization of training and measurement protocol for eNose analysis of urine headspace aimed at prostate cancer diagnosis
title_sort optimization of training and measurement protocol for enose analysis of urine headspace aimed at prostate cancer diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536694/
https://www.ncbi.nlm.nih.gov/pubmed/34686703
http://dx.doi.org/10.1038/s41598-021-00033-y
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