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Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breat...

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Autores principales: Polaka, Inese, Bhandari, Manohar Prasad, Mezmale, Linda, Anarkulova, Linda, Veliks, Viktors, Sivins, Armands, Lescinska, Anna Marija, Tolmanis, Ivars, Vilkoite, Ilona, Ivanovs, Igors, Padilla, Marta, Mitrovics, Jan, Shani, Gidi, Haick, Hossam, Leja, Marcis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871298/
https://www.ncbi.nlm.nih.gov/pubmed/35204584
http://dx.doi.org/10.3390/diagnostics12020491
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author Polaka, Inese
Bhandari, Manohar Prasad
Mezmale, Linda
Anarkulova, Linda
Veliks, Viktors
Sivins, Armands
Lescinska, Anna Marija
Tolmanis, Ivars
Vilkoite, Ilona
Ivanovs, Igors
Padilla, Marta
Mitrovics, Jan
Shani, Gidi
Haick, Hossam
Leja, Marcis
author_facet Polaka, Inese
Bhandari, Manohar Prasad
Mezmale, Linda
Anarkulova, Linda
Veliks, Viktors
Sivins, Armands
Lescinska, Anna Marija
Tolmanis, Ivars
Vilkoite, Ilona
Ivanovs, Igors
Padilla, Marta
Mitrovics, Jan
Shani, Gidi
Haick, Hossam
Leja, Marcis
author_sort Polaka, Inese
collection PubMed
description Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.
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spelling pubmed-88712982022-02-25 Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection Polaka, Inese Bhandari, Manohar Prasad Mezmale, Linda Anarkulova, Linda Veliks, Viktors Sivins, Armands Lescinska, Anna Marija Tolmanis, Ivars Vilkoite, Ilona Ivanovs, Igors Padilla, Marta Mitrovics, Jan Shani, Gidi Haick, Hossam Leja, Marcis Diagnostics (Basel) Article Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications. MDPI 2022-02-14 /pmc/articles/PMC8871298/ /pubmed/35204584 http://dx.doi.org/10.3390/diagnostics12020491 Text en © 2022 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
Polaka, Inese
Bhandari, Manohar Prasad
Mezmale, Linda
Anarkulova, Linda
Veliks, Viktors
Sivins, Armands
Lescinska, Anna Marija
Tolmanis, Ivars
Vilkoite, Ilona
Ivanovs, Igors
Padilla, Marta
Mitrovics, Jan
Shani, Gidi
Haick, Hossam
Leja, Marcis
Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title_full Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title_fullStr Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title_full_unstemmed Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title_short Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
title_sort modular point-of-care breath analyzer and shape taxonomy-based machine learning for gastric cancer detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871298/
https://www.ncbi.nlm.nih.gov/pubmed/35204584
http://dx.doi.org/10.3390/diagnostics12020491
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