Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air
Novel non-invasive methods for the diagnosis of malignancies should be effective for early diagnosis, reproducible, inexpensive, and independent from the human factor. Our aim was to establish the applicability of the non-invasive method, based on the analysis of air exhaled by patients who are at d...
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MDPI AG
2020-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/10/11/934 |
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| author | Vladimir I. Chernov Evgeniy L. Choynzonov Denis E. Kulbakin Ekaterina N. Menkova Elena V. Obkhodskaya Artem V. Obkhodskiy Aleksandr S. Popov Evgeniy O. Rodionov Victor I. Sachkov Anna S. Sachkova |
| author_facet | Vladimir I. Chernov Evgeniy L. Choynzonov Denis E. Kulbakin Ekaterina N. Menkova Elena V. Obkhodskaya Artem V. Obkhodskiy Aleksandr S. Popov Evgeniy O. Rodionov Victor I. Sachkov Anna S. Sachkova |
| author_sort | Vladimir I. Chernov |
| collection | DOAJ |
| description | Novel non-invasive methods for the diagnosis of malignancies should be effective for early diagnosis, reproducible, inexpensive, and independent from the human factor. Our aim was to establish the applicability of the non-invasive method, based on the analysis of air exhaled by patients who are at different stages of oropharyngeal, larynx and lung cancer. The diagnostic device includes semiconductor sensors capable of measuring the concentrations of gas components in exhaled air, with the high sensitivity of 1 ppm. The neural network uses signals from these sensors to perform classification and identify cancer patients. Prior to the diagnostic procedure of the non-invasive method, we clarified the extent and stage of the tumor according to current international standards and recommendations for the diagnosis of malignancies. The statistical dataset for neural network training and method validation included samples from 121 patients with the most common tumor localizations (lungs, oropharyngeal region and larynx). The largest number of cases (21 patients) were lung cancer, while the number of patients with oropharyngeal or laryngeal cancer varied from 1 to 9, depending on tumor localization (oropharyngeal, tongue, oral cavity, larynx and mucosa of the lower jaw). In the case of lung cancer, the parameters of the diagnostic device are determined as follows: sensitivity—95.24%, specificity—76.19%. For oropharyngeal cancer and laryngeal cancer, these parameters were 67.74% and 87.1%, respectively. This non-invasive method could lead to relevant medicinal findings and provide an opportunity for clinical utility and patient benefit upon early diagnosis of malignancies. |
| first_indexed | 2024-03-10T14:57:19Z |
| format | Article |
| id | doaj.art-de17cc106e254e2695929cd1b6f1b7e7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T14:57:19Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-de17cc106e254e2695929cd1b6f1b7e72023-11-20T20:30:36ZengMDPI AGDiagnostics2075-44182020-11-01101193410.3390/diagnostics10110934Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled AirVladimir I. Chernov0Evgeniy L. Choynzonov1Denis E. Kulbakin2Ekaterina N. Menkova3Elena V. Obkhodskaya4Artem V. Obkhodskiy5Aleksandr S. Popov6Evgeniy O. Rodionov7Victor I. Sachkov8Anna S. Sachkova9Tomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, RussiaTomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, RussiaTomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, RussiaTomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, RussiaLaboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, RussiaLaboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, RussiaLaboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, RussiaTomsk National Research Medical Center of the Russian Academy of Sciences, Cancer Research Institute, 5 Kooperativny Street, 634009 Tomsk, RussiaLaboratory of Chemical Technologies, National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, RussiaSchool of Nuclear Science & Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, RussiaNovel non-invasive methods for the diagnosis of malignancies should be effective for early diagnosis, reproducible, inexpensive, and independent from the human factor. Our aim was to establish the applicability of the non-invasive method, based on the analysis of air exhaled by patients who are at different stages of oropharyngeal, larynx and lung cancer. The diagnostic device includes semiconductor sensors capable of measuring the concentrations of gas components in exhaled air, with the high sensitivity of 1 ppm. The neural network uses signals from these sensors to perform classification and identify cancer patients. Prior to the diagnostic procedure of the non-invasive method, we clarified the extent and stage of the tumor according to current international standards and recommendations for the diagnosis of malignancies. The statistical dataset for neural network training and method validation included samples from 121 patients with the most common tumor localizations (lungs, oropharyngeal region and larynx). The largest number of cases (21 patients) were lung cancer, while the number of patients with oropharyngeal or laryngeal cancer varied from 1 to 9, depending on tumor localization (oropharyngeal, tongue, oral cavity, larynx and mucosa of the lower jaw). In the case of lung cancer, the parameters of the diagnostic device are determined as follows: sensitivity—95.24%, specificity—76.19%. For oropharyngeal cancer and laryngeal cancer, these parameters were 67.74% and 87.1%, respectively. This non-invasive method could lead to relevant medicinal findings and provide an opportunity for clinical utility and patient benefit upon early diagnosis of malignancies.https://www.mdpi.com/2075-4418/10/11/934malignancycancermarkersnon-invasive diagnosisexhaled airsensor-based gas analyzer |
| spellingShingle | Vladimir I. Chernov Evgeniy L. Choynzonov Denis E. Kulbakin Ekaterina N. Menkova Elena V. Obkhodskaya Artem V. Obkhodskiy Aleksandr S. Popov Evgeniy O. Rodionov Victor I. Sachkov Anna S. Sachkova Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air Diagnostics malignancy cancer markers non-invasive diagnosis exhaled air sensor-based gas analyzer |
| title | Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air |
| title_full | Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air |
| title_fullStr | Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air |
| title_full_unstemmed | Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air |
| title_short | Non-Invasive Diagnosis of Malignancies Based on the Analysis of Markers in Exhaled Air |
| title_sort | non invasive diagnosis of malignancies based on the analysis of markers in exhaled air |
| topic | malignancy cancer markers non-invasive diagnosis exhaled air sensor-based gas analyzer |
| url | https://www.mdpi.com/2075-4418/10/11/934 |
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