Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities
Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic c...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/13/2/203 |
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| author | Azamat Z. Temerdashev Elina M. Gashimova Vladimir A. Porkhanov Igor S. Polyakov Dmitry V. Perunov Ekaterina V. Dmitrieva |
| author_facet | Azamat Z. Temerdashev Elina M. Gashimova Vladimir A. Porkhanov Igor S. Polyakov Dmitry V. Perunov Ekaterina V. Dmitrieva |
| author_sort | Azamat Z. Temerdashev |
| collection | DOAJ |
| description | Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82–88% sensitivity and 80–86% specificity on the test data. |
| first_indexed | 2024-03-11T08:26:27Z |
| format | Article |
| id | doaj.art-9a51ebeaed9a4aaea9bd36c337764e7e |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-11T08:26:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-9a51ebeaed9a4aaea9bd36c337764e7e2023-11-16T22:04:20ZengMDPI AGMetabolites2218-19892023-01-0113220310.3390/metabo13020203Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and ComorbiditiesAzamat Z. Temerdashev0Elina M. Gashimova1Vladimir A. Porkhanov2Igor S. Polyakov3Dmitry V. Perunov4Ekaterina V. Dmitrieva5Department of Analytical Chemistry, Kuban State University, Stavropol’skaya St. 149, Krasnodar 350040, RussiaDepartment of Analytical Chemistry, Kuban State University, Stavropol’skaya St. 149, Krasnodar 350040, RussiaResearch Institute–Regional Clinical Hospital N° 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar 350086, RussiaResearch Institute–Regional Clinical Hospital N° 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar 350086, RussiaResearch Institute–Regional Clinical Hospital N° 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar 350086, RussiaDepartment of Analytical Chemistry, Kuban State University, Stavropol’skaya St. 149, Krasnodar 350040, RussiaNon-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82–88% sensitivity and 80–86% specificity on the test data.https://www.mdpi.com/2218-1989/13/2/203volatile organic compoundsexhaled breathlung cancerthermal desorptionGC-MScomorbidities |
| spellingShingle | Azamat Z. Temerdashev Elina M. Gashimova Vladimir A. Porkhanov Igor S. Polyakov Dmitry V. Perunov Ekaterina V. Dmitrieva Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities Metabolites volatile organic compounds exhaled breath lung cancer thermal desorption GC-MS comorbidities |
| title | Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities |
| title_full | Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities |
| title_fullStr | Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities |
| title_full_unstemmed | Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities |
| title_short | Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities |
| title_sort | non invasive lung cancer diagnostics through metabolites in exhaled breath influence of the disease variability and comorbidities |
| topic | volatile organic compounds exhaled breath lung cancer thermal desorption GC-MS comorbidities |
| url | https://www.mdpi.com/2218-1989/13/2/203 |
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