Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Темердашев, А. З.; Gashimova, Elina; Порханов, В. А.; Polyakov, I. S.; Perunov, Dmitry; Dmitrieva, Ekaterina

doi:10.3390/metabo13020203

Cited by 9 publications

(4 citation statements)

References 42 publications

(62 reference statements)

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“…To address whether VOCs could distinguish lung cancer specifically, one study analyzed the VOCs in the breath using GC–MS of those with lung cancer (n = 85) and other types of cancer: esophageal (n = 11), breast (n = 22), colorectal (n = 16), kidney (n = 14), stomach (n = 7), prostate (n = 6), cervix (n = 5), and skin (n = 4) (Gashimova et al, 2023 ). The VOCs hexane, acetonitrile, 1-methyltiopropene, 1-methylthiopropane, and dimethyl sulfide were associated with lung cancer vs other cancer types, which have been associated with lung cancer previously (Rudnicka et al, 2014 ; Sakumura et al, 2017 ; Temerdashev et al, 2023 ). Breath VOCs were able to distinguish lung cancer and patients with other cancers with moderate sensitivity (68%) and specificity (69%).…”

Section: Respiratory Diseasementioning

confidence: 86%

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Chou,

Godbeer,

Allsworth

et al. 2024

Metabolomics

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Background The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review. Aim of review This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research. Key scientific concepts of review VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

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Section: Respiratory Diseasementioning

confidence: 86%

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Chou,

Godbeer,

Allsworth

et al. 2024

Metabolomics

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“…This finding holds particular significance, as elderly participants often present with a higher prevalence of comorbidities compared to their younger counterparts. These comorbidities may have introduced complexity into the eNose breathprint profiles [26]. It is noteworthy that elderly patients constitute an emerging demographic among lung cancer patients, and early lung cancer detection could enhance the feasibility of surgical interventions and further improvement of performance of eNose may be warranted [27].…”

Section: Discussionmentioning

confidence: 99%

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

Lee,

Kao,

Hsieh

et al. 2024

Respir Res

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Background Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. Methods Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. Results In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). Conclusion Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. Clinical trial registration This study is not a clinical trial and was therefore not registered.

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“…Fu et al [ 30 ] found that the concentration of 2-butanone was significantly lower in patients with stage I LC than in patients with stage II to IV LC. Conversely, Azamat et al [ 14 ] postulated an increased concentration of 2-butanone in all advanced LC stages. Azamat also found that peaks such as 2,3-Butandione and 1-methylthiopropene were significantly correlated with TNM staging of LC.…”

Section: Discussionmentioning

confidence: 99%

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

Fan,

Zhong,

Liang

et al. 2024

BMC Cancer

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Background Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined. Methods Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies’ titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity. Results This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research. Conclusion This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

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Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Cited by 9 publications

References 42 publications

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

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