A review of exhaled breath: a key role in lung cancer diagnosis

The LOD paradox: When lower isn't always better in biosensor research and development

Biosensor research has long focused on achieving the lowest possible Limits of Detection (LOD), driving significant advances in sensitivity and opening up new possibilities in analysis. However, this intense focus on low LODs may not always meet the practical needs or suit the actual uses of these devices. While technological improvements are impressive, they can sometimes overlook important factors such as detection range, ease of use, and market readiness, which are vital for biosensors to be effective in real-world applications. This review advocates for a balanced approach to biosensor development, emphasizing the need to align technological advancements with practical utility. We delve into various applications, including the detection of cancer biomarkers, pathology-related biomarkers, and illicit drugs, illustrating the critical role of LOD within these contexts. By considering clinical needs and broader design aspects like cost-effectiveness, sustainability, and regulatory compliance, we argue that integrating technical progress with practicality will enhance the impact of biosensors. Such an approach ensures that biosensors are not only technically sound but also widely useable and beneficial in real-world applications. Addressing the diverse analytical parameters alongside user expectations and market demands will likely maximize the real-world impact of biosensors.

Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS)

The analysis of volatile organic compounds (VOCs) in exhaled air has attracted the interest of the scientific community because it provides the possibility of monitoring physiological and metabolic processes and non-invasive diagnostics of various diseases. However, this method remains underused in clinical practice as well as in research because of the lack of standardized procedures for the collection, storage and transport of breath samples, which would guarantee good reproducibility and comparability of results. The method of sampling, as well as the storage time of the breath samples in the polymer bags used for sample storage and transport, affect the composition and concentration of VOCs present in the breath samples. The aim of our study was to compare breath samples obtained using two methods with fully disposable equipment: a Haldane sampling tube intended for direct breath collection and breath samples exhaled into a transparent Tedlar bag. The second task was to monitor the stability of selected compounds of real breath samples stored in a Tedlar bag for 6 h. Gas chromatography coupled with ion mobility spectrometry (GC-IMS) implemented in the BreathSpec®device was used to analyse exhaled breath. Our results showed a significant difference in the signal intensity of some volatiles when taking a breath sample with a Haldane tube and a Tedlar bag. Due to its endogenous origin, acetone levels were significantly higher when the Haldane tube sampler was used while elevated levels of 2-propanol and unidentified VOC (designated as VOC 3) in the Tedlar bag samples likely originated from contamination of the Tedlar bags. The VOC stability study revealed compound-specific signal intensity changes of the selected VOCs with storage time in the Tedlar bags, with some volatiles showing increasing signal intensity during storage in Tedlar bags. This limits the use of Tedlar bags only for very limited time and carefully selected purpose. Our results highlight the importance of careful design and implementation of experiments and clinical protocols to obtain relevant and reliable results.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Non-invasive detection of orthotopic human lung tumors by microRNA expression profiling of mouse exhaled breath condensates and exhaled extracellular vesicles

Aim

The lung is the second most frequent site of metastatic dissemination. Early detection is key to improving survival. Given that the lung interfaces with the external environment, the collection of exhaled breath condensate (EBC) provides the opportunity to obtain biological material including exhaled miRNAs that originate from the lung.

Methods

In this proof-of-principal study, we used the highly metastatic MDA-MB-231 subline 3475 breast cancer cell line (LM-3475) to establish an orthotopic lung tumor-bearing mouse model and investigate non-invasive detection of lung tumors by analysis of exhaled miRNAs. We initially conducted miRNA NGS and qPCR validation analyses on condensates collected from unrestrained animals and identified significant miRNA expression differences between the condensates of lung tumor-bearing and control mice. To focus our purification of EBC and evaluate the origin of these differentially expressed miRNAs, we developed a system to collect EBC directly from the nose and mouth of our mice.

Results

Using nanoparticle distribution analyses, TEM, and ONi super-resolution nanoimaging, we determined that human tumor EVs could be increasingly detected in mouse EBC during the progression of secondary lung tumors. Using our customizable EV-CATCHER assay, we purified human tumor EVs from mouse EBC and demonstrated that the bulk of differentially expressed exhaled miRNAs originate from lung tumors, which could be detected by qPCR within 1 to 2 weeks after tail vein injection of the metastatic cells.

Conclusion

This study is the first of its kind and demonstrates that lung tumor EVs are exhaled in mice and provide non-invasive biomarkers for detection of lung tumors.

Interaction of the III-As monolayer with SARS-CoV-2 biomarkers: implications for biosensor development

The emergence of SARS-CoV-2 in 2019 led to the global COVID-19 pandemic, highlighting the urgency for developing cost-effective and non-invasive methods to detect diseases at an early stage. Human breath, rich in volatile organic compounds (VOCs), is promising for cost-effective and rapid disease detection, with specific VOCs like methanol, ethanal, butanone, acetone, and ethyl butyrate linked to COVID-19. Recent advances in biomarker detection and gas sensing with 2D materials, particularly III-As monolayers like BAs, GaAs, and AlAs, offer high sensitivity at low concentrations, providing a novel avenue for exploring their potential in detecting COVID-19 biomarkers. This article aims to examine the effects of adsorption on different properties of III-Arsenide (BAs, GaAs and AlAs) monolayers, particularly in connection with SARS-CoV-2 biomarkers. In order to examine the interaction between the monolayers and biomarkers, first-principles computations within the framework of density functional theory (DFT) are utilized. The present study involves an investigation of the modifications in the band structure, density of states (DOS), work function, electron density difference, and optical properties (reflectance and absorbance) of III-As monolayers, with the aim of assessing their viability for the detection of SARS-CoV-2 biomarkers along with interfering gases such as CO2 and H2O. It is observed that VOCs induce a notable change in the work function of GaAs which serves as an indicator of the presence of these biomarkers. However, the changes in work function are not as substantial as those for AlAs and BAs. Additionally, the chemiresistive sensitivity, optical sensitivity and recovery time of III-As are investigated. The findings suggest that the pristine GaAs monolayer displays a significant level of sensitivity and selectivity towards the SARS-CoV-2 biomarkers, rendering it a material with potential for utilization in sensing applications. Furthermore, it has been observed that the recovery time of the GaAs monolayer subsequent to its exposure to the VOC biomarkers lies within an acceptable threshold. Upon exposure to UV light, the recovery time is further reduced. The outcomes of our study indicate that GaAs monolayers exhibit considerable potential as chemiresistive, work function-based and optical sensors for the precise and discerning identification of VOCs linked to the SARS-CoV-2 virus compared to the other two III-As monolayers.

Self-powered triboelectric nanogenerator sensor for detecting humidity level and monitoring ethanol variation in a simulated exhalation environment

Respiration stands as a vital process reflecting physiological and pathological human health status. Exhaled breath analysis offers a facile, non-invasive, swift, and cost-effective approach for diagnosing and monitoring diseases by detecting concentration changes of specific biomarkers. In this study, we employed Polyethylene oxide/copper (I) oxide composite nanofibers (PCNFs), synthesized via the electrospinning method as the sensing material to measure ethanol levels (1-200 ppm) in an exhaled breath simulator environment. The integrated contact-separation triboelectric nanogenerator was utilized to power the self-powered PCNFs exhaled breath sensor. The PCNFs-based gas sensor demonstrates promising results with values of 0.9 and 3.2 for detecting 5 ppm and 200 ppm ethanol, respectively, in the presence of interfering gas at 90% relative humidity (RH). Notably, the sensor displayed remarkable ethanol selectivity, with ratios of 10:1 to methanol and 25:1 to acetone. Response and recovery times for 200 ppm ethanol at 90 RH% were rapid, at 2.7 s and 5.8 s, respectively. The PCNFs-based exhaled breath sensor demonstrated consistent and stable performance in practical conditions, showcasing its potential for integration into wearable devices. This self-powered breath sensor enabling continuous monitoring of lung cancer symptoms and facilitating compliance checks with legal alcohol consumption limits.

Preservation of exhaled breath samples for analysis by off-line SESI-HRMS: proof-of-concept study

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) is an established technique in the field of breath analysis characterized by its short analysis time, as well as high levels of sensitivity and selectivity. Traditionally, SESI-HRMS has been used for real-time breath analysis, which requires subjects to be at the location of the analytical platform. Therefore, it limits the possibilities for an introduction of this methodology in day-to-day clinical practice. However, recent methodological developments have shown feasibility on the remote sampling of exhaled breath in Nalophan® bags prior to measurement using SESI-HRMS. To further explore the range of applications of this method, we conducted a proof-of-concept study to assess the impact of the storage time of exhaled breath in Nalophan® bags at different temperatures (room temperature and dry ice) on the relative intensities of the compounds. In addition, we performed a detailed study of the storage effect of 27 aldehydes related to oxidative stress. After 2 h of storage, the mean of intensity of allm/zsignals relative to the samples analyzed without prior storage remained above 80% at both room temperature and dry ice. For the 27 aldehydes, the mean relative intensity losses were lower than 20% at 24 h of storage, remaining practically stable since the first hour of storage following sample collection. Furthermore, the mean relative intensity of most aldehydes in samples stored at room temperature was higher than those stored in dry ice, which could be related to water vapor condensation issues. These findings indicate that the exhaled breath samples could be preserved for hours with a low percentage of mean relative intensity loss, thereby allowing more flexibility in the logistics of off-line SESI-HRMS studies.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Analysis of a Broad Range of Carbonyl Metabolites in Exhaled Breath by UHPLC-MS

Analysis of volatile organic compounds (VOCs) in exhaled breath (EB) has shown great potential for disease detection including lung cancer, infectious respiratory diseases, and chronic obstructive pulmonary disease. Although many breath sample collection and analytical methods have been developed for breath analysis, analysis of metabolic VOCs in exhaled breath is still a challenge for clinical application. Many carbonyl compounds in exhaled breath are related to the metabolic processes of diseases. This work reports a method of ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) for the analysis of a broad range of carbonyl metabolites in exhaled breath. Carbonyl compounds in the exhaled breath were captured by a fabricated silicon microreactor with a micropillar array coated with 2-(aminooxy)ethyl-N,N,N-trimethylammonium (ATM) triflate. A total of six subgroups consisting of saturated aldehydes and ketones, hydroxy-aldehydes, and hydroxy-ketones, unsaturated 2-alkenals, and 4-hydroxy-2-alkenals were identified in the exhaled breath. The combination of a silicon microreactor for the selective capture of carbonyl compounds with UHPLC-MS analysis may provide a quantitative method for the analysis of carbonyls to identify disease markers in exhaled breath.

Remote Medical Scent Detection of Cancer and Infectious Diseases With Dogs and Rats: A Systematic Review

BACKGROUND

Remote medical scent detection of cancer and infectious diseases with dogs and rats has been an increasing field of research these last 20 years. If validated, the possibility of implementing such a technique in the clinic raises many hopes. This systematic review was performed to determine the evidence and performance of such methods and assess their potential relevance in the clinic.

METHODS

Pubmed and Web of Science databases were independently searched based on PRISMA standards between 01/01/2000 and 01/05/2021. We included studies aiming at detecting cancers and infectious diseases affecting humans with dogs or rats. We excluded studies using other animals, studies aiming to detect agricultural diseases, diseases affecting animals, and others such as diabetes and neurodegenerative diseases. Only original articles were included. Data about patients' selection, samples, animal characteristics, animal training, testing configurations, and performances were recorded.

RESULTS

A total of 62 studies were included. Sensitivity and specificity varied a lot among studies: While some publications report low sensitivities of 0.17 and specificities around 0.29, others achieve rates of 1 sensitivity and specificity. Only 6 studies were evaluated in a double-blind screening-like situation. In general, the risk of performance bias was high in most evaluated studies, and the quality of the evidence found was low.

CONCLUSIONS

Medical detection using animals' sense of smell lacks evidence and performances so far to be applied in the clinic. What odors the animals detect is not well understood. Further research should be conducted, focusing on patient selection, samples (choice of materials, standardization), and testing conditions. Interpolations of such results to free running detection (direct contact with humans) should be taken with extreme caution. Considering this synthesis, we discuss the challenges and highlight the excellent odor detection threshold exhibited by animals which represents a potential opportunity to develop an accessible and non-invasive method for disease detection.

Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields.

Non-invasive Exhaled Breath and Skin Analysis to Diagnose Lung Cancer: Study of Age Effect on Diagnostic Accuracy

Development of simple, fast, and non-invasive tests for lung cancer diagnostics is essential for clinical practice. In this paper, exhaled breath and skin were studied as potential objects to diagnose lung cancer. The influence of age on the performance of diagnostic models was studied. Gas chromatography in combination with mass spectrometry (MS) was used to analyze the exhaled breath of 110 lung cancer patients and 212 healthy individuals of various ages. Peak area ratios of volatile organic compounds (VOCs) were used for data analysis instead of VOC peak areas. Various machine learning algorithms were applied to create diagnostic models, and their performance was compared. The best results on the test data set were achieved using artificial neural networks (ANNs): classification of patients with lung cancer and young healthy volunteers: 88 ± 4% sensitivity and 83 ± 3% specificity; classification of patients with lung cancer and old healthy individuals: 81 ± 3% sensitivity and 85 ± 1% specificity. The difference between performance of models based on young and old healthy groups was minor. The results obtained have shown that metabolic dysregulation driven by the disease biology is too high, which significantly overlaps the age effect. The influence of tumor localization and histological type on exhaled breath samples of lung cancer patients was studied. Statistically significant differences between some parameters in these samples were observed. A possibility of assessing the disease status by skin analysis in the Zakharyin-Ged zones using an electronic nose based on the quartz crystal microbalance sensor system was evaluated. Diagnostic models created using ANNs allow us to classify the skin composition of patients with lung cancer and healthy subjects of different ages with a sensitivity of 69 ± 2% and a specificity of 68 ± 8% for the young healthy group and a sensitivity of 74 ± 7% and a specificity of 66 ± 6% for the old healthy group. Primary results of skin analysis in the Zakharyin-Ged zones for the lung cancer diagnosis have shown its utility, but further investigation is required to confirm the results obtained.

C. elegans as a Powerful Tool for Cancer Screening

Regular cancer screening is critical for early cancer detection. Cancer screening tends to be burdensome, invasive, and expensive, especially for a comprehensive multi-organ check. Improving the rate and effectiveness of routine cancer screenings remain a challenge in health care. Multi-cancer early detection (MCED) is an exciting concept and a potentially effective solution for addressing current issues with routine cancer screening. In recent years, several technologies have matured for MCED, such as identifying cell-free tumor DNA in blood or using organisms such as Caenorhabditis elegans as a tool for early cancer detection. In Japan, N-NOSE is a commercially available multi-cancer detection test based on the chemotaxis of C. elegans using a urine sample showing 87.5% sensitivity and 90.2% specificity. In this review, we focus on using C. elegans as a powerful biosensor for universal cancer screening. We review N-NOSE clinical research results, spotlighting it as an effective primary cancer screening test.

Surface-Enhanced Electrochemiluminescence Imaging for Multiplexed Immunoassays of Cancer Markers in Exhaled Breath Condensates

Recently we have demonstrated that the surface plasmon of noble metal nanoparticles can effectively enhance the ECL intensity of Ru(bpy)₃²⁺, and we named this detection principle as surface-enhanced electrochemiluminescence (SEECL-I). However, SEECL based on photomultiplier tube (PMT) detection can only detect one target at a time, which is not suitable for multiple targets detection. In this work, we combined our previous developed SEECL with a bioimaging device to develop a novel multiplexed immunassay for simultaneous and fast analysis of cancer markers. A core-shell nanocomposite consisted of gold-silicon dioxide nanoparticles doped with Ru(bpy)₃²⁺(Au@SiO₂-Ru) with strong ECL emission was employed as ECL label due to the localized surface plasmon resonance (LSPR) of AuNPs, which can significantly enhance the ECL emission of Ru(bpy₎₃²⁺. The ECL signals from the 4 × 4 electrode arrays were collected using the constant potential method (current-time curve method) imaging with a sCOMS camera. As a proof-of-concept application, we demonstrated the use of the proposed SEECL-I for simultaneous detection of carcinoembryonic antigen (CEA), neuron specific enolase (NSE), and squamous cell carcinoma antigen (SCC) in exhaled breath condensates (EBCs) with low detection limit (LOD) of 0.17, 0.33, and 0.33 pg/mL (S/N = 3), respectively. The results demonstrated that the proposed SEECL-I strategy can provide a high sensitivity, fast analysis, and high-throughput platform for clinical diagnosis of cancer markers in EBCs.

Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study

BACKGROUND

Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing.

METHODS

The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs.

FINDINGS

In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%.

INTERPRETATION

Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs.

FUNDING

National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

Review of non-invasive detection of SARS-CoV-2 and other respiratory pathogens in exhaled breath condensate

In 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged to cause high viral infectivity and severe respiratory illness in humans (COVID-19). Worldwide, limited pandemic mitigation strategies, including lack of diagnostic test availability, resulted in COVID-19 overrunning health systems and spreading throughout the global population. Currently, proximal respiratory tract (PRT) specimens such as nasopharyngeal swabs are used to diagnose COVID-19 because of their relative ease of collection and applicability in large scale screening. However, localization of SARS-CoV-2 in the distal respiratory tract (DRT) is associated with more severe infection and symptoms. Exhaled breath condensate (EBC) is a sample matrix comprising aerosolized droplets originating from alveolar lining fluid that are further diluted in the DRT and then PRT and collected via condensation during tidal breathing. The COVID-19 pandemic has resulted in recent resurgence of interest in EBC collection as an alternative, non-invasive sampling method for the staging and accurate detection of SARS-CoV-2 infections. Herein, we review the potential utility of EBC collection for detection of SARS-CoV-2 and other respiratory infections. While much remains to be discovered in fundamental EBC physiology, pathogen-airway interactions, and optimal sampling protocols, EBC, combined with emerging detection methods, presents a promising non-invasive sample matrix for detection of SARS-CoV-2.

Diagnostic Value of Imaging Combined With Tumor Markers in Early Detection of Lung Cancer

Objective: The objective of this research is to explore the diagnostic value of imaging plus tumor markers in the early detection of lung cancer. Methods: Sixty patients with lung cancer treated in our hospital from January 2018 to January 2019 were selected as group A. They were matched with 60 patients with benign lung disease as group B and 60 healthy subjects examined in our hospital as group C. The carcino-embryonic antigen (CEA), CYFRA21-1, and neuron-specific enolase (NSE) were assessed, and the diagnostic value of tumor markers plus imaging in lung cancer diagnosis was explored. Results: The CEA, CYFRA21-1, and NSE in group A were evidently superior to those in groups B and C, and those in group B were superior to those in group C (all P < 0.001). CEA had the highest sensitivity (56.7%), and NSE had the highest specificity (93.3%). The tumor markers plus imaging had the highest sensitivity for different types of lung cancer, and the sensitivity to early lung cancer (90%) was superior to other diagnostic methods (P < 0.05). Conclusion: The tumor markers plus imaging is of great significance in early lung cancer diagnosis and provides a reference for judging the pathological classification.

Noninvasive real-time breath test for controlling hormonal background of the human body: detection of serotonin and melatonin with quantum point-contact sensors

Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains ofHelicobacter pyloriand selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient's breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

Exhaled breath analysis using GC-MS and an electronic nose for lung cancer diagnostics

Exhaled breath analysis is an interesting and promising approach for the diagnostics of various diseases. Being non-invasive, convenient and simple, this approach has tremendous potential utility for further translation into clinical practice. In this study, gas chromatography-mass spectrometry (GC-MS) and quartz microbalance sensor-based "electronic nose" were applied for analysis of the exhaled breath of 40 lung cancer patients and 40 healthy individuals. It was found that the electronic nose was unable to distinguish the samples of different groups. However, the application of GC-MS allowed identifying statistically significant differences in compound peak areas and their ratios for investigated groups. Diagnostic models were created using random forest classifier based on peak areas and their ratios with the sensitivity and specificity of peak areas (ratios) of 85.7-96.5% (75.0-93.1%) and 73.3-85.1% (90.0-92.5%) on training data and 63.6-75.0% (72.7-100.0%) and 50.0-69.2% (76.9-84.6%) on test data, respectively. The exhaled breath samples of lung cancer patients and healthy volunteers could be distinguished by GC-MS with the use of individual compounds, but application of their ratios could help to determine specific differences between investigated groups and the level the influence of individual metabolism features alternating from one person to another as well as daily instrument reproducibility deviations. The electronic nose has to be significantly improved to apply it to lung cancer diagnostics of exhaled breath analysis and the influence of water vapour has to be lowered to increase the sensitivity of the sensors to detect lung cancer biomarkers.

MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.

Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose

This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath

Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.

Fluorination vs. Chlorination: Effect on the Sensor Response of Tetrasubstituted Zinc Phthalocyanine Films to Ammonia

In this work, the effect of fluorine and chlorine substituents in tetrasubstituted zinc phthalocyanines, introduced into the non-peripheral (ZnPcR4-np, R = F, Cl) and peripheral (ZnPcR4-p, R = F, Cl) positions of macrocycle, on their structure and chemiresistive sensor response to low concentration of ammonia is studied. The structure and morphology of the zinc phthalocyanines films (ZnPcR4) were investigated by X-ray diffraction and atomic force microscopy methods. To understand different effects of chlorine and fluorine substituents, the strength and nature of the bonding of ammonia and ZnPcHal4 molecules were studied by quantum chemical simulation. It was shown on the basis of comparative analysis that the sensor response to ammonia was found to increase in the order ZnPcCl4-np < ZnPcF4-np < ZnPcF4-p < ZnPcCl4-p, which is in good agreement with the values of bonding energy between hydrogen atoms of NH3 and halogen substituents in the phthalocyanine rings. ZnPcCl4-p films demonstrate the maximal sensor response to ammonia with the calculated detection limit of 0.01 ppm; however, they are more sensitive to humidity than ZnPcF4-p films. It was shown that both ZnPcF4-p and ZnPcCl4-p and can be used for the selective detection of NH3 in the presence of carbon dioxide, dichloromethane, acetone, toluene, and ethanol.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer

Importance

Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.

Objective

To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

Design, Setting, and Participants

This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.

Exposures

Breath testing and SVM algorithm.

Main Outcomes and Measures

The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).

Results

Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.

Conclusions and Relevance

This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

Detection of lung cancer with electronic nose using a novel ensemble learning framework

Breath analysis based on electronic nose (e-nose) is a promising new technology for the detection of lung cancer that is non-invasive, simple to operate and cost-effective. Lung cancer screening by e-nose relies on predictive models established using machine learning methods. However, using only a single machine learning method to detect lung cancer has some disadvantages, including low detection accuracy and high false negative rate. To address these problems, groups of individual learning models with excellent performance were selected from classic models, including Support Vector Machine, Decision Tree, Random Forest, Logistic Regression and K-nearest neighbor regression, to build an ensemble learning framework (PCA-SVE). The output result of the PCA-SVE framework was obtained by voting. To test this approach, we analyzed 214 breath samples measured by e-nose with 11 gas sensors of four types using the proposed PCA-SVE framework. Experimental results indicated that the accuracy, sensitivity, and specificity of the proposed framework were 95.75%, 94.78%, and 96.96%, respectively. This framework overcomes the disadvantages of a single model, thereby providing an improved, practical alternative for exhaled breath analysis by e-nose.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Influence of Chronic Obstructive Pulmonary Disease on Volatile Organic Compounds in Patients with Non-Small Cell Lung Cancer

INTRODUCTION

The etiology of lung cancer is multifactorial. Exposure to tobacco smoke and the role played by the carcinogenic compounds that it contains would explain the common association between lung cancer and chronic obstructive pulmonary disease (COPD), which is closely linked to tobacco use. In both diseases, sustained inflammation is caused by increased oxidative stress (for example, lipid peroxidation). This generates low molecular weight substances called volatile organic compounds (VOC) that are excreted during breathing. VOC metabolomics provides an indirect measure of oxidative stress.

OBJECTIVE

The aim of this study was to establish the relative influence of COPD on the VOC profile in patients with non-small cell lung cancer (NSCLC), by first studying the possible variation of VOC associated with lung cancer histology.

PATIENTS AND METHODS

Exhaled air was tested in 107 NSCLC patients, who were divided into 2 groups: NSCLC with COPD and non-COPD with NSCLC. The exhaled air sample was obtained with the BIOVOC® sampler, and transferred to desorption tubes for later analysis by thermal desorption-gas chromatography-mass spectrometry. The VOC analysis showed lineal aldehydes and carboxylic acids.

RESULTS AND CONCLUSIONS

No statistically significant differences were found in VOC associated with histology. NSCLC and COPD patients present a 1.7-fold (1.1-2.7) greater probability of detection of propionic acid (95% CI: 1.22-6.2) than patients without COPD or NSCLC (p = 0.013).

Chemical signature of colorectal cancer: case-control study for profiling the breath print

BACKGROUND

Effective screening for colorectal cancer can reduce mortality by early detection of tumours and colonic polyps. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a potential non-invasive diagnostic tool for detection of cancer. The aim of this study was to evaluate the reliability of breath-testing for colorectal cancer screening and early diagnosis using an advanced breath sampler.

METHODS

The exhaled breath of patients with colorectal cancer and non-cancer controls with negative findings on colonoscopy was collected using the ReCIVA® Breath Sampler. This portable device is able to capture the alveolar breath fraction without environmental contamination. VOCs were desorbed thermally and analysed by gas chromatography-mass spectrometry. The discriminatory ability of VOCs in detecting colorectal cancer was evaluated by receiver operating characteristic (ROC) curve analysis for each VOC, followed by cross-validation by the leave-one-out method, and by applying stepwise logistic regression analysis.

RESULTS

The study included 83 patients with colorectal cancer and 90 non-cancer controls. Fourteen VOCs were found to have significant discriminatory ability in detecting patients with colorectal cancer. The model with the diagnosis of cancer versus no cancer resulted in a statistically significant likelihood of discrimination of 173·45 (P < 0·001), with an area under the ROC curve of 0·979. Cross-validation of the model resulted in a true predictive value for colorectal cancer of 93 per cent overall. Reliability of the breath analysis was maintained irrespective of cancer stage.

CONCLUSION

This study demonstrated that analysis of exhaled VOCs can discriminate patients with colorectal cancer from those without. This finding may eventually lead to the creation of a smart online sensory device, capable of providing a binary answer (cancer/no cancer) and directing to further screening.

Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons

The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal-Wallis and Mann-Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001-0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve-AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient's physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis.

Non-invasive cancer detection using volatile biomarkers: Is urine superior to breath?

In recent years numerous reports have highlighted the options of chemical breath analysis with regard to non-invasive cancer detection. Certain volatile organic compounds (VOC) supposedly present in higher amounts or in characteristic patterns have been suggested as potential biomarkers. However, so far no clinical application based on a specific set of compounds appears to exist. Numerous reports on the capability of sniffer dogs and sensor arrays or electronic noses to distinguish breath of cancer patients and healthy controls supports the concept of genuine cancer-related volatile profiles. However, the actual compounds responsible for the scent are completely unknown and there is no correlation with the potential biomarkers suggested on basis of chemical trace analysis. It is outlined that specific features connected with the VOC analysis in breath - namely small concentrations of volatiles, interfering background concentrations, considerable sampling effort and sample instability, impracticability regarding routine application - stand in the way of substantial progress. The underlying chemical-analytical challenge can only be met considering the severe susceptibility of VOC determination to these adverse conditions. Therefore, the attention is drawn to the needs for appropriate quality assurance/quality control as the most important feature for the reliable quantification of volatiles present in trace concentration. Consequently, the advantages of urine as an alternative matrix for volatile biomarker search in the context of diagnosing lung and other cancers are outlined with specific focus on quality assurance and practicability in clinical chemistry. The headspace over urine samples as the VOC source allows adapting gas chromatographical procedures well-established in water analysis. Foremost, the selection of urine over breath as non-invasive matrix should provide considerably more resilience to adverse effects during sampling and analysis. The most important advantage of urine over breath is seen in the option to partition, dispense, mix, spike, store, and thus to dispatch taylor-made urine samples on demand for quality control measures. Although it is still open at this point if cancer diagnosis supported by non-invasively sampled VOC profiles will ultimately reach clinical application the advantages of urine over breath should significantly facilitate urgently required steps beyond the current proof-of-concept stage and towards standardisation.

A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms

Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different -omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Constructing an E-Nose Using Metal-Ion-Induced Assembly of Graphene Oxide for Diagnosis of Lung Cancer via Exhaled Breath

A flexible electronic-nose (E-nose) was constructed by assembling graphene oxide (GO) using different types of metal ions (M^x+) with different ratio of GO to M^x+. Owing to the cross-linked networks, the M^x+-induced assembly of graphene films resulted in different porous structures. A chemi-resistive sensor array was constructed by coating the GO-M hybrid films on PET substrate patterned with 8 interdigited electrodes, followed by in situ reduction of GO to rGO with hydrazine vapor. Each of the sensing elements on the sensor array showed a cross-reactive response toward different types of gases at room temperature. Compared to bare rGO, incorporation of metal species into rGO significantly improved sensitivity owing to the additional interaction between metal species and gas analyte. Principle component analysis (PCA) showed that four types of exhaled breath (EB) biomarkers including acetone, isoprene, ammonia, and hydrothion in sub-ppm concentrations can be discriminated well. To overcome the interference from humidity in EB, a protocol to collect and analyze EB gases was established and further validated by simulated EB samples. Finally, clinical EB samples collected from patients with lung cancer and healthy controls were analyzed. In a 106 case study, the healthy group can be accurately distinguished from lung cancer patients by linear discrimination analysis. With the assistance of an artificial neural network, a sensitivity of 95.8% and specificity of 96.0% can be achieved in the diagnosis of lung cancer based on the E-nose. We also find that patients with renal failure can be distinguished through comparison of dynamic response curves between patient and healthy samples on some specific sensing elements. These results demonstrate the proposed E-nose will have great potential in noninvasive disease screening and personalized healthcare management.

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

A Metal Oxide Gas Sensors Array for Lung Cancer Diagnosis Through Exhaled Breath Analysis

Lung cancer high mortality rate is mainly related to late-stage tumor diagnosis. Survival rates and treatments could be greatly improved with an effective early diagnosis. Volatile organic compounds (VOCs) in exhaled breath have been known for long to be linked to the presence of a disease. Exhaled breath analysis for early diagnosis of lung cancer represents a non-invasive, low-cost and user-friendly approach. In this paper we present the design and development of an electronic nose based on a metal oxide sensors array for the early diagnosis of lung cancer. Breath samples collected from healthy controls (n=10) and lung cancer subjects (n=6) were analyzed by the electronic nose, and classification was performed using an artificial neural network (ANN). A sensitivity of 85.7%, specificity of 100%, and accuracy of 93.8% were reached with leave one out cross validation (LOOCV). The presented device demonstrates that a simple, cost-effective, and non-invasive approach based on exhaled breath analysis has the potential to be of great help in decreasing lung cancer mortality.

Two-step investigation of lung cancer detection by sniffer dogs

Early detection of lung cancer (LC) is a priority since LC is characterized by symptoms mimicking other respiratory conditions, but it remains the leading cause of oncological disease death. Properly trained dogs can perceive the volatile organic compounds (VOCs) related to cancer thanks to their acute sense of smell. The use of dogs for LC detection could be advantageous: reliably trained dogs would represent a valuable, cost-effective, non-invasive method of screening, which gives a clear-cut yes/no response. However, whether sniffer dogs are able to maintain their discriminative capacity under long-term control, and in different types of environments, needs further investigation. In this study, we sought to test two hypotheses: firstly, if dogs can be trained to perceive LC-related VOCs in human urine, a substrate which is not influenced by the carrier materials and may thus be a good candidate for large-number screening; and secondly, whether trained dogs retain their performance stability over time, even if the environment in which the tests are carried out varies. We have selected three family dogs that underwent a one-year training period (two weekly training sessions) by the clicker training method. At the end of the training, the dogs underwent two separate test phases, in two different locations, one year apart. All the other procedures had been maintained unchanged. The donors of the samples submitted to the dogs were recruited by the European Institute of Oncology (IEO), Milan, Italy. The results show that the dogs had different sensitivity (range: 45%-73%) and specificity rates (range: 89%-91%), and were deceived neither by lung conditions (that the dogs did not consider) nor by the existence of tumors in the beginning stage, that were correctly reported by the dogs. The one-year interruption of the research work and the changes in the test environment did not induce statistically significant differences in the dogs' perceptive capacity. To our knowledge, so far, these issues have never been highlighted.

Influence of Chronic Obstructive Pulmonary Disease on Volatile Organic Compounds in Patients with Non-small Cell Lung Cancer

INTRODUCTION

The etiology of lung cancer is multifactorial. Exposure to tobacco smoke and the role played by the carcinogenic compounds that it contains would explain the common association between lung cancer and chronic obstructive pulmonary disease (COPD), a disease which is very much linked to tobacco use. In both diseases, sustained inflammation is caused by increased oxidative stress (for example, lipid peroxidation). This generates low molecular weight substances called volatile organic compounds (VOC) that are excreted during breathing. VOC metabolomics provides an indirect measure of oxidative stress.

OBJECTIVE

The aim of this study was to establish the relative influence of COPD on the VOC profile in patients with non-small cell lung cancer (NSCLC), by first studying the possible variation of VOC associated with lung cancer histology.

PATIENTS AND METHODS

Exhaled air was tested in 107 NSCLC patients, who were divided into 2groups: NSCLC with COPD and non-COPD with NSCLC. The exhaled air sample was obtained with the BIOVOC® sampler, and transferred to desorption tubes for later analysis by thermal desorption-gas chromatography-mass spectrometry. The VOC analysis showed lineal aldehydes and carboxylic acids.

RESULTS AND CONCLUSIONS

No statistically significant differences were found in VOC associated with histology. NSCLC and COPD patients present a 1.7-fold (1.1-2.7) probability of detection of propionic acid (95% CI: 1.22- 6.2) than patients without COPD or NSCLC (P = 0.013).

Recent advances in breath analysis to track human health by new enrichment technologies

Detection of biomarkers in exhaled breath has been gaining increasing attention as a tool for diagnosis of specific diseases. However, rapid and accurate quantification of biomarkers associated with specific diseases requires the use of analytical methods capable of fast sampling and preconcentration from breath matrix. In this regard, solid phase microextraction and needle trap technology are becoming increasingly popular in the field of breath analysis due to the unique benefits imparted by such methods, such as the integration of sampling, extraction, and preconcentration in a single step. This review discusses recent advances in breath analysis using these sample preparation techniques, providing a summary of recent developments of analytical methods based on breath volatile organic compounds analysis, including the successful identification of various biomarkers related to human diseases.