Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.

INTRODUCTION: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population. OBJECTIVES: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs). METHODS: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers. RESULTS: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

A Dual Sensing Platform for Human Exhaled Breath Enabled by Fe-MIL-101-NH2 Metal-Organic Frameworks and its Derived Co/Ni/Fe Trimetallic Oxides.

Limited by the insufficient active sites and the interference from breath humidity, designing reliable gas sensing materials with high activity and moisture resistance remains a challenge to analyze human exhaled breath for the translational application of medical diagnostics. Herein, the dual sensing and cooperative diagnosis is achieved by utilizing metal-organic frameworks (MOFs) and its derivative. The Fe-MIL-101-NH2 serves as the quartz crystal microbalance humidity sensing layer, which exhibits high selectivity and rapid response time (16 s/15 s) to water vapor. Then, the Co2+ and Ni2+ cations are further co-doped into Fe-MIL-101-NH2 host to obtain the derived Co/Ni/Fe trimetallic  oxides (CoNiFe-MOS-n). The chemiresistive CoNiFe-MOS-n sensor displays the high sensitivity (560) and good selectivity to acetone, together with a lower original resistance compared with Fe2 O3 and NiFe2 O4 . Moreover, as a proof-of-concept application, synergistic integration of Fe-MIL-101-NH2 and derived CoNiFe-MOS-n is carried out. The Fe-MIL-101-NH2 is applied as moisture sorbent materials, which realize a sensitivity compensation of CoNiFe-MOS-n sensors for the detection of acetone (biomarker gas of diabetes). The findings provide an insight for effective utilization of MOFs and the derived materials to achieve a trace gas detection in exhaled breath analysis.

Detecting Coronary Artery Disease Using Exhaled Breath Analysis.

INTRODUCTION: Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide, and there is an unmet need for a simple, inexpensive, noninvasive tool aimed at CAD detection. The aim of this pilot study was to evaluate the possible use of breath analysis in detecting the presence of CAD. MATERIALS AND METHODS: In a prospective study, breath from patients with no history of CAD who presented with acute chest pain to the emergency room was sampled using a designated portable electronic nose (eNose) system. First, breath samples from 60 patients were analyzed and categorized as obstructive, nonobstructive, and no-CAD according to the actual presence and extent of CAD as was demonstrated on cardiac imaging (either computerized tomography angiography or coronary angiography). Classification models were built according to the results, and their diagnostic performance was then examined in a blinded manner on a new set of 25 patients. The data were compared with the actual results of coronary arteries evaluation. Sensitivity, specificity, and accuracy were calculated for each model. RESULTS: Obstructive CAD was correctly distinguished from nonobstructive and no-CAD with 89% sensitivity, 31% specificity, 83% negative predictive value (NPV), 42% positive predictive value (PPV), and 52% accuracy. In another model, any extent of CAD was successfully distinguished from no-CAD with 69% sensitivity, 67% specificity, 54% NPV, 79% PPV, and 68% accuracy. CONCLUSION: This proof-of-concept study shows that breath analysis has the potential to be used as a novel rapid, noninvasive diagnostic tool to help identify presence of CAD in patients with acute chest pain.

First-Principles Insight into a B4C3 Monolayer as a Promising Biosensor for Exhaled Breath Analysis.

Nanomaterial-based room temperature gas sensors are used as a screening tool for diagnosing various diseases through breath analysis. The stable planar structure of boron carbide (B4C3) is utilized as a base material for adsorption of human breath exhaled VOCs, namely formaldehyde, methanol, acetone, toluene along, with interfering gases of carbon dioxide and water. The adsorption energy, charge density, density of states, energy band gap variation, recovery time, sensitivity, and work function of adsorbed molecules on pristine B4C3 are analyzed by density functional theory. The computed adsorption energies of VOC are in the range of - 0.176 to - 0.238 eV, and a larger interaction distance validate the physisorption behavior of these VOCs biomarkers on pristine boron carbide monolayer. Minute changes are determined from the electronic band structure of all adsorbed systems conserving the semiconducting nature of the B4C3 monolayer. The band gap variation upon adsorption of VOCs and interfering gases is examined between 0.05 and 0.52%. The 13.63 × 10-9 s recovery time of methanol is slower among VOCs, and 0.556 × 10-9 s of carbon dioxide (CO2) is faster for desorption. The results reveal that boron carbide can be utilized as a biosensor at room temperature for the analysis of exhaled VOCs from human breath.

Molecular approaches to lung cancer prevention.

Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches.

Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics.

Human exhaled breath consists of more than 3000 volatile organic compounds, many of which are relevant biomarkers for various diseases. Although gas chromatography has been the gold standard for volatile organic compound (VOC) detection in exhaled breath, recent developments in mid-infrared (MIR) laser spectroscopy have led to the promise of compact point-of-care (POC) optical instruments enabling even single breath diagnostics. In this review, we discuss the evolution of MIR sensing technologies with a special focus on photoacoustic spectroscopy, and its application in exhaled breath biomarker detection. While mid-infrared point-of-care instrumentation promises high sensitivity and inherent molecular selectivity, the lack of standardization of the various techniques has to be overcome for translating these techniques into more widespread real-time clinical use.

Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept study.

Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae (SP) or Pseudomonas aeruginosa (PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography-mass-spectrometry (TD-GC-MS) and selected ion flow tube-mass spectrometry (SIFT-MS) of exhaled breath. Male adult rats (n = 50) received an intratracheal inoculation of 1) 200 µl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TD-GC-MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73-0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94-1) and CCR of 99.9% for SP versus PA, 0.92 (0.83-1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92-1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens.

Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath.

BACKGROUND: Immune checkpoint inhibitors have improved survival outcome of advanced non-small-cell lung cancer (NSCLC). However, most patients do not benefit. Therefore, biomarkers are needed that accurately predict response. We hypothesized that molecular profiling of exhaled air may capture the inflammatory milieu related to the individual responsiveness to anti-programmed death ligand 1 (PD-1) therapy. This study aimed to determine the accuracy of exhaled breath analysis at baseline for assessing nonresponders versus responders to anti-PD-1 therapy in NSCLC patients. METHODS: This was a prospective observational study in patients receiving checkpoint inhibitor therapy using both a training and validation set of NSCLC patients. At baseline, breath profiles were collected in duplicate by a metal oxide semiconductor electronic nose (eNose) positioned at the rear end of a pneumotachograph. Patients received nivolumab or pembrolizumab of which the efficacy was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. Data analysis involved advanced signal-processing and statistics based on independent t-tests followed by linear discriminant and receiver operating characteristic (ROC) analysis. RESULTS: Exhaled breath data of 143 NSCLC patients (training: 92, validation: 51) were available at baseline. ENose sensors contributed significantly (P < 0.05) at baseline in differentiating between patients with different responses at 3 months of anti-PD-1 treatment. The eNose sensors were combined into a single biomarker with an ROC-area under the curve (AUC) of 0.89 [confidence interval (CI) 0.82-0.96]. This AUC was confirmed in the validation set: 0.85 (CI 0.75-0.96). CONCLUSION: ENose assessment was effective in the noninvasive prediction of individual patient responses to immunotherapy. The predictive accuracy and efficacy of the eNose for discrimination of immunotherapy responder types were replicated in an independent validation set op patients. This finding can potentially avoid application of ineffective treatment in identified probable nonresponders.

Cascade laser sensing concepts for advanced breath diagnostics.

With more than a thousand constituents at trace level concentrations, exhaled breath analysis (EBA) allows for non-invasive point-of-care (POC) disease diagnostics and metabolic status monitoring in or close to real-time. A number of biomarkers in breath may be used to not only identify diseases and disease progression but also to monitor therapeutic interventions. Although the relationship of selected breath components/biomarkers with certain disease pathologies is well established, diagnosing the exhaled breath composition remains an analytical and practical challenge due to the concentration levels of molecules of interest, i.e., low parts-per-billion (ppb) regime and below. Besides the analytical assessment of breath components via conventional methods such as gas chromatography coupled to mass spectrometry and related techniques, the application of cascade laser spectroscopy (CLS) is relatively new and exhibits several advantages when aiming for compact and user-friendly trace gas sensors with high molecular selectivity, the required sensitivity, and potentially reasonable instrumental costs. This trend article highlights the current status and potential of CLS in breath diagnostics with a focus on recent advancements in instrumentation and application along with future prospects and challenges. Graphical abstract Cascade laser technology in the mid-infrared spectral range enables sensitive and molecularly selective exhaled breath analysis with near real-time response, label-free detection, and point-of-care feasibility.

Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO2 and O2 at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.

Profiling of volatile organic compounds produced by clinical Aspergillus isolates using gas chromatography-mass spectrometry.

Volatile organic compounds (VOCs) in exhaled breath may identify the presence of invasive pulmonary aspergillosis. We aimed to detect VOC profiles emitted by in vitro cultured, clinical Aspergillus isolates using gas chromatography-mass spectrometry (GC-MS). Three clinical Aspergillus isolates and a reference strain were cultured while conidiation was prevented. Headspace samples were analyzed using a standardized method. Breath samples of patients from which the cultures were obtained were checked for the presence of the VOCs found in vitro. Each Aspergillus isolate produced a distinct VOC profile. These profiles could not be confirmed in exhaled breath in vivo.

Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma.

BACKGROUND: Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids (ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control. OBJECTIVES: To determine whether exhaled volatile organic compounds (VOCs) as measured by gas-chromatography/mass-spectrometry (GC/MS) and electronic nose (eNose) technology discriminate between clinically stable and unstable episodes of asthma. METHODS: Twenty-three patients with (partly) controlled mild to moderate persistent asthma using ICS were included in this prospective steroid withdrawal study. Exhaled metabolites were measured at baseline, during loss of control and after recovery. Standardized sampling of exhaled air was performed, after which samples were analysed by GC/MS and eNose. Univariate analysis of covariance (ANCOVA), followed by multivariate principal component analysis (PCA) was used to reduce data dimensionality. Next paired t tests were utilized to analyse within-subject breath profile differences at the different time-points. Finally, associations between exhaled metabolites and sputum inflammation markers were examined. RESULTS: Breath profiles by eNose showed 95% (21/22) correct classification for baseline vs loss of control and 86% (19/22) for loss of control vs recovery. Breath profiles using GC/MS showed accuracies of 68% (14/22) and 77% (17/22) for baseline vs loss of control and loss of control vs recovery, respectively. Significant associations between exhaled metabolites captured by GC/MS and sputum eosinophils were found (Pearson r≥.46, P<.01). CONCLUSIONS & CLINICAL RELEVANCE: Loss of asthma control can be discriminated from clinically stable episodes by longitudinal monitoring of exhaled metabolites measured by GC/MS and particularly eNose. Part of the uncovered biomarkers was associated with sputum eosinophils. These findings provide proof of principle for monitoring and identification of loss of asthma control by breathomics.

Breath-print analysis by e-nose may refine risk stratification for adverse outcomes in cirrhotic patients.

BACKGROUND & AIMS: The spectrum of volatile organic compounds in the exhaled breath (breath-print, BP) has been shown to characterize patients with cirrhosis and with worse hepatic function. However, the association of different BPs with clinically relevant outcomes has not been described yet. Hence, we aimed to evaluate the association between BPs, mortality and hospitalization in cirrhotic patients and to compare it with that of the "classical" prognostic indices (Child-Pugh Classification [CPC] and MELD). METHODS: Eighty-nine cirrhotic patients (M/F 59/30, mean age 64.8 ± 11.3, CPC A/B/C 37/33/19) were recruited and followed up for a median time of 23 months. Clinical and biochemical data were collected. Breath collection and analysis were obtained through Pneumopipe® and BIONOTE e-nose respectively. RESULTS: Four different BP clusters (A, B, C, D) were identified. BP clusters A and D were associated with a significantly increased risk of mortality (HR 2.9, 95% confidence intervals [CI] 1.5-5.6) and hospitalization (HR 2.6, 95% CI 1.4-4.6), even in multiple adjusted models including CPC and MELD score (adjusted [a]HR 2.8, 95% CI 1.1-7.0 for mortality and aHR 2.2, 95% CI 1.1-4.2 for hospitalization). CPC C maintained the strongest association with both mortality (aHR 17.6, 95% CI 1.8-174.0) and hospitalization (aHR 12.4, 95% CI 2.0-75.8). CONCLUSIONS: This pilot study demonstrates that BP clusters are associated with significant clinical endpoints (mortality and hospitalization) even independently from "classical" prognostic indices. Even though further studies are warranted on this topic, our findings suggest that the e-nose may become an adjunctive aid to stratify the risk of adverse outcomes in cirrhotic patients.

Noninvasive detection of gastric cancer.

Gastric cancer (GC) is the fifth most common cancer and the third common cause of cancer death worldwide. Endoscopy is the most effective method for GC screening, but its application is limited by the invasion. Therefore, continuous efforts have been made to develop noninvasive methods for GC detection and promising results have been reported. Here, we review the advances in GC detection by protein and nucleic acid tumor markers, circulating tumor cells, and tumor-associated autoantibodies in peripheral blood. Some potential new noninvasive methods for GC detection are also reviewed, including exhaled breath analysis, blood spectroscopy analysis and molecular imaging.

Exhaled Breath Analysis in Heart Failure.

Heart failure (HF) is a clinical condition that presents high morbidity and mortality and is one of the main reasons for hospital admissions all over the world. Although biochemical processes that occur in the body during heart failure are known, this syndrome is still associated to poor prognosis. Exhaled breath analysis has emerged as a promising noninvasive tool in different clinical conditions and, recently, it has been also tested in patients with HF. This review presents the main breath HF biomarkers, which reflect metabolic changes that occur in this complex syndrome. It also discusses the diagnostic and prognostic value of exhaled breath compounds for HF and makes a short description of the main technologies involved in this analysis. Some perspectives on the area are presented as well.

Development of an Exhaled Breath Monitoring System with Semiconductive Gas Sensors, a Gas Condenser Unit, and Gas Chromatograph Columns.

Various volatile organic compounds (VOCs) in breath exhaled by patients with lung cancer, healthy controls, and patients with lung cancer who underwent surgery for resection of cancer were analyzed by gas condenser-equipped gas chromatography-mass spectrometry (GC/MS) for development of an exhaled breath monitoring prototype system involving metal oxide gas sensors, a gas condenser, and gas chromatography columns. The gas condenser-GC/MS analysis identified concentrations of 56 VOCs in the breath exhaled by the test population of 136 volunteers (107 patients with lung cancer and 29 controls), and selected four target VOCs, nonanal, acetoin, acetic acid, and propanoic acid, for use with the condenser, GC, and sensor-type prototype system. The prototype system analyzed exhaled breath samples from 101 volunteers (74 patients with lung cancer and 27 controls). The prototype system exhibited a level of performance similar to that of the gas condenser-GC/MS system for breath analysis.

Breathomics Detect the Cardiovascular Disease: Delusion or Dilution of the Metabolomic Signature.

Volatile organic compounds (VOCs) can be subdivided into exogenous and endogenous categories based on their origin. Analyzing the endogenous VOCs can provide insights into maintaining the internal organs' homeostasis. Despite the ongoing development and the current understanding, studies have suggested a link between cardiovascular metabolic alterations in patients with ischemic heart disease and elevated levels of ethane and isoprene detectable through exhaled breath analysis. Conversely, patients with chronic heart failure exhibit elevated acetone and pentane in their exhaled air. These substances originate from disturbances in the heart tissue, including cellular and subcellular modulations. Hypothetically, ethane levels in the exhaled breath analysis can demonstrate the severity of ischemic heart disease and, consequently, the risk of death in the next 10 years due to cardiovascular disease (CVD). Real-time direct mass spectrometry is the preferred method for assessing VOCs in exhaled breath analysis. The accuracy of this analysis depends on several factors, including the selection of the relevant breath fraction, the type of breath collection container (if used), and the pre-concentration technique.

Detection of lung cancer and stages via breath analysis using a self-made electronic nose device.

BACKGROUND: Breathomics is an emerging area focusing on monitoring and diagnosing pulmonary diseases, especially lung cancer. This research aims to employ metabolomic methods to create a breathprint in human-expelled air to rapidly identify lung cancer and its stages. RESEARCH DESIGN AND METHODS: An electronic nose (e-nose) system with five metal oxide semiconductor (MOS) gas sensors, a microcontroller, and machine learning algorithms was designed and developed for this application. The volunteers in this study include 114 patients with lung cancer and 147 healthy controls to understand the clinical potential of the e-nose system to detect lung cancer and its stages. RESULTS: In the training phase, in discriminating lung cancer from controls, the XGBoost classifier model with 10-fold cross-validation gave an accuracy of 91.67%. In the validation phase, the XGBoost classifier model correctly identified 35 out of 42 patients with lung cancer samples and 44 out of 51 healthy control samples providing an overall sensitivity of 83.33% and specificity of 86.27%. CONCLUSIONS: These results indicate that the exhaled breath VOC analysis method may be developed as a new diagnostic tool for lung cancer detection. The advantages of e-nose based diagnostics, such as an easy and painless method of sampling, and low-cost procedures, will make it an excellent diagnostic method in the future.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study.

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2fold change ⩾1 andq-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z-242.18428 andm/z-117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.