Screening for emphysema via exhaled volatile organic compounds. Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array Breath analysis as a potential diagnostic tool for tuberculosis. Detection of gastro-oesophageal reflux disease (GORD) in patients with obstructive lung disease using exhaled breath profiling. Application of laser spectroscopy for measurement of exhaled ethane in patients with lung cancer. Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study Correlation of Pneumonia Score with Electronic Nose Signature: A Prospective Study Exhaled volatile organic compounds in patients with non-small cell lung cancer: cross sectional and nested short-term follow-up study Electronic Nose Prediction of a Clinical Pneumonia Score: Biosensors and Microbes Detection of lung cancer by sensor array analyses of exhaled breath. Diagnosis of pneumonia with an electronic nose: correlation of vapor signature with chest computed tomography scan findings. Prediction of lung cancer using volatile biomarkers in breath. Non-invasive phenotyping using exhaled volatile organic compounds in asthma Exhaled Breath Analysis with a Colorimetric Sensor Array for the Identification and Characterization of Lung Cancer Detection of lung cancer using weighted digital analysis of breath biomarkers. Point-of-care breath test for biomarkers of active pulmonary tuberculosis. An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls.
Chronic obstructive pulmonary disease ( COPD ) /emphysema risk groups are well defined and screening allows for early identification of disease . The capability of exhaled volatile organic compounds ( VOCs ) to detect emphysema , as found by computed tomography ( CT ) in current and former heavy smokers participating in a lung cancer screening trial , was investigated . CT scans , pulmonary function tests and breath sample collection s were obtained from 204 subjects . Breath sample s were analyzed with a proton-transfer reaction mass spectrometer ( PTR-MS ) to obtain VOC profiles listed as ions at various mass-to-charge ratios ( m/z ) . Using bootstrapped stepwise forward logistic regression , we identified specific breath profiles as a potential tool for the diagnosis of emphysema , of airflow limitation or gas-exchange impairment . A marker for emphysema was found at m/z 87 ( tentatively attributed to 2-methylbutanal ) . The area under the receiver operating characteristic curve ( ROC ) of this marker to diagnose emphysema was 0.588 ( 95 % CI 0.453 - 0.662 ) . Mass-to-charge ratios m/z 52 ( most likely chloramine ) and m/z 135 ( alkyl benzene ) were linked to obstructive disease and m/z 122 ( most probably alkyl homologs ) to an impaired diffusion capacity . ROC areas were 0.646 ( 95 % CI 0.562 - 0.730 ) and 0.671 ( 95 % CI 0.524 - 0.710 ) , respectively . In the screening setting , exhaled VOCs measured by PTR-MS constitute weak markers for emphysema , pulmonary obstruction and impaired diffusion capacity Background : The pattern of volatile organic compounds ( VOCs ) in the exhaled breath of patients with lung cancer may be unique . New sensor systems that detect patterns of VOCs have been developed . One of these sensor systems , a colorimetric sensor array , has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge . The colours of these spots change based on the chemicals with which they come into contact . In this proof of principle study , the ability of this sensor system to detect a pattern of VOCs unique to lung cancer is assessed . Methods : Individuals with lung cancer , those with other lung diseases and healthy controls performed tidal breathing of room air for 12 min while exhaling into a device design ed to draw their breath across a colorimetric sensor array . The colour changes that occurred for each individual were converted into a numerical vector . The vectors were analysed statistically , using a r and om forests technique , to determine whether lung cancer could be predicted from the responses of the sensor . Results : 143 individuals participated in the study : 49 with non-small cell lung cancer , 18 with chronic obstructive pulmonary disease 15 with idiopathic pulmonary fibrosis 20 with pulmonary arterial hypertension 20 with sarcoidosis and 21 controls . A prediction model was developed using observations from 70 % of the subjects . This model was able to predict the presence of lung cancer in the remaining 30 % of subjects with a sensitivity of 73.3 % and a specificity of 72.4 % ( p = 0.01 ) . Conclusions : The unique chemical signature of the breath of patients with lung cancer can be detected with moderate accuracy by a colorimetric sensor array SETTING Cape Town , South Africa . OBJECTIVES We investigated the potential of breath analysis by gas chromatography-mass spectrometry ( GC-MS ) to discriminate between sample s collected prospect ively from patients with suspected tuberculosis ( TB ) . DESIGN Sample s were obtained in a TB-endemic setting in South Africa , where 28 % of culture-proven TB patients had Ziehl-Neelsen ( ZN ) negative sputum smear . A training set of breath sample s from 50 sputum culture-proven TB patients and 50 culture-negative non-TB patients was analysed using GC-MS . We used support vector machine analysis for classification of the patient sample s into TB and non-TB . RESULTS A classification model with seven compounds had a sensitivity of 72 % , a specificity of 86 % and an accuracy of 79 % compared with culture . The classification model was vali date d with breath sample s from a different set of 21 TB and 50 non-TB patients from the same area , giving a sensitivity of 62 % , a specificity of 84 % and an accuracy of 77 % . CONCLUSION This study shows that GC-MS breath analysis is able to differentiate between TB and non-TB breath sample s even among patients with a negative ZN sputum smear but a positive culture for Mycobacterium tuberculosis . We conclude that breath analysis by GC-MS merits further research Gastro-oesophageal reflux disease ( GORD ) has been implicated in the worsening of several respiratory disorders . Current methods of diagnosis lack accuracy , are invasive and can be costly . Recently , novel methods of analysing lung pathophysiology have been developed including the use of an electronic nose and analysis of components of exhaled breath condensate ( EBC ) . We hypothesised that these methods would distinguish patients with GORD from those without GORD in the common obstructive lung diseases and healthy controls . In a cross-sectional study , exhaled breath was analysed using the Cyranose 320 electronic nose , using principal components and canonical discriminant analyses .  EBC pH and pepsin were quantified using a pH meter and an enzyme-linked immunosorbent assay , respectively . A st and ardized reflux disease question naire ( RDQ ) was used to assess reflux symptoms . The Cyranose 320 distinguished exhaled breath profiles of obstructive lung disease patients without GORD from obstructive lung disease patients with GORD ( p = 0.023 , accuracy 67.6 % ) , asthmatic patients with reflux from asthmatics without GORD ( 85 % , p = < 0.015 , interclass M distance > 2.8 ) , but did not produce as robust a profile for patients with COPD and COPD with GORD ( p = 0.047 , accuracy 64 % ) . Patients with obstructive lung disease and GORD had significantly higher levels of EBC pepsin ( 9.81 ± interquartile range ( IQR ) 4.38 ng ml(-1 ) ) than those without GORD ( 4.6 ± IQR 6.95 ng ml(-1 ) ) , as well as healthy controls ( 3.44 ± IQR 7.87 ng ml(-1 ) ; p = < 0.013 ) . EBC pH was not significantly related to the presence of GORD in any group . The RDQ results correlated significantly with the presence of EBC pepsin . This pilot study has shown that exhaled breath profiling can be used for detecting GORD in obstructive lung diseases . While the electronic nose was useful in asthma , EBC pepsin was more helpful in COPD . In this study , several different confounders could potentially have affected results and larger prospect i ve interventional studies are needed There is increasing interest in ethane ( C(2)H(6 ) ) in exhaled breath as a non-invasive marker of oxidative stress ( OS ) and thereby a potential indicator of disease . However , the lack of real-time measurement techniques has limited progress in the field . Here we report on a novel Tunable Diode Laser Spectrometer ( TDLS ) applied to the analysis of exhaled ethane in patients with lung cancer . The patient group ( n=52 ) comprised r and omly selected patients presenting at a respiratory clinic . Of these , a sub-group ( n=12 ) was subsequently diagnosed with lung cancer . An age-matched group ( n=12 ) corresponding to the lung cancer group was taken from a larger control group of healthy adults ( n=58 ) . The concentration of ethane in a single exhaled breath sample collected from all subjects was later measured using the TDLS . This technique is capable of real-time analysis of sample s with accuracy 0.1 parts per billion ( ppb ) , over 10 times less than typical ambient levels in the northern hemisphere . After correcting for ambient background , ethane in the control group ( 26 % smokers ) ranged from 0 to 10.54 ppb ( median of 1.9 ppb ) while ethane in the lung cancer patients ( 42 % smokers ) ranged from 0 to 7.6 ppb ( median of 0.7 ppb ) . Ethane among the non-lung cancer patients presenting for investigation of respiratory disease ranged from 0 to 25 ppb ( median 1.45 ppb ) . We conclude that , while the TDLS proved effective for accurate and rapid sample analysis , there was no significant difference in exhaled ethane among any of the subject groups . Comments are made on the suitability of the technique for monitoring applications BACKGROUND Many volatile organic compounds ( VOCs ) , principally alkanes and benzene derivatives , have been identified in breath from patients with lung cancer . We investigated whether a combination of VOCs could identify such patients . METHODS We collected breath sample s from 108 patients with an abnormal chest radiograph who were scheduled for bronchoscopy .  The sample s were collected with a portable apparatus , then assayed by gas chromatography and mass spectroscopy . The alveolar gradient of each breath VOC , the difference between the amount in breath and in air , was calculated . Forward stepwise discriminant analysis was used to identify VOCs that discriminated between patients with and without lung cancer . FINDINGS Lung cancer was confirmed histologically in 60 patients . A combination of 22 breath VOCs , predominantly alkanes , alkane derivatives , and benzene derivatives , discriminated between patients with and without lung cancer , regardless of stage ( all p<0.0003 ) . For stage 1 lung cancer , the 22 VOCs had 100 % sensitivity and 81.3 % specificity . Cross-validation of the combination correctly predicted the diagnosis in 71.7 % patients with lung cancer and 66.7 % of those without lung cancer . INTERPRETATION In patients with an abnormal chest radiograph , a combination of 22 VOCs in breath sample s distinguished between patients with and without lung cancer . Prospect i ve studies are needed to confirm the usefulness of breath VOCs for detecting lung cancer in the general population Objectives : Ventilator-associated pneumonia ( VAP ) is a frequent complication in patients in surgical intensive care units . Pneumonia scores , chest radiography , and bronchoscopy are all employed , but there is no gold st and ard test for the diagnosis of VAP . The electronic nose , a sensor of volatile molecules , is well suited to testing the breath of mechanically ventilated patients . Our objective was to determine the potential use of an electronic nose as a diagnostic adjunct in the detection of VAP . Methods : We performed a prospect i ve study of mechanically ventilated patients in a surgical intensive care unit . Clinical data , including temperature , white blood cell count , character and quantity of tracheal secretions , ratio of partial pressure of arterial oxygen to fraction of inspired oxygen , and chest radiographs , were collected , and a pneumonia score between 0 and 10 was calculated . Exhaled gas was sample d from the expiratory limb of the ventilator circuit . The gases were assayed with a commercially available electronic nose . Multidimensional data reduction analysis was used to analyze the results . Results : Forty-four patients were studied . Fifteen patients had pneumonia scores of 7 or greater , and 29 patients had scores of 6 or less . With Fisher discriminant analysis and K — nearest neighbor analysis , the electronic nose was able to discriminate between the two groups . Conclusions : The electronic nose is a new technology that is inexpensive , noninvasive , and portable . We demonstrate its ability to predict pneumonia , based on a well-recognized scoring system . This technology promises to serve as a diagnostic adjunct in the management of VAP Background Non-invasive diagnostic strategies aim ed at identifying biomarkers of lung cancer are of great interest for early cancer detection . The aim of this study was to set up a new method for identifying and quantifying  volatile organic compounds ( VOCs ) in exhaled air of patients with non-small cells lung cancer ( NSCLC ) , by comparing the levels with those obtained from healthy smokers and non-smokers , and patients with chronic obstructive pulmonary disease . The VOC collection and analyses were repeated three weeks after the NSCLC patients underwent lung surgery . Methods The subjects ' breath was collected in a Teflon ® bulb that traps the last portion of single slow vital capacity . The 13 VOCs selected for this study were concentrated using a solid phase micro extraction technique and subsequently analysed by means of gas cromatography/mass spectrometry . Results The levels of the selected VOCs ranged from 10 - 12 M for styrene to 10 - 9 M for isoprene . None of VOCs alone discriminated the study groups , and so it was not possible to identify one single chemical compound as a specific lung cancer biomarker . However , multinomial logistic regression analysis showed that VOC profile can correctly classify about 80 % of cases . Only isoprene and decane levels significantly decreased after surgery . Conclusion As the combination of the 13 VOCs allowed the correct classification of the cases into groups , together with conventional diagnostic approaches , VOC analysis could be used as a complementary test for the early diagnosis of lung cancer . Its possible use in the follow-up of operated patients can not be recommended on the basis of the results of our short-term nested study Background : The authors performed a prospect i ve study to determine whether breath test analysis using an electronic nose correlates with a clinical pneumonia score . Methods : Exhaled gas was sample d from the expiratory limb of the ventilator in mechanically ventilated surgical intensive care patients and assayed with the electronic nose . Components of a clinical pneumonia score were recorded concurrently . Results : The score predicted by the electronic nose showed good correlation with the actual pneumonia score ( r2 = 0.81 ) . Bl and Altman analysis showed a mean bias of 0.0 ( limits ± 2.6 ) . Conclusions : The electronic nose is a new biosensor technology that correlates with a clinical pneumonia score RATIONALE Electronic noses are successfully used in commercial applications , including detection and analysis of volatile organic compounds in the food industry . OBJECTIVES We hypothesized that the electronic nose could identify and discriminate between lung diseases , especially bronchogenic carcinoma . METHODS In a discovery and training phase , exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed . Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer . Discrimination between classes was performed using Mahalanobis distance . Support vector machine analysis was used to create and apply a cancer prediction model prospect ively in a separate group of 76 individuals , 14 with and 62 without cancer . MAIN RESULTS Principal components and canonic discriminant analysis demonstrated discrimination between sample s from patients with lung cancer and those from other groups . In the validation study , the electronic nose had 71.4 % sensitivity and 91.9 % specificity for detecting lung cancer ; positive and negative predictive values were 66.6 and 93.4 % , respectively . In this population with a lung cancer prevalence of 18 % , positive and negative predictive values were 66.6 and 94.5 % , respectively . CONCLUSION The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose . The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer OBJECTIVES /HYPOTHESIS The electronic nose is a sensor of volatile molecules that is useful in the analysis of expired gases . The device is well suited to testing the breath of patients receiving mechanical ventilation and is a potential diagnostic adjunct that can aid in the detection of patients with ventilator-associated pneumonia . STUDY DESIGN A prospect i ve study . METHODS We performed a prospect i ve study of patients receiving mechanical ventilation in a surgical intensive care unit who underwent chest computed tomography ( CT ) scanning .  A single attending radiologist review ed the chest CT scans , and imaging features were recorded on a st and ardized form . Within 48 hours of chest CT scan , five sets of exhaled gas were sample d from the expiratory limb of the ventilator circuit . The gases were assayed with a commercially available electronic nose . Both linear and nonlinear analyses were performed to identify correlations between imaging features and the assayed gas signatures . RESULTS Twenty-five patients were identified , 13 of whom were diagnosed with pneumonia by CT scan .  Support vector machine analysis was performed in two separate analyses . In the first analysis , in which a training set was identical to a prediction set , the accuracy of prediction results was greater than 91.6 % . In the second analysis , in which the training set and the prediction set were different , the accuracy of prediction results was at least 80 % , with higher accuracy depending on the specific parameters and models being used . CONCLUSION The electronic nose is a new technology that continues to show promise as a potential diagnostic adjunct in the diagnosis of pneumonia and other infectious diseases BACKGROUND Normal metabolism generates several volatile organic compounds ( VOCs ) that are excreted in the breath ( e.g. alkanes ) . In patients with lung cancer , induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs , so that their altered abundance in breath may provide biomarkers of lung cancer . METHODS VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT . Subjects were r and omly assigned to a training set or to a prediction set in a 2:1 split . A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer . RESULTS Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group ( p<0.0001 in all TNM stages ) . The model predicted primary lung cancer with 84.6 % sensitivity , 80.0 % specificity , and 0.88 area under curve ( AUC ) of the receiver operating characteristic ( ROC ) curve . Predictive accuracy was similar in TNM stages 1 through 4 , and was not affected by current or former tobacco smoking . The predictive model achieved near-maximal performance with six breath VOCs , and was progressively de grade d by r and om classifiers . Predictions with fuzzy logic were consistently superior to multilinear analysis . If applied to a population with 2 % prevalence of lung cancer , a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 ( true positive rate = 0.277 , false positive rate = 0.029 ) . CONCLUSIONS A two-minute breath test predicted lung cancer with accuracy comparable to screening CT of chest . The accuracy of the test was not affected by TNM stage of disease or tobacco smoking . Alterations in breath VOCs in lung cancer were consistent with a non-linear pathophysiologic process , such as an off-on switch controlling high-risk cytochrome p450 activity . Further research is needed to determine if detection of lung cancer with this test will reduce mortality Background Breath volatile organic compounds ( VOCs ) may be useful for asthma diagnosis and phenotyping , identifying patients who could benefit from personalised therapeutic strategies . The authors aim ed to identify specific patterns of breath VOCs  in patients with asthma and in clinical ly relevant disease phenotypes . Methods Breath sample s were analysed by gas chromatography – mass spectrometry . The Asthma Control Question naire was completed , together with lung function and induced sputum cell counts . Breath data were reduced to principal components , and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis , sputum inflammatory cell profile and asthma control . Results The authors recruited 35 patients with asthma and 23 matched controls . A model derived from 15 VOCs classified patients with asthma with an accuracy of 86 % , and positive and negative predictive values of 0.85 and 0.89 , respectively . Models also classified patients with asthma based on the following phenotypes : sputum ( obtained in 18 patients with asthma ) eosinophilia ≥2 % area under the receiver operating characteristics ( AUROC ) curve 0.98 , neutrophilia ≥40 % AUROC 0.90 and uncontrolled asthma ( Asthma Control Question naire ≥1 ) AUROC 0.96 . Conclusions Detection of characteristic breath VOC profiles could classify patients with asthma versus controls , and clinical ly relevant disease phenotypes based on sputum inflammatory profile and asthma control . Prospect i ve validation of these models may lead to clinical application of non-invasive breath profiling in asthma Introduction : The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer . Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature . Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature . Objectives : To develop an exhaled breath biosignature  of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors . Methods : The exhaled breath of 229 study subjects , 92 with lung cancer and 137 controls , was drawn across a colorimetric sensor array . Logistic prediction models were developed and statistically vali date d based on the color changes of the sensor . Age , sex , smoking history , and chronic obstructive pulmonary disease were incorporated in the prediction models . Results : The vali date d prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects ( C-statistic 0.811 ) . The accuracy improved when the model focused on only one histology ( C-statistic 0.825–0.890 ) . Individuals with different histologies could be accurately distinguished from one another ( C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma ) . Moderate accuracies were noted for vali date d breath biosignatures of stage and survival ( C-statistic 0.785 and 0.693 , respectively ) . Conclusions : A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer . The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors BACKGROUND A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone . We developed a non-linear method of multivariate analysis , weighted digital analysis ( WDA ) , and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath . METHODS WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight , a cutoff value , and a sign for each predictor variable employed in the model . The weight of each predictor variable was the area under the curve ( AUC ) of the receiver operating characteristic ( ROC ) curve minus a fixed offset of 0.55 , where the AUC was obtained by employing that predictor variable alone , as the sole marker of disease . The sign ( + /- ) was used to invert the predictor variable if a lower value indicated a higher probability of disease . When employed to predict the presence of a disease in a particular patient , the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values . The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment . We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer , comprising the volatile organic compounds ( VOCs ) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT . RESULTS The WDA discriminant function accurately identified patients with lung cancer  in a model employing 30 breath VOCs ( ROC curve AUC=0.90 ; sensitivity=84.5 % , specificity=81.0 % ) . These results were superior to multilinear regression analysis of the same data set ( AUC=0.74 , sensitivity=68.4 , specificity=73.5 % ) . WDA test accuracy did not vary appreciably with TNM ( tumor , node , metastasis ) stage of disease , and results were not affected by tobacco smoking ( ROC curve AUC=0.92 in current smokers , 0.90 in former smokers ) . WDA was a robust predictor of lung cancer : r and om removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by > 10 % ( 99.7 % CI ) . CONCLUSIONS A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography . The algorithm identified dependencies that were not apparent with traditional linear methods . WDA appears to provide a useful new technique for non-linear multivariate analysis of data RATIONALE Volatile organic compounds ( VOCs ) in breath provide biomarkers of tuberculosis ( TB ) because Mycobacterium tuberculosis manufactures VOC metabolites that are detectable in the breath of infected patients . OBJECTIVES We evaluated  breath VOC biomarkers in subjects with active pulmonary TB , using an internet-linked rapid point-of-care breath test . METHODS 279 subjects were studied at four centers in three countries , Philippines , UK , and India , and data was analyzed from 251 ( 130 active pulmonary TB , 121 controls ) . A point-of-care system collected and concentrated breath and air VOCs , and analyzed them with automated thermal desorption , gas chromatography , and surface acoustic wave detection . A breath test was completed in 6 min . Chromatograms were converted to a series of Kovats Index ( KI ) windows , and biomarkers of active pulmonary TB were identified by Monte Carlo analysis of KI window alveolar gradients ( abundance in breath minus abundance in room air ) . MEASUREMENTS AND MAIN RESULTS Multiple Monte Carlo simulations identified eight KI windows as biomarkers with better than r and om performance . Four KI windows corresponded with KI values of VOCs previously identified as biomarkers of pulmonary TB and metabolic products of M. tuberculosis , principally derivatives of naphthalene , benzene and alkanes . A multivariate predictive algorithm identified active pulmonary TB with 80 % accuracy ( area under curve of receiver operating characteristic curve ) , sensitivity=71.2 % , and specificity = 72 % . Accuracy increased to 84 % in age-matched subgroups . In a population with 5 % prevalence , the breath test would identify active pulmonary TB with 98 % negative predictive value and 13 % positive predictive value . CONCLUSIONS A six-minute point-of-care breath test for volatile biomarkers accurately identified subjects with active pulmonary TB BACKGROUND Malignant Pleural Mesothelioma ( MPM ) is a tumour of the surface cells of the pleura that is highly aggressive and mainly caused by asbestos exposure . Electronic noses capture the spectrum of exhaled volatile organic compounds ( VOCs ) providing a composite biomarker profile ( breathprint ) . OBJECTIVE We tested the hypothesis that an electronic nose can discriminate exhaled air of patients with MPM from subjects with a similar long-term professional exposure to asbestos without MPM and from healthy controls . METHODS 13 patients with a histology confirmed diagnosis of MPM ( age 60.9±12.2 year ) , 13 subjects with certified , long-term professional asbestos exposure ( age 67.2±9.8 ) , and 13 healthy subjects without asbestos exposure ( age 52.2±16.2 ) participated in a cross-sectional study . Exhaled breath  was collected by a previously described method and sample d by an electronic nose ( Cyranose 320 ) . Breathprints were analyzed by canonical discriminant analysis on principal component reduction . Cross-vali date d accuracy ( CVA ) was calculated . RESULTS Breathprints from patients with MPM were separated from subjects with asbestos exposure ( CVA : 80.8 % , sensitivity 92.3 % , specificity 85.7 % ) . MPM was also distinguished from healthy controls ( CVA : 84.6 % ) . Repeated measurements confirmed these results . CONCLUSIONS Molecular pattern recognition of exhaled breath can correctly distinguish patients with MPM from subjects with similar occupational asbestos exposure without MPM and from healthy controls . This suggests that breathprints obtained by electronic nose have diagnostic potential for MPM
There is an increasing interest in the potential of exhaled biomarkers , such as volatile organic compounds ( VOCs ) , to improve accurate diagnoses and management decisions in pulmonary diseases .
The objective of this manuscript is to systematic ally review the current knowledge on exhaled VOCs with respect to their potential clinical use in asthma , lung cancer , chronic obstructive pulmonary disease ( COPD ) , cystic fibrosis ( CF ) , and respiratory tract infections .
The collection and analysis of exhaled VOCs is non-invasive and could be easily applied in the broad range of patients , including subjects with severe disease and children .
Pulmonary diseases seem to be characterized by a disease specific breath-print , as distinct profiles were found in patients with dissimilar diseases .