Diagnosis of head-and-neck cancer from exhaled breath Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study Exhaled Breath Analysis for Lung Cancer Detection Using Ion Mobility Spectrometry Exhaled volatile organic compounds in patients with non-small cell lung cancer: cross sectional and nested short-term follow-up study Detection of lung cancer by sensor array analyses of exhaled breath. Prediction of lung cancer using volatile biomarkers in breath. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions Exhaled Breath Analysis with a Colorimetric Sensor Array for the Identification and Characterization of Lung Cancer Volatile biomarkers in the breath of women with breast cancer. Detection of lung cancer using weighted digital analysis of breath biomarkers. Prediction of breast cancer using volatile biomarkers in the breath Volatile organic compounds (VOCs) in exhaled breath of patients with breast cancer in a clinical setting. Classification of breast cancer precursors through exhaled breath Breath Analysis by Mass Spectrometry: A New Tool for Breast Cancer Detection? Mass Spectrometric Analysis of Exhaled Breath for the Identification of Volatile Organic Compound Biomarkers in Esophageal and Gastric Adenocarcinoma Non-invasive Breath Analysis of Pulmonary Nodules An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls. Detection of precancerous gastric lesions and gastric cancer through exhaled breath Combined sputum hypermethylation and eNose analysis for lung cancer diagnosis
Background : Head- and -neck cancer ( HNC ) is the eighth most common malignancy worldwide . It is often diagnosed late due to a lack of screening methods and overall cure is achieved in < 50 % of patients . Head- and -neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract , mostly HNC or lung cancer ( LC ) , making lifelong follow-up necessary . Methods : Alveolar breath was collected from 87 volunteers ( HNC and LC patients and healthy controls ) in a cross-sectional clinical trial . The discriminative power of a tailor-made Nanoscale Artificial Nose ( NA-NOSE ) based on an array of five gold nanoparticle sensors was tested , using 62 breath sample s. The NA-NOSE signals were analysed to detect statistically significant differences between the sub- population s using ( i ) principal component analysis with ANOVA and Student 's t-test and ( ii ) support vector machines and cross-validation . The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry ( GC – MS ) , using 40 breath sample s. Results : The NA-NOSE could clearly distinguish between ( i ) HNC patients and healthy controls , ( ii ) LC patients and healthy controls , and ( iii ) HNC and LC patients . The GC – MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups . Conclusion : The presented results could lead to the development of a cost-effective , fast , and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE , with a future potential as screening tool 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 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 Background Conventional methods for lung cancer detection including computed tomography ( CT ) and bronchoscopy are expensive and invasive . Thus , there is still a need for an optimal lung cancer detection technique . Methods The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy sample s ( 32 adenocarcinomas , 10 squamous cell carcinomas , 8 small cell carcinomas ) , were analyzed using ion mobility spectrometry ( IMS ) and compared with 39 healthy volunteers . As a secondary assessment , we compared adenocarcinoma patients with and without epidermal growth factor receptor ( EGFR ) mutation . Results A decision tree algorithm could separate patients with lung cancer including adenocarcinoma , squamous cell carcinoma and small cell carcinoma . One hundred-fifteen separated volatile organic compound ( VOC ) peaks were analyzed . Peak-2 noted as n-Dodecane using the IMS data base was able to separate values with a sensitivity of 70.0 % and a specificity of 89.7 % . Incorporating a decision tree algorithm starting with n-Dodecane , a sensitivity of 76 % and specificity of 100 % was achieved . Comparing VOC peaks between adenocarcinoma and healthy subjects , n-Dodecane was able to separate values with a sensitivity of 81.3 % and a specificity of 89.7 % . Fourteen patients positive for EGFR mutation  displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR ( p<0.01 ) , with a sensitivity of 85.7 % and a specificity of 78.6 % . Conclusion In this prospect i ve study , VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer . Moreover , n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation 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 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 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 : Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the st and ard method for diagnosing gastric cancer ( GC ) . However , this procedure may not be widely available for screening in the developing world , whereas in developed countries endoscopy is frequently used without major clinical gain . There is a high dem and for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination . Here , we studied the feasibility of a nano material -based breath test for identifying GC among patients with gastric complaints . Methods : Alveolar exhaled breath sample s from 130 patients with gastric complaints ( 37 GC/32 ulcers / 61 less severe conditions ) that underwent endoscopy/biopsy  were analyzed using nano material -based sensors . Predictive models were built employing discriminant factor analysis ( DFA ) pattern recognition , and their stability against possible confounding factors ( alcohol/tobacco consumption ; Helicobacter pylori ) was tested . Classification success was determined ( i ) using leave-one-out cross-validation and ( ii ) by r and omly blinding 25 % of the sample s as a validation set . Complementary chemical analysis of the breath sample s was performed using gas chromatography coupled with mass spectrometry . Results : Three DFA models were developed that achieved excellent discrimination between the sub population s : ( i ) GC vs benign gastric conditions , among all the patients ( 89 % sensitivity ; 90 % specificity ) ; ( ii ) early stage GC ( I and II ) vs late stage ( III and IV ) , among GC patients ( 89 % sensitivity ; 94 % specificity ) ; and ( iii ) ulcer vs less severe , among benign conditions ( 84 % sensitivity ; 87 % specificity ) . The models were insensitive against the tested confounding factors . Chemical analysis found that five volatile organic compounds ( 2-propenenitrile , 2-butoxy-ethanol , furfural , 6-methyl-5-hepten-2-one and isoprene ) were significantly elevated in patients with GC and /or peptic ulcer , as compared with less severe gastric conditions . The concentrations both in the room air and in the breath sample s were in the single p.p.b.v range , except in the case of isoprene . Conclusion : The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases . It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses . Although this pilot study does not allow drawing far-reaching conclusions , the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions 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 We sought biomarkers of breast cancer in the breath because the disease is accompanied by increased oxidative stress and induction of cytochrome P450 enzymes , both of which generate volatile organic compounds ( VOCs ) that are excreted in breath . We analyzed breath VOCs in 54 women with biopsy-proven breast cancer and 204 cancer-free controls , using gas chromatography/mass spectroscopy .  Chromatograms were converted into a series of data points by segmenting them into 900 time slices ( 8 s duration , 4 s overlap ) and determining their alveolar gradients ( abundance in breath minus abundance in ambient room air ) . Monte Carlo simulations identified time slices with better than r and om accuracy as biomarkers of breast cancer by excluding r and om identifiers . Patients were r and omly allocated to training sets or test sets in 2:1 data splits . In the training sets , time slices were ranked according their C-statistic values ( area under curve of receiver operating characteristic ) , and the top ten time slices were combined in multivariate algorithms that were cross-vali date d in the test sets . Monte Carlo simulations identified an excess of correct over r and om time slices , consistent with non-r and om biomarkers of breast cancer in the breath . The outcomes of ten r and om data splits ( mean ( st and ard deviation ) ) in the training sets were sensitivity = 78.5 % ( 6.14 ) , specificity = 88.3 % ( 5.47 ) , C-statistic = 0.89 ( 0.03 ) and in the test sets , sensitivity = 75.3 % ( 7.22 ) , specificity = 84.8 ( 9.97 ) , C-statistic = 0.83 ( 0.06 ) . A breath test identified women with breast cancer , employing a combination of volatile biomarkers in a multivariate algorithm 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 Summary We evaluated a breath test for volatile organic compounds ( VOCs ) as a predictor of  breast cancer .  Breath VOCs were assayed in 51 asymptomatic women with abnormal mammograms and biopsy-proven breast cancer , and 42 age-matched healthy women . A fuzzy logic model predicted breast cancer with accuracy superior to previously reported findings . Following r and om assignment to a training set ( 64 ) or a prediction set ( 29 ) , a model was constructed in the training set employing five breath VOCs that predicted breast cancer in the prediction set with 93.8 % sensitivity and 84.6 % specificity . The same model predicted no breast cancer in 16/50 ( 32.0 % ) women with abnormal mammograms and no cancer on biopsy . A two-minute breath test could potentially provide a safe , accurate and painless screening test for breast cancer , but prospect i ve validation studies are required BACKGROUND Carcinogenic products in the exhaled breath of cancer patients are of growing medical interest as they can serve as noninvasive disease markers . Breath analysis can be used as an alternative or complementary diagnostic tool in breast cancer patients who have a different pattern of chemical composition in their breath . This study aims to verify the existence of specific volatile organic compounds ( VOCs ) in the breath of breast cancer patients . METHODS This prospect i ve study included ten patients suffering from breast cancer and ten healthy pair-matched women .  Breath sample s of each member of the two respective groups were taken and scanned by gas chromatography/mass spectometry for the presence of volatile organic compounds such as alkanes , ketones , halogenated hydrocarbon , aldehydes , and esters . RESULTS The spectrum of VOCs differed significantly within the two groups . Five specific VOCs could be identified as typical discriminatory markers in the breath sample s. Four VOCs were elevated in the healthy controls , one specific VOC was found to be elevated in women affected by breast cancer CONCLUSIONS This pilot study revealed a specific VOC pattern using gas chromatography in the breath of breast cancer patients . Five specific breast cancer-VOCs were identified . At relatively low cost the identification of VOCs may be used to detect breast cancer Certain benign breast diseases are considered to be precursors of invasive breast cancer . Currently available techniques for diagnosing benign breast conditions lack accuracy . The purpose of this study was to deliver a proof-of-concept for a novel method that is based on breath testing to identify breast cancer precursors . Within this context , the authors explored the possibility of using exhaled alveolar breath to identify and distinguish between benign breast conditions , malignant lesions , and healthy states , using a small-scale , case-controlled , cross-sectional clinical trial . Breath sample s were collected from 36 volunteers and were analyzed using a tailor-made nanoscale artificial NOSE ( NA-NOSE ) . The NA-NOSE signals were analyzed using two independent methods : ( i ) principal component analysis , ANOVA and Student ’s t-test and ( ii ) support vector machine analysis to detect statistically significant differences between the sub- population s. The NA-NOSE could distinguish between all studied test population s. Breath testing with a NA-NOSE holds future potential as a cost-effective , fast , and reliable diagnostic test for breast cancer risk factors and precursors , with possible future potential as screening method Breath analysis has received attention as a noninvasive diagnostic tool with increasing research into its potential usefulness . We are investigating the utility of the analysis of breath volatile organic compounds ( VOCs ) as an effective modality for breast cancer ( BC ) detection and monitoring by collecting breath sample s with a simple portable device to determine whether BC patients have breath VOCs distinct from those in healthy volunteers . We prospect ively enrolled 20 healthy volunteers and 20 newly diagnosed stage II-IV BC patients . The study subjects deeply exhaled into a commercially available Teflon/valved breath sample r equipped with a rapid passive diffusive sample r five times at 5-minute intervals trapping alveolar breath VOCs . The exhaled breath sample s were analyzed by thermal desorption/gas chromatography/mass spectrometry monitoring 383 VOCs in the breath of both population s. Our results indicate that aggregate low-dimensional summaries and compound quantities result in specific patterns that can confirm BC . We found a definite clustering of the presence of BC from cancer-free points . Overall sensitivity was 72 per cent and specificity was 64 per cent result ing in a correct classification rate of approximately 77 per cent . Our data show promising evidence that BC patients can be differentiated from healthy volunteers through distinct breath VOCs Objective : The present study assessed whether exhaled breath analysis using Selected Ion Flow Tube Mass Spectrometry could distinguish esophageal and gastric adenocarcinoma from noncancer controls . Background : The majority of patients with upper gastrointestinal cancer present with advanced disease , result ing in poor long-term survival rates . Novel methods are needed to diagnose potentially curable upper gastrointestinal malignancies . Methods : A Profile-3 Selected Ion Flow Tube Mass Spectrometry instrument was used for analysis of volatile organic compounds  ( VOCs ) within exhaled breath sample s. All study participants had undergone upper gastrointestinal endoscopy on the day of breath sampling . Receiver operating characteristic analysis and a diagnostic risk prediction model were used to assess the discriminatory accuracy of the identified VOCs . Results : Exhaled breath sample s were analyzed from 81 patients with esophageal ( N = 48 ) or gastric adenocarcinoma ( N = 33 ) and 129 controls including Barrett 's metaplasia ( N = 16 ) , benign upper gastrointestinal diseases ( N = 62 ) , or a normal upper gastrointestinal tract ( N = 51 ) . Twelve VOCs — pentanoic acid , hexanoic acid , phenol , methyl phenol , ethyl phenol , butanal , pentanal , hexanal , heptanal , octanal , nonanal , and decanal — were present at significantly higher concentrations ( P < 0.05 ) in the cancer groups than in the noncancer controls . The area under the ROC curve using these significant VOCs to discriminate esophageal and gastric adenocarcinoma from those with normal upper gastrointestinal tracts was 0.97 and 0.98 , respectively . The area under the ROC curve for the model and validation subsets of the diagnostic prediction model was 0.92 ± 0.01 and 0.87 ± 0.03 , respectively . Conclusions : Distinct exhaled breath VOC  profiles can distinguish patients with esophageal and gastric adenocarcinoma from noncancer controls Introduction : The search for non-invasive diagnostic methods of lung cancer ( LC ) has led to new avenues of research , including the exploration of the exhaled breath . Previous studies have shown that LC can , in principle , be detected through exhaled-breath analysis . This study evaluated the potential of exhaled-breath analysis for the distinction of benign and malignant pulmonary nodules ( PNs ) . Methods : Breath sample s were taken from 72 patients with PNs in a prospect i ve trial . Profiles of volatile organic compounds were determined by ( 1 ) gas chromatography/mass spectrometry ( GC-MS ) combined with solid-phase micro extraction and ( 2 ) a chemical nanoarray . Results : Fifty-three PNs were malignant and 19 were benign with similar smoking histories and comorbidities .  Nodule size ( mean ± SD ) was 2.7 ± 1.7 versus 1.6 ± 1.3 cm ( p = 0.004 ) , respectively . Within the malignant group , 47 were non – small-cell lung cancer and six were small-cell lung cancer . Thirty patients had early-stage disease and 23 had advanced disease . Gas chromatography/mass spectrometry analysis identified a significantly higher concentration of 1-octene in the breath of LC , and the nanoarray distinguished significantly between benign versus malignant PNs ( p < 0.0001 ; accuracy 88 ± 3 % ) , between adeno- and squamous-cell carcinomas [ LINE SEPARATOR](p < 0.0001 ; 88 ± 3 % ) and between early stage and advanced disease ( p < 0.0001 ; 88 ± 2 % ) . Conclusions : In this pilot study , breath analysis discriminated benign from malignant PNs in a high-risk cohort based on LC-related volatile organic compound profiles . Furthermore , it discriminated adeno- and squamous-cell carcinoma and between early versus advanced disease . Further studies are required to vali date this noninvasive approach , using a larger cohort of patients with PNs detected by computed tomography 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 Objectives Timely detection of gastric cancer ( GC ) and the related precancerous lesions could provide a tool for decreasing both cancer mortality and incidence . Design 968 breath sample s were collected from 484 patients ( including 99 with GC ) for two different analyses . The first sample was analysed by gas chromatography linked to mass spectrometry ( GCMS ) while applying t test with multiple corrections ( p value<0.017 ) ; the second by cross-reactive nanoarrays combined with pattern recognition . For the latter , 70 % of the sample s were r and omly selected and used in the training set while the remaining 30 % constituted the validation set . The operative link on gastric intestinal metaplasia ( OLGIM ) assessment staging system was used to stratify the presence/absence and risk level of precancerous lesions . Patients with OLGIM stages III – IV were considered to be at high risk . Results According to the GCMS results , patients with cancer as well as those at high risk had distinctive breath-print compositions . Eight significant volatile organic compounds ( p value<0.017 ) were detected in exhaled breath in the different comparisons . The nanoarray analysis made it possible to discriminate between the patients with GC and the control group ( OLGIM 0–IV ) with 73 % sensitivity , 98 % specificity and 92 % accuracy . The classification sensitivity , specificity , and accuracy between the subgroups was as follows : GC versus OLGIM 0–II—97 % , 84 % and 87 % ; GC versus OLGIM III – IV—93 % , 80 % and 90 % ; but OLGIM I – II versus OLGIM III – IV and dysplasia combined—83 % , 60 % and 61 % , respectively . Conclusions Nanoarray analysis could provide the missing non-invasive screening tool for GC and related precancerous lesions as well as for surveillance of the latter . Trial registration number Clinical Trials.gov number , NCT01420588 ( 3/11/2013 ) Aims The aim of this study is to explore  DNA hypermethylation analysis in sputum and exhaled breath analysis for their complementary , non-invasive diagnostic capacity in lung cancer . Methods  Sputum sample s and exhaled breath  were prospect ively collected from 20 lung cancer patients and 31 COPD controls ( Set 1 ) . An additional 18 lung cancer patients and 8 controls only collected exhaled breath as validation set ( Set 2 ) . DNA hypermethylation of biomarkers RASSF1A , cytoglobin , APC , FAM19A4 , PHACTR3 , 3OST2 and PRDM14 was considered , and breathprints from exhaled breath sample s were created using an electronic nose ( eNose ) . Results Both DNA hypermethylation markers in sputum and eNose were independently able to distinguish lung cancer patients from controls . The combination of RASSF1A and 3OST2 hypermethylation had a sensitivity of 85 % with a specificity of 74 % . eNose had a sensitivity of 80 % with a specificity of 48 % . Sensitivity for lung cancer diagnosis increased to 100 % , when RASSF1A hypermethylation was combined with eNose , with specificity of 42 % . Both methods showed to be complementary to each other ( p≤0.011 ) . eNose results were reproducible in Set 2 . Conclusions When used in concert , RASSF1A hypermethylation in sputum and exhaled breath analysis are complementary for lung cancer diagnosis , with 100 % sensitivity in this series . This finding should be further vali date
BACKGROUND Timely diagnosis of cancer represents a challenging task ; in particular , there is a need for reliable non-invasive screening tools that could achieve high levels of adherence at virtually no risk in population -based screening .
In this review , we summarize the current evidence of exhaled breath analysis for  cancer  detection using st and ard analysis techniques and electronic nose .