human
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 
    Output:
    
agent
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 
    Output:
    