Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer

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 br...

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Vydané v:	Journal of thoracic oncology Ročník 7; číslo 1; s. 137
Hlavní autori:	Mazzone, Peter J, Wang, Xiao-Feng, Xu, Yaomin, Mekhail, Tarek, Beukemann, Mary C, Na, Jie, Kemling, Jonathan W, Suslick, Kenneth S, Sasidhar, Madhu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.01.2012
Predmet:	Adenocarcinoma - diagnosis Adenocarcinoma - pathology Adult Aged Breath Tests Carcinoma, Non-Small-Cell Lung - diagnosis Carcinoma, Non-Small-Cell Lung - pathology Carcinoma, Small Cell - diagnosis Carcinoma, Small Cell - pathology Carcinoma, Squamous Cell - diagnosis Carcinoma, Squamous Cell - pathology Colorimetry Humans Logistic Models Lung Neoplasms - diagnosis Lung Neoplasms - pathology Middle Aged Neoplasm Staging Predictive Value of Tests Prospective Studies ROC Curve
ISSN:	1556-1380, 1556-1380
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Abstract	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. 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. 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 validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models. The validated 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 validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively). 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.
AbstractList	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.INTRODUCTIONThe 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.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.OBJECTIVESTo 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.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 validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models.METHODSThe 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 validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models.The validated 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 validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively).RESULTSThe validated 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 validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively).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.CONCLUSIONSA 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. 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. 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. 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 validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models. The validated 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 validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively). 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.
Author	Beukemann, Mary C Mazzone, Peter J Xu, Yaomin Mekhail, Tarek Na, Jie Kemling, Jonathan W Suslick, Kenneth S Sasidhar, Madhu Wang, Xiao-Feng
Author_xml	– sequence: 1 givenname: Peter J surname: Mazzone fullname: Mazzone, Peter J email: mazzonp@ccf.org organization: Respiratory Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. mazzonp@ccf.org – sequence: 2 givenname: Xiao-Feng surname: Wang fullname: Wang, Xiao-Feng – sequence: 3 givenname: Yaomin surname: Xu fullname: Xu, Yaomin – sequence: 4 givenname: Tarek surname: Mekhail fullname: Mekhail, Tarek – sequence: 5 givenname: Mary C surname: Beukemann fullname: Beukemann, Mary C – sequence: 6 givenname: Jie surname: Na fullname: Na, Jie – sequence: 7 givenname: Jonathan W surname: Kemling fullname: Kemling, Jonathan W – sequence: 8 givenname: Kenneth S surname: Suslick fullname: Suslick, Kenneth S – sequence: 9 givenname: Madhu surname: Sasidhar fullname: Sasidhar, Madhu
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/22071780$$D View this record in MEDLINE/PubMed
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SubjectTerms	Adenocarcinoma - diagnosis Adenocarcinoma - pathology Adult Aged Breath Tests Carcinoma, Non-Small-Cell Lung - diagnosis Carcinoma, Non-Small-Cell Lung - pathology Carcinoma, Small Cell - diagnosis Carcinoma, Small Cell - pathology Carcinoma, Squamous Cell - diagnosis Carcinoma, Squamous Cell - pathology Colorimetry Humans Logistic Models Lung Neoplasms - diagnosis Lung Neoplasms - pathology Middle Aged Neoplasm Staging Predictive Value of Tests Prospective Studies ROC Curve
Title	Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer
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