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J Thorac Oncol. 2012 Jan;7(1):137-42. doi: 10.1097/JTO.0b013e318233d80f.

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

Author information

1
Respiratory Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. mazzonp@ccf.org

Abstract

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 validated 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 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).

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.

PMID:
22071780
PMCID:
PMC3241851
DOI:
10.1097/JTO.0b013e318233d80f
[Indexed for MEDLINE]
Free PMC Article

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