Exhaled breath analysis for lung cancer detection using ion mobility spectrometry

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 samples (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 database 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 prospective 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

Box-and-whisker plots of peak-2 between healthy and lung adenocarcinoma patients.

<p>Peak 2 was significantly higher in patients with lung cancer (p<0.001). The box represents the 25th and 75th percentiles, the whiskers represent the range, and the lined box represents the median, whereas circles represent the mean. Lung adenocarcinoma patients revealed a significantly higher n-Dodecane VOC peak than healthy volunteers and the n-Dodecane VOC peak could separate values with a sensitivity of 81.3% and a specificity of 89.7%.</p

IMS chromatogram in patients with lung adenocarcinoma positive for EGFR mutation (A) and negative for EGFR mutation (B).

<p>IMS chromatogram in patients with lung adenocarcinoma positive for EGFR mutation (A) and negative for EGFR mutation (B).</p

Decision tree algorithm to discriminate between healthy and lung cancer patients.

<p>Decision tree algorithm to discriminate between healthy and lung cancer patients.</p

Characteristics of patients.

<p>Characteristics of patients.</p

Detection of VOC peaks using Visual Now database.

<p>Lung cancer vs. healthy subjects.</p><p>Detection of VOC peaks using Visual Now database.</p

Characteristics of ion mobility spectrometer (BioScout).

<p>Characteristics of ion mobility spectrometer (BioScout).</p

IMS chromatogram in a healthy volunteer.

<p>One hundred-fifteen VOC peaks were detected with ion mobility spectrometry in patients with lung cancer and healthy volunteers.</p

A decision tree algorithm could separate small cell carcinoma, squamous cell carcinoma and adenocarcinoma.

<p>A decision tree algorithm could separate small cell carcinoma, squamous cell carcinoma and adenocarcinoma.</p