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

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

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

Box-and-whisker plots showing the IMS signal intensity of peak-2 in adenocarcinoma patients positive and negative for EGFR.

<p>Fourteen patients with EGFR mutation displayed a significantly higher n-Dodecane peak with a sensitivity of 85.7% and a specificity of 78.6% (p<0.01) than in 14 adenocarcinoma patients without the EGFR mutation.</p