Exhaled breath analysis for XXXX detection using ion mobility spectrometry.

@entity1 @entity4153 @entity470 @entity957 @entity2 @entity18105 @entity420
BACKGROUND: Conventional methods for @entity2 detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal @entity2 detection technique. METHODS: The exhaled breath of 50 @entity1 with @entity2 histologically proven by bronchoscopic biopsy samples (32 @entity420 , 10 @entity957 , 8 @entity4153 ), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared @entity420 @entity1 with and without @entity470 ( @entity470 ) mutation. RESULTS: A decision tree algorithm could separate @entity1 with @entity2 including @entity420 , @entity957 and @entity4153 . One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n- @entity18105 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 @entity18105 , a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between @entity420 and healthy subjects, n- @entity18105 was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen @entity1 positive for @entity470 mutation displayed a significantly higher n- @entity18105 than for the 14 @entity1 negative for @entity470 (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 @entity2 . Moreover, n- @entity18105 analysis from @entity420 @entity1 might be useful to discriminate the @entity470 mutation.
