Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose

• Published in: IEEE transactions on biomedical engineering Vol. 62; no. 8; pp. 2044 - 2054
• Main Authors: McWilliams, Annette, Beigi, Parmida, Srinidhi, Akhila, Lam, Stephen, MacAulay, Calum E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2015
• Subjects: Accuracy; Aged; Arrays; Biomarkers - analysis; Biomedical measurement; Breath analysis; Breath Tests - methods; Cancer; Early Detection of Cancer; Electronic Nose; Female; Humans; lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - metabolism; Lungs; Male; Middle Aged; pattern recognition; Pattern Recognition, Automated; Predictive models; Reproducibility of Results; sensor array; Smoking - metabolism; Training; Volatile Organic Compounds; Volatile Organic Compounds - analysis; lung cancer; electronic nose (e-nose); volatile organic compounds (VOCs); Breath analysis; sensor array; pattern recognition
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2015.2409092

Objective: Volatile organic compounds (VOCs) in exhaled breath as measured by electronic nose (e-nose) have utility as biomarkers to detect subjects at risk of having lung cancer in a screening setting. We hypothesize that breath analysis using an e-nose chemo-resistive sensor array could be used as a screening tool to discriminate patients diagnosed with lung cancer from high-risk smokers. Methods: Breath samples from 191 subjects-25 lung cancer patients and 166 high-risk smoker control subjects without cancer-were analyzed. For clinical relevancy, subjects in both groups were matched for age, sex, and smoking histories. Classification and regression trees and discriminant functions classifiers were used to recognize VOC patterns in e-nose data. Cross-validated results were used to assess classification accuracy. Repeatability and reproducibility of e-nose data were assessed by measuring subject-exhaled breath in parallel across two e-nose devices. Results : e-Nose measurements could distinguish lung cancer patients from high-risk control subjects, with a better than 80% classification accuracy. Subject sex and smoking status impacted classification as area under the curve results (ex-smoker males 0.846, ex-smoker female 0.816, current smoker male 0.745, and current smoker female 0.725) demonstrated. Two e-nose systems could be calibrated to give equivalent readings across subject-exhaled breath measured in parallel. Conclusions: e-Nose technology may have significant utility as a noninvasive screening tool for detecting individuals at increased risk for lung cancer. Significance: The results presented further the case that VOC patterns could have real clinical utility to screen for lung cancer in the important growing ex-smoker population.