McWilliams Annette, Beigi Parmida, Srinidhi Akhila, Lam Stephen, MacAulay Calum E
IEEE Trans Biomed Eng. 2015 Aug;62(8):2044-54. doi: 10.1109/TBME.2015.2409092. Epub 2015 Mar 11.
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.
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.
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.
e-Nose technology may have significant utility as a noninvasive screening tool for detecting individuals at increased risk for lung cancer.
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.
通过电子鼻(e-nose)测量呼出气体中的挥发性有机化合物(VOCs),作为生物标志物在筛查环境中检测有患肺癌风险的受试者。我们假设使用e-nose化学电阻传感器阵列进行呼气分析可作为一种筛查工具,以区分被诊断为肺癌的患者与高危吸烟者。
分析了191名受试者的呼气样本,其中包括25名肺癌患者和166名无癌症的高危吸烟者对照受试者。为确保临床相关性,两组受试者在年龄、性别和吸烟史方面进行了匹配。使用分类回归树和判别函数分类器来识别电子鼻数据中的VOC模式。交叉验证结果用于评估分类准确性。通过在两个电子鼻设备上并行测量受试者呼出的气体,评估电子鼻数据的重复性和再现性。
电子鼻测量能够区分肺癌患者与高危对照受试者,分类准确率超过80%。受试者的性别和吸烟状态对分类有影响,曲线下面积结果表明(已戒烟男性0.846,已戒烟女性0.816,当前吸烟者男性0.745,当前吸烟者女性0.725)。可以对两个电子鼻系统进行校准,以便在并行测量的受试者呼出气体中给出等效读数。
电子鼻技术作为一种用于检测肺癌风险增加个体的非侵入性筛查工具可能具有重要作用。
所呈现的结果进一步表明,VOC模式在重要且不断增长的已戒烟人群中筛查肺癌可能具有实际临床应用价值。