Gasparri Roberto, Santonico Marco, Valentini Claudia, Sedda Giulia, Borri Alessandro, Petrella Francesco, Maisonneuve Patrick, Pennazza Giorgio, D'Amico Arnaldo, Di Natale Corrado, Paolesse Roberto, Spaggiari Lorenzo
Division of Thoracic Surgery, European Institute of Oncology, Milan, Italy.
J Breath Res. 2016 Feb 9;10(1):016007. doi: 10.1088/1752-7155/10/1/016007.
Exhaled breath contains hundreds of volatile organic compounds (VOCs). Several independent researchers point out that the breath of lung cancer patients shows a characteristic VOC-profile which can be considered as lung cancer signature and, thus, used for diagnosis. In this regard, the analysis of exhaled breath with gas sensor arrays is a potential non-invasive, relatively low-cost and easy technique for the early detection of lung cancer. This clinical study evaluated the gas sensor array response for the identification of the exhaled breath of lung cancer patients. This study involved 146 individuals: 70 with lung cancer confirmed by computerized tomography (CT) or positron emission tomography-(PET) imaging techniques and histology (biopsy) or with clinical suspect of lung cancer and 76 healthy controls. Their exhaled breath was measured with a gas sensor array composed of a matrix of eight quartz microbalances (QMBs), each functionalized with a different metalloporphyrin. The instrument produces, for each analyzed sample, a vector of signals encoding the breath (breathprint). Breathprints were analyzed with multivariate analysis in order to correlate the sensor signals to the disease. Breathprints of the lung cancer patients were differentiated from those of the healthy controls with a sensitivity of 81% and specificity of 91%. Similar values were obtained in patients with and without metabolic comorbidities, such as diabetes, obesity and dyslipidemia (sensitivity 85%, specificity 88% and sensitivity 76%, specificity 94%, respectively). The device showed a large sensitivity to lung cancer at stage I with respect to stage II/III/IV (92% and 58% respectively). The sensitivity for stage I did not change for patients with or without metabolic comorbidities (90%, 94%, respectively). Results show that this electronic nose can discriminate the exhaled breath of the lung cancer patients from those of the healthy controls. Moreover, the largest sensitivity is observed for the subgroup of patients with a lung cancer at stage I.
呼出的气体中含有数百种挥发性有机化合物(VOCs)。几位独立研究人员指出,肺癌患者呼出的气体呈现出一种特征性的VOC谱,可被视为肺癌特征,从而用于诊断。在这方面,使用气体传感器阵列分析呼出气体是一种潜在的非侵入性、成本相对较低且简便的肺癌早期检测技术。这项临床研究评估了气体传感器阵列对肺癌患者呼出气体识别的响应。该研究涉及146名个体:70名经计算机断层扫描(CT)或正电子发射断层扫描(PET)成像技术及组织学(活检)确诊为肺癌或临床怀疑患有肺癌的患者,以及76名健康对照者。他们呼出的气体用由八个石英微量天平(QMB)组成的气体传感器阵列进行测量,每个QMB都用不同的金属卟啉进行功能化。该仪器为每个分析样本生成一个编码呼吸的信号向量(呼吸指纹)。对呼吸指纹进行多变量分析,以便将传感器信号与疾病相关联。肺癌患者的呼吸指纹与健康对照者的呼吸指纹得以区分,灵敏度为81%,特异性为91%。在有和没有代谢合并症(如糖尿病、肥胖症和血脂异常)的患者中也获得了类似的值(灵敏度分别为85%、特异性为88%以及灵敏度为76%、特异性为94%)。该设备对I期肺癌相对于II/III/IV期肺癌显示出较高的灵敏度(分别为92%和58%)。I期患者无论有无代谢合并症,其灵敏度均无变化(分别为90%、94%)。结果表明,这种电子鼻能够区分肺癌患者和健康对照者呼出的气体。此外,对于I期肺癌患者亚组观察到最高的灵敏度。