Detection of lung cancer by sensor array analyses of exhaled breath
Detection of lung cancer by sensor array analyses of exhaled breath
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DOI：
10.1164/rccm.200409-1184oc
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发表时间：
2005-06-01
影响因子：
24.7
通讯作者：
Erzurum, SC
中科院分区：
文献类型：
--
作者：
Machado, RF;Laskowski, D;Erzurum, SC
基本原理：电子鼻已成功应用于商业应用，包括食品工业中挥发性有机化合物的检测和分析。目的：我们假设电子鼻可以识别和区分肺部疾病，特别是肺癌。方法：在发现和训练阶段，对14例肺癌患者、45例健康对照组和45例非肺癌对照组的呼气进行分析。传感器数据的主成分和典型判别分析被用来确定呼出的气体是否可以区分癌症和非癌症。使用马氏距离进行类别间的区分。支持向量机分析被用来在76名个体中前瞻性地创建和应用癌症预测模型，其中14名患有癌症，62名未患癌症。主要结果：主成分分析和典型判别分析表明，肺癌患者样本与其他人群样本之间存在差异。在验证性研究中，电子鼻对肺癌的诊断敏感性为71.4%，特异性为91.9%；阳性预测值为66.6%，阴性预测值为93.4%。在肺癌患病率为18%的人群中，阳性预测值和阴性预测值分别为66.6%和94.5%。结论：肺癌患者呼气具有明显的特征，可用电子鼻进行识别。研究结果为电子鼻用于肺癌的管理和检测提供了可行性。
Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer.