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Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Roberto F. Machado, Daniel Laskowski, Olivia Deffenderfer, Timothy Burch, Shuo Zheng, Peter J. Mazzone, Tarek Mekhail, Constance Jennings, James K. Stoller, Jacqueline Pyle, Jennifer Duncan, Raed A. Dweik and Serpil C. Erzurum

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.

Key Words: breath tests • bronchogenic cancer • electronic nose • volatile organic compounds

Smelling to establish diagnoses is a time-honored practice in medicine. For example, detecting fetor hepaticus and the putrid smell of anaerobic infections represent but two examples of olfactory diagnosis, which has largely been abandoned in the face of new diagnostic technologies. However, recent advances in odor-sensing technology, signal processing, and diagnostic algorithms have created chemical sensing and identification devices called "electronic noses," which promise to resurrect olfaction as an important diagnostic option. Electronic noses rely on arrays of chemical vapor sensors that respond to specific stereochemical characteristics of an odorant molecule, particularly volatile organic compounds (VOCs) (1). Multidimensional data obtained from the sensor array can be analyzed by statistical algorithms (e.g., principal components analysis, discriminant function analysis, factor analysis) or by structural algorithms (neural networks) to discriminate and identify odorant samples (2–4). Like the human nose, its electronic counterpart responds in concert to a given odor to generate a pattern, or "smellprint," which is analyzed, compared with stored patterns, and recognized.

Human breath contains a mixture of hundreds of VOCs (5), which offers the possibility that this new electronic nose technology may have many medical applications (6–8). There may be potential utility for electronic nose technology in medical applications, including identification of bacterial pathogens (6, 7, 9, 10) and pneumonia (8), and monitoring of glucose control in patients with diabetes (11). In this context, many VOCs, in particular alkanes and benzene derivatives, measured by mass spectrometry of the exhaled breath have been used to predict the presence of lung cancer in patients (12, 13). However, the method of mass spectrometry to separate and identify 20 or more VOCs in a complex mixture is cumbersome and requires expensive equipment and highly skilled analysts, which limits its widespread application in screening and diagnosis (14).

Because the electronic nose is highly sensitive for detecting VOCs, and based on a previous study involving patients with lung neoplasms (15), we hypothesized that an electronic nose would detect lung cancer on the basis of the complex smellprints of numerous VOCs in exhaled breath from individuals with lung cancer as compared with individuals with other, noncancer lung diseases, or healthy control subjects. Here, we applied support vector machine (SVM) analysis of smellprints of exhaled gases to create a cancer prediction model using a training set of exhaled breath from individuals with cancer or other noncancer lung diseases, or healthy control subjects. To validate the potential utility of smellprint signatures for identifying lung cancer, the discrimination power of the model was tested in an independent sample of 76 individuals. Some of the results of these studies have been previously reported in the form of an abstract (16).

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