Detection of lung cancer by sensor array analyses of exhaled breath

Abstract

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.

Figure 1.

Principal components analysis plot, shown as a two-dimensional projection of the 32-dimensional vector analyses, demonstrates distinct clustering of the samples from patients with lung cancer separate from healthy control subjects (upper panel), whereas patients with interstitial lung disease or emphysema are not separable from healthy control subjects (lower panel). Inset: Example of a typical smellprint derived from the 32 sensor responses, which are used in the multidimensional analyses, from a healthy control subject (black bars) and a patient with lung cancer (gray bars). α1-AT = α₁-antitrypsin; CBD = chronic pulmonary beryllium disease; R = postmeasurement sensor resistance; Ro = baseline sensor resistance.

Figure 2.

Support vector machine (SVM) classification for lung cancer. During classification, SVM calculates the distance of the unknown sample from the decision boundary in the model it has learned. In this graph, the margin for each breath sample is shown, with a positive value indicating classification of lung cancer (i.e., how far within the lung cancer boundary the sample falls). A minimum of five analyses performed on each individual's exhaled breath is shown. A negative value indicates a noncancer classification, with the value indicating how far outside of the lung cancer boundary the sample falls. The incorrect classification of a sample is identified by the open circles at the end of the line, and correct classification by closed circles. The majority of predictions were concordant (i.e., all five classifications of an individual the same in 92% of cases). Discordance occurred in 8% of cases. Assignment as cancer was predicted based on the predominant response (three or more of five) for that particular patient. Incorrect classification of lung cancer as noncancer is noted for two individuals with small cell carcinoma (f, g: all five analyses for each predict noncancer) and two individuals with relatively small primary lesions (h: four of five analyses predict noncancer; i: all five analyses for each predict noncancer). Incorrect classification of control subjects as cancer is noted for an individual with asthma with severe airflow limitation (a: three of five analyses cancer prediction), an individual with primary pulmonary hypertension (PAH; b: all five analyses predict cancer), and three healthy nonsmoking control subjects with no known lung disease (c, e: four analyses predict cancer; d: all five analyses predict cancer). *Indicates breath samples from two different individuals with lung cancer after curative resection of cancer.