Detection of Barrett’s oesophagus through exhaled breath using an electronic nose device

Correspondence to Professor Peter D Siersema, Gastroenterology and hepatology, Radboudumc, Nijmegen, Gelderland 6525 GA, Netherlands; [email protected]

Study background

The incidence of oesophageal adenocarcinoma (OAC) has risen by sixfold over the last few decades.¹ The majority of patients with OAC present with advanced disease, resulting in poor survival rates.² Although Barrett’s oesophagus (BO) is a known precursor of OAC, >90% of patients with OAC never had prior endoscopy and OAC is usually diagnosed outside BO surveillance.³ Upper endoscopy, the current standard for BO detection, is invasive and expensive and therefore not suitable for population-based screening. Analysis of volatile organic compounds (VOCs) in exhaled breath may be a promising technique to detect undiagnosed cases of BO in the population.⁴ VOCs are gaseous end products resulting from physiologic metabolic processes in the body, pathophysiologic processes such as inflammation or oxidative stress-related activity, or external factors such as medication use or changes in the microbiome.⁵ Therefore, VOC profiles may serve as a biomarker for various diseases. Electronic noses represent a potential technique for real-time, high throughput exhaled VOC pattern analysis. In this multicentre, proof-of-principle study, we assessed the accuracy of exhaled VOCs analysis using an electronic nose device for detection of BO compared with endoscopy and biopsy as the reference standard.

Methods

Adult patients undergoing a clinically indicated upper endoscopy were invited to provide a 5 min breath sample using an electronic nose immediately before a scheduled endoscopy. VOC analysis by an electronic nose device is based on pattern recognition and resembles mammalian olfaction (figure 1).^{6 7} The Aeonose (the eNose company, Zutphen, the Netherlands) consists of three metal-oxide sensors and uses chemical to electrical interfaces to measure subtle VOC profiles of different diseases in exhaled breath. Data were analysed by an artificial neural network in a supervised fashion to identify data classifiers to extract breath print differences between patients with BO, gastro-oesophageal reflux disease (GORD), and controls. Leave-10%-out cross-validation of data was performed after training the artificial neural network to make sure the prediction model generated was disease-specific. More details on the methods and the electronic nose technology can be found in the online supplementary file.^{8 9}

Results

Breath samples were obtained from 513 individuals, resulting in a patient acceptability rate...