eNose analysis of exhaled breath for diagnosing and phenotyping respiratory diseases
Publication:
Aim:
- To investigate a new method for exhaled breath analysis in asthma, COPD, and lung cancer patients.
- To identify phenotypes of chronic airway disease (amongst patients with asthma and/or COPD) driven by eNose analysis of exhaled breath.
- To identify responders and nonresponders to immunotherapy (anti-PD-1) in patients with non-small cell lung cancer.
- To evaluate the differentiation between mesothelioma patients with and without a clinical response to treatment and we investigate alterations in breath profiles during treatment.
- To assess the diagnostic accuracy of eNose analysis for prospective prediction of the clinical manifestation of lung cancer in COPD patients.
Introduction
Respiratory diseases like asthma, COPD, and lung cancer pose significant diagnostic challenges due to their heterogeneity. Early and accurate diagnosis is essential for effective treatment. Traditional methods like spirometry and imaging have limitations, especially in identifying distinct disease phenotypes and early disease stages. This thesis validates the use of exhaled breath analysis with an electronic nose (eNose) for diagnosing and phenotyping respiratory diseases. eNose technology leverages breathomics, a branch of metabolomics, which analyzes volatile organic compounds (VOCs) in breath to capture unique metabolic signatures associated with specific diseases.
Methods
The thesis encompasses several studies, including an extensive technical validation and clinical assessments of the SpiroNose®. Study populations included healthy controls, asthma patients, COPD patients, and lung cancer patients. Exhaled breath samples were collected and analyzed using metal-oxide semiconductor sensor arrays. Signal processing and pattern recognition algorithms were employed to interpret the breath profiles. The diagnostic performance of the eNose was evaluated through cross-validation and compared with traditional diagnostic methods.
Results
Multiple studies demonstrated that the SpiroNose could effectively distinguish between different (respiratory) diseases with high accuracy. For instance, the eNose achieved a diagnostic accuracy of 80-90% for differentiating asthma, COPD, and lung cancer. The cloud-connected eNose provided robust, reproducible results and enabled real-time analysis. Moreover, the technology was successful in identifying inflammatory phenotypes within asthma and COPD, as well as distinguishing responders from non-responders to immunotherapy in lung cancer.
Discussion
The results of this thesis highlight the potential of eNose technology as a non-invasive diagnostic tool for respiratory diseases. By leveraging the power of breathomics, the SpiroNose offers a novel approach to accurately capture metabolic signatures linked to conditions like asthma, COPD, and lung cancer. Unlike traditional diagnostic methods that may miss early disease signs or struggle with heterogeneous phenotypes, the SpiroNose has demonstrated a high level of accuracy and reproducibility in identifying distinct disease profiles.
This technology provides real-time insights, making it a valuable addition to routine clinical practice. The cloud-based eNose not only enhances diagnostic precision but also enables rapid decision-making at the point of care. The ability to monitor inflammatory phenotypes and predict responses to therapy further underscores its potential in personalized medicine.
