Early detection of lung cancer in COPD using the SpiroNose
Last week our Scientific Project Manager, Niloufar Farzan, attended the Breath Summit conference (IABR) in Leicestershire, UK to meet and listen to world-renowned researchers in breath research. The Breath Summit conference is held annually and provides great opportunities for researchers to share and transfer their clinical and technical knowledge as well as novel findings in the field.
During the conference, Niloufar had the chance to present and discuss findings of a study performed by the Amsterdam UMC research group. The study, a spin-off of the BreathCloud study, evaluated the accuracy of exhaled breath analysis using eNose technology for detection of early lung cancer in patients with Chronic Obstructive Pulmonary Disease (COPD). BreathCloud is an ongoing multi-centre observational study (>3.000 patients) that uses eNose technology to capture breath profiles of patients with a clinical diagnosis of asthma, COPD or lung cancer. The BreathBase database, that currently contains more than 5.000 breath profiles of patients with a known diagnosis, originated from this study.
Early detection of lung cancer
COPD is a progressive lung disease often caused by significant exposure to harmful gases such as tobacco smoke. Compared to the general population, patients with COPD are at higher risk of developing lung cancer, which is often diagnosed at advanced stages, where the chances of cure are overwhelmingly low. The results of the study showed that capturing the complete mixture of volatile organic compounds (VOCs) using eNose technology could identify COPD patients in whom lung cancer subsequently manifested within 2 years after inclusion. In other words, the results indicated that eNose was able to detect early lung cancer up to 2 years before the clinical signs of the disease manifest.
Capturing complex biological information of individual patients
The presentation was followed by a fruitful discussion and questions regarding the eNose technology and its ability in detecting single molecules. One of the questions that was asked by many was: ‘Do you know what (molecules) the eNose is detecting? and if not, isn’t that a weakness of the technology?’
The answer to these questions depends on the main aim of the study. Technologies that (in some cases) can detect and identify individual VOCs are particularly suitable for pathophysiologic research. However, it is well known by now that diseases such as COPD, asthma and lung cancer have complex underlying molecular networks which vary from patient to patient. This means that to be able to bring clinical diagnostics and personalized medicine to the point-of-care, technologies such as the eNose are required to capture complex biological information of each individual patient. The cloud-connected eNose, SpiroNose, is linked to powerful pattern recognition algorithms that allow real-time and online analysis of the eNose data and provides immediate feedback at the point-of-care. Therefore, the inability to identify individual VOCs, does not impede the clinical application of eNose technology in any way.
What could be clinical implications of this study?
We know that current screening techniques such as CT scanning have high false positive rates. This means that even individuals that do not have lung cancer will undergo follow-up investigations such as biopsy. Due to the risks of these follow-up investigations on individuals, patient with COPD who are in fact at higher risk of developing lung cancer, are not even included in lung cancer screening programs. The results of our study suggest that eNose technology could potentially be used as a screening tool for lung cancer in high risk patients such as COPD before referring the patients to perform tests such as CT scanning. Hence, our next step is to perform a large scale screening study using eNose technology for diagnosis of early lung cancer in these patients. If everything goes well, we believe that eNose technology can help improve the selection procedure of the high-risk individuals for further follow-up tests.
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