e22525 Background: Non-small cell lung cancer (NSCLC) is highly lethal as most cases are late stage at diagnosis. Screening efforts with computed tomography aimed at reducing NSCLC mortality is limited to specific sub-populations with limited implementation globally. Thus, rapid scalable screening tools to detect NSCLC continue to be an unmet need. Detection and analysis of volatile organic compounds (VOCs) from exhaled breath with proton transfer reaction-time-of-flight mass spectrometry (PTR-MS) is real-time, non-invasive, and highly sensitive. With the hypothesis that PTR-MS analytics of exhaled VOC can discriminate between patients with lung cancer and healthy volunteers, we performed a pilot study to identify and define a unique exhaled-breath VOC signature representative of newly diagnosed, treatment-naïve NSCLC. Methods: A single-centre, prospective, pilot study was conducted. Treatment-naïve patients with newly diagnosed, histologically confirmed NSCLC of any stage were recruited and the control group comprised age, sex, and smoking history matched healthy volunteers with no known malignancy. Exhaled-breath VOC profiles were analysed with PTR-MS testing. A proprietary machine learning algorithm was employed to analyse breath samples to identify a VOC signature that could distinguish between patients with newly diagnosed, treatment naïve NSCLC and healthy controls. Results: 185 newly diagnosed treatment naïve lung cancer patients were recruited over 18 months with over 500 distinct VOC measured in each breath. Amongst these VOC, 20 were identified that differed significantly in concentration within breath of patients with lung cancer compared to breath of healthy controls. To make a 20 VOC multiplex predictive biomarker for lung cancer, a random forest model with 10-fold cross validation was adopted to build the classifier. The optimised VOC signature distinguished patients with newly diagnosed treatment naïve NSCLC from healthy controls with an area under the curve of 0.9 and 0.87 in the training and validation sets respectively with corresponding sensitivity and specificity of 93% and 86% in the training set, and 89% and 85% in the validation set. Conclusions: This study represents the largest known cohort of newly diagnosed, treatment naïve NSCLC patients with exhaled-breath VOC samples. A high-performance VOC signature unique to newly diagnosed NSCLC was identified. The high sensitivity and specificity demonstrated provides a strong signal for the feasibility of breath-based screening for NSCLC. Further large-scale external validation is ongoing to test the utility of exhaled-breath VOC as a real-time, point-of-care non-invasive screening tool for NSCLC.
Kaur et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: