2024 Asia Conference on Lung Cancer (ACLC) - Poster & ePosters-EP01.10: Non-Invasive Diagnosis of Thymoma Using Breath Volatile Organic Compounds: A Pilot Study

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This pilot study explores the potential of using volatile organic compounds (VOCs) in exhaled breath as a non-invasive diagnostic tool for identifying thymoma, which is crucial due to the disease's often asymptomatic and diverse presentations. Conducted at the First Affiliated Hospital of Sun Yat-sen University, the research involved 21 participants, including 13 thymoma patients and 8 controls with benign conditions. Participants were selected based on computed tomography and histological assessments, ensuring matches in age, gender, BMI, and smoking status.<br /><br />The study employed a standardized protocol for breath collection, followed by analysis using gas thermal desorption-chromatography-mass spectrometry (TD-GC-MS). Data analysis included principal component analysis (PCA), hypothesis testing, and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential VOC biomarkers that are statistically significant and have a high variable importance in projection (VIP).<br /><br />Results indicated that 13 distinct VOCs showed promise as biomarkers for thymoma, presenting significant differences compared to controls. These included a range of chemical classes such as ketones, aldehydes, alkenes, aromatic hydrocarbons, sulfur compounds, terpenes, alkanes, benzophenones, lactones, and thiazoles. The potential exogenous origins of some VOCs necessitate further investigation.<br /><br />The study concludes that breath VOC analysis could be a promising non-invasive method for diagnosing thymoma. However, the validation of these findings in larger cohorts is critical to determine the clinical utility of the identified biomarkers for early detection and management of thymoma.

Asset Subtitle

Xinxin Cheng

Keywords

volatile organic compounds

VOCs

thymoma diagnosis

non-invasive diagnostic tool

breath analysis

TD-GC-MS

biomarkers

principal component analysis

orthogonal partial least squares

First Affiliated Hospital of Sun Yat-sen University