Breath biomarkers for esophageal cancer: identification, quantification, and diagnostic modeling.

Abstract

Esophageal cancer is a major global health issue with a high mortality rate. Early diagnosis is crucial for improving patient outcomes, but traditional diagnostic methods are often invasive and costly. This study explores the potential of exhaled volatile organic compounds (VOCs) as a non-invasive diagnostic tool for esophageal cancer. Using gas chromatography-mass spectrometry (GC-MS), we analyzed the breath samples of 80 esophageal cancer patients and 60 healthy controls, identifying and quantifying over 100 VOCs. The results revealed significant differences in the concentrations of VOCs such as acetone, ethanol, and isoprene between the two groups. A multi-parameter regression diagnostic model based on a neural network algorithm achieved an accuracy of 90.3% in distinguishing esophageal cancer patients from healthy individuals. Further optimization incorporating physiological factors, including smoking, drinking, and dietary habits, improved the model's accuracy to 92.4%, with a specificity of 93.1%, representing a significant improvement over previous studies. These results suggest that VOCs analysis in exhaled breath holds great promise as a non-invasive, cost-effective, and accurate method for early detection of esophageal cancer.