Exploratory analysis of exhaled volatile organic compounds for binary discrimination between lung cancer, pneumonia, and healthy controls using machine learning

This exploratory study demonstrates that exhaled breath VOC profiles, analyzed via machine learning, yield statistically distinguishable signals in pairwise comparisons between lung cancer, pneumonia, and healthy individuals. These results provide preliminary evidence that breath analysis could address the critical clinical challenge of differentiating radiographically similar conditions non-invasively. The presented methodology and dataset establish a foundational framework for characterizing disease-specific metabolic signatures. However, the findings remain hypothesis-generating. Definitive evaluation of clinical utility necessitates subsequent studies employing multiclass modeling, validation in independent and prospective cohorts, and direct assessment of diagnostic impact in real-world triage scenarios.