Updated test version achieved significant performance improvement in detecting advanced precancerous lesions, including those with high-grade dysplasia

**Abstract:** Early-stage lung cancer detection remains a significant challenge, relying heavily on invasive biopsies and often presenting with vague symptoms. This paper proposes a novel analytical framework leveraging hyperdimensional computing (HDC) and machine learning for non-invasive lung cancer detection via analysis of Exhaled Volatile Organic Compounds (EVOCs). The method, termed HV-CancerDx, transforms complex EVOC profiles into high-dimensional hypervectors, enabling the identification of subtle biomarker correlations undetectable by conventional analytical techniques.

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