Advanced gas sensor arrays analyzing volatile organic compounds in plasma successfully distinguished between ovarian cancer, endometrial cancer, and healthy controls across 289 participants. The machine learning system processed signals from 32 sensors, extracting 85 features per sample to create diagnostic models that could identify cancer presence, differentiate between the two cancer types, and determine disease stage. Metabolic disruptions in cancer cells generate distinctive VOC fingerprints as tumor necrosis releases specific compounds into circulation. This represents a potentially transformative shift from invasive tissue sampling toward liquid biopsy approaches for gynecologic malignancies. The technology addresses a critical gap in early detection, particularly for ovarian cancer where vague symptoms typically delay diagnosis until advanced stages when survival rates plummet. Previous VOC research has shown promise in lung and colorectal cancers, but this application to gynecologic cancers could be especially impactful given the aggressive nature of these diseases. The ability to stage cancers non-invasively could revolutionize treatment planning and monitoring. However, validation in larger, diverse populations remains essential before clinical implementation, and the technology's performance compared to existing biomarkers like CA-125 requires direct comparison studies.