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TRV-2026-0728Certified recordPeer-reviewed

Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer

Background Bladder cancer is the 11th most common cancer in the United Kingdom, with approximately 10,500 new cases annually. Diagnosis and surveillance typically involve cystoscopy, an expensive, time-consuming, and uncomfortable procedure which has encouraged efforts to identify biomarkers, particularly in urine, given its direct contact with malignant tissue. Methods Urine collected from 100 participants (50 bladder cancer patients, 50 controls) was subjected to solvent extraction followed by gas chromatograp…

Health · G Space — documented gain · certified 2026-08-10 · v1 · article view · machine-readable

Current reading — gain

Solvent extraction of urine analyzed by GC-MS and XGBoost distinguished bladder cancer patients from controls with AUROC 0.869 and 85% balanced sensitivity and specificity using an 8-metabolite panel.

What this doesn’t fix

Findings are based on a small cohort of 100 participants with 50 cases and 50 controls, limiting generalizability and requiring larger validation before clinical deployment.

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Truvace Impact Record TRV-2026-0728, v1: “Urinary volatilomics using liquid-liquid extraction and gas chromatography-mass spectrometry (GC-MS) combined with machine learning algorithms as a tool for diagnosis and surveillance of urothelial bladder cancer.” Truvace, 2026-08-10. /record/TRV-2026-0728 (accessed at citation time). sha256 101c9b9d52b1b55d…

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