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 chromatography-mass spectrometry (GC-MS) to determine potential volatile and semi-volatile biomarkers. The results were analysed using classical univariate statistics and machine learning methods. Five machine learning algorithms were evaluated, with recursive feature elimination (RFE) identifying optimal biomarker panels. Results Machine learning with XGBoost achieved area under the receiver operating characteristic curve (AUROC) of 0.869 (95% CI: 0.740–0.988), representing a significant improvement over the classical statistical approach (AUROC 0.752). An 8-metabolite panel achieved balanced sensitivity and specificity of 85%, or 95% sensitivity with 70% specificity when optimised for screening. Conclusions The findings indicate that solvent extraction of urine shows promise for isolating putative biomarkers of bladder cancer. Employing machine learning achieved diagnostic accuracy potentially suitable for clinical deployment as a non-invasive bladder cancer detection tool.