Authors
Ignacio Deza, Ben de Lacy Costello, Natalia Drabińska-Fois, Paul White, Norman Ratcliffe, Henry Lazarowicz, Chris Probert
Published in
BJC reports. Volume 4. Issue 1. Aug 07, 2026. Epub Aug 07, 2026.
Abstract
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.
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.
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.
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.
PMID:
42567887
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.
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