Evaluation of gas chromatography mass spectrometry and pattern recognition for the identification of bladder cancer from urine headspace

Abstract

Previous studies have indicated that volatile organic compounds specific to bladder cancer may exist in urine headspace, raising the possibility that they may be of diagnostic value for this particular cancer. To further examine this hypothesis, urine samples were collected from patients diagnosed with either bladder cancer or a non-cancerous urological disease/infection, and from healthy volunteers, from which the volatile metabolomes were analysed using gas chromatography mass spectrometry. The acquired data were subjected to a specifically designed pattern recognition algorithm, involving cross-model validation. The best diagnostic performance, achieved with independent test data provided by healthy volunteers and bladder cancer patients, was 89% overall accuracy (90% sensitivity and 88% specificity). Permutation tests showed that these were statistically significant, providing further evidence of the potential for volatile biomarkers to form the basis of a non-invasive diagnostic technique.