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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.

In a previous canine study, we demonstrated that volatile organic compounds specific to bladder cancer are present in urine headspace, subsequently showing that up to 70% of tumours can be correctly classified using an electronic nose. This study aimed to evaluate the sensitivity and specificity which can be achieved by a group of four trained dogs. In a series of 30 double-blind test runs, each consisting of one bladder cancer urine sample placed alongside six controls, the highest sensitivity achieved by the best performing dog was 73% (95% CI 55-86%), with the group as a whole correctly identifying the cancer samples 64% (95% CI 55-73%) of the time. Specificity of the dogs individually ranged from 92% (95% CI 82-97%) for urine samples obtained from healthy, young volunteers down to 56% (95% CI 42-68%) for those taken from older patients with non-cancerous urological disease. Odds ratio comparisons confirmed a significant decrease in performance as the extent of urine dipstick abnormality and/or pathology amongst the control population increased. Importantly, however, statistical analysis indicated that covariates such as smoking, gender and age, as well as blood, protein and /or leucocytes in the urine did not significantly alter the odds of response to the cancer samples. Our results provide further evidence that volatile biomarkers for bladder cancer exist in urine headspace, and that these have the potential to be exploited for diagnosis.

Previous studies have indicated that volatile compounds specific to bladder cancer may exist in urine headspace, raising the possibility that headspace analysis could be used for diagnosis of this particular cancer. In this paper, we evaluate the use of a commercially available gas sensor array coupled with a specifically designed pattern recognition algorithm for this purpose. The best diagnostic performance that we were able to obtain with independent test data provided by healthy volunteers and bladder cancer patients was 70% overall accuracy (70% sensitivity and 70% specificity). When the data of patients suffering from other non-cancerous urological diseases were added to those of the healthy controls, the classification accuracy fell to 65% with 60% sensitivity and 67% specificity. While this is not sufficient for a diagnostic test, it is significantly better than random chance, leading us to conclude that there is useful information in the urine headspace but that a more informative analytical technique, such as mass spectrometry, is required if this is to be exploited fully.