Diagnostic device detects bladder cancer through urine odor

Cancer cells smell, emitting gases laced with biomarkers revealing volatile organic compounds, and, with the right technology, breaking down that odor can help diagnose bladder cancer, according to a study.

U.K. researchers have developed a sensor-strapped device that can read urine odor and identify patients with bladder cancer, reporting early results that beat out standard diagnostic methods.

In a study published in PLoS ONE, the research team took 98 urine samples--24 from men with bladder cancer and the rest from men with benign bladder problems--heated them and let their diagnostic do the rest. The device identified cancer with 96% accuracy, according to the study, besting current clinical techniques and setting the table for further research.

"The novel combination of a (gas chromatograph) and a unique metal oxide sensor based device has provided data to underpin a new instrument for the diagnosis of bladder cancer," the team wrote. "The statistical model could be easily used to write an algorithm that will display the diagnosis."

But, promising though the pilot study may be, the technology is still in its infancy and will require much more research before coming close to clinical applicability, Cancer Research UK's Sarah Hazell told the BBC.

"This latest method is still at an early stage of development and needs to be tried out on a much larger set of samples, including samples from both women and men," Hazell said. "... But it is another promising step towards detecting bladder cancer from urine samples, something that would ultimately provide a less-invasive means of diagnosing the disease."

- read the study
- get more from the BBC

Suggested Articles

BD will begin working with Babson Diagnostics to help bring its lab-quality device for collecting blood from capillaries into retail pharmacies.

The former CEO of the molecular testing company Foundation Medicine, Troy Cox, has been named chairman of the Swiss big data firm Sophia Genetics.

Researchers at MIT used a machine-learning algorithm to uncover the potent antibiotic properties hiding within an old small-molecule candidate.