image: Study schema for canine olfaction trial. (A) The two dogs, Florin and Midas, selected to participate in the trial. (B) Image of the presentation pots. (C) Test pots placed into the metal arm attached to the carousel. (D) Comparison of indications to biopsy-negative control and cancer samples in double blind trial. This table shows that out of the 21 control samples, Florin produced 5 false positive indications resulting in 76.2% specificity versus Midas' 6 false positive indications resulting in 70% specificity. Both dogs correctly indicated to 5 out of 7 target samples giving 71.4 sensitivity. view more
Credit: Guest et al, 2021 (PLOS ONE, CC-BY 4.0, https://creativecommons.org/licenses/by/4.0/)
New research demonstrates the ability of dogs to detect aggressive prostate cancer from urine samples and suggests that an artificial neural network could learn from this olfactory ability, with an eye toward replicating it in novel detection tools. Claire Guest of Medical Detection Dogs in Milton Keynes, U.K., and colleagues present these findings in the open-access journal PLOS ONE on February 17, 2021.
The widely used prostate specific antigen (PSA) screening test can miss aggressive prostate cancer in men who have it, or indicate that a cancer is aggressive when it really poses little risk. Alternative tests are being explored, and research has also shown that dogs can be trained to detect prostate cancer from urine samples with a high degree of accuracy. However, dogs would be impractical for large-scale screening.
In a pilot study, Guest and colleagues set out to combine the strengths of canine olfaction with those of other promising detection methods in order to surface new insights that could aid development of better prostate cancer tests.
The researchers trained two dogs to detect aggressive prostate cancer from urine samples. These dogs showed 71 percent sensitivity (ability to identify truly positive cases) and 70 to 76 percent specificity (ability to correctly identify negative cases) in detecting prostate cancer with a Gleason score of 9, indicating highly aggressive disease.
The team also applied two laboratory detection methods to the urine samples: Gas chromatography-mass spectroscopy analysis of volatile compounds and analysis of microbial species found naturally in urine. Both methods surfaced key differences between cancer-positive and cancer-negative samples.
Finally, the researchers used the dogs' data to train an artificial neural network to identify specific portions of the spectroscopy data that contributed significantly to the dogs' diagnoses. This also revealed specific differences between positive and negative samples.
The findings suggest that larger studies could further integrate these disparate methodologies in order to improve detection of advanced prostate cancer and aid development of new diagnostic tools that replicate dogs' olfactory capabilities.
The authors add: "We've shown it is possible to replicate the dog's performance as sensors and brains, it is now time to put this technology in every smartphone."
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Citation: Guest C, Harris R, Sfanos KS, Shrestha E, Partin AW, Trock B, et al. (2021) Feasibility of integrating canine olfaction with chemical and microbial profiling of urine to detect lethal prostate cancer. PLoS ONE 16(2): e0245530. https://doi.org/10.1371/journal.pone.0245530
Funding: This work was funded by the Prostate Cancer Foundation Grant (18PILO02) received by CG, AM, and KS. PCF provided partial salary support for authors CG, RH, PMS, AM, and had a role in the study design and preparation of the manuscript, but had no role in the data collection and analysis or the decision to publish. The National Cancer Institute of the National Institutes of Health provided support for WYL and QG (Award Number SC1CA245675). Imagination Engines, Inc. provided support in the form of salary for ST. The NCI NIH and Imagination Engines, Inc. played a role in study design, analysis, decision to publish, and preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
Competing Interests: The authors have read the journal's policy and have the following competing interests: ST is a paid employee of Imagination Engines, Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.
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