Learning from prostate cancer-detecting dogs to improve diagnostic tests

Cross-disciplinary study integrates canine olfaction with other promising methods

Date:: February 17, 2021
Source:: PLOS
Summary:: 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.
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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 journalPLOS ONEon 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.

研究人员训练两只狗检测攻击ive 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."