With 300 million olfactory receptors in their noses—50 times more than humans—dogs can smell scents that humans cannot even perceive. Researchers now report that this sniffing capability allows dogs to distinguish between blood samples of patients with cancer and blood samples of healthy individuals.
For the study, presented at the American Society for Biochemistry and Molecular Biology: Experimental Biology Meeting 2019, four 2-year-old beagle dogs were trained by operant conditioning to use their sense of smell to distinguish between blood serum from patients with malignant lung cancer and serum from healthy controls.
Three of the four dogs were able to identify the cancer samples with a sensitivity of 96.7%, specificity of 97.5%, positive predictive value of 90.6%, and negative predictive value of 99.2%. Because the fourth dog, Snuggles, was not motivated to perform, he identified the cancer samples with a specificity of 80% and a sensitivity of 60%.
Canine detection could potentially be used as way to test for early recognition of cancer.
"Although there is currently no cure for cancer, early detection offers the best hope of survival," remarked the study's lead author, Heather Junqueira, Lead Researcher at BioScentDx. "A highly sensitive test for detecting cancer could potentially save thousands of lives and change the way the disease is treated."
More studies are needed to further the understanding of canine cancer detection.
"This work is very exciting because it paves the way for further research along two paths, both of which could lead to new cancer detection tools," said Ms. Junqueira. "One is using canine scent detection as a screening method for cancers, and the other would be to determine the biologic compounds the dogs detect and then design cancer-screening tests based on those compounds."
For More Information
Junqueira H, Quinn T, Biringer R, et al (2019). Accuracy of canine scent detection of lung cancer in blood serum. American Society for Biochemistry and Molecular Biology: Experimental Biology Meeting 2019. Abstract 635.
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