University of Otago TV Set

Training Dogs to Detect Cancer

Various presenters

Two videos are included here. The first describes the statistics of diagnostic testing and the second develops the science including health details, procedures, as well as the selection, training, and care of appropriate dogs.

Diagnostic testing is described in a general context with an explanation of sensitivity, specificity, and positive and negative predictive values. Focus then turns to ongoing research investigating the ability of dogs to detect prostate and bowel cancer.

Currently in the proof of concept phase, this research is initially training the dogs on laboratory grown cells with validation experiments to be undertaken. If successful the focus will turn to using patient samples to estimate diagnostic accuracy and investigate how this would be used in clinical practice to improve patient outcomes.

Video 1

The Statistics of Diagnostic Testing

Details of diagnostic testing are outlined to illustrate the concepts of Sensitivity, Specificity, False Negatives, and False Positives leading to Proof of Concept of a procedure to diagnose, in this case, prostate cancer. Laboratory-developed urine samples are used in this investigation. Validation of the procedure to detect Prostate Cancer was held over five consecutive days detecting 200 samples of various concentration ratios from 100% down to 5%. The dog successfully identified those with Prostate Cancer (Sensitivity) 100% of the time and successfully ignored those without the disease (Specificity) 100% of the time. The research therefore can move to the next phase in the pursuit of a non-invasive diagnostic procedure for diagnosing prostate cancer.

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Video 2

Dogs and Science Working Together

In the proof of concept phase, the dogs were trained on laboratory grown cells. With the success of a simple, non-invasive diagnostic test using these urine samples confirmed, stage 2 of the research places the focus on actual patient urine samples in a clinical trial to estimate diagnostic accuracy and investigate how this would be used in clinical practice to improve patient outcomes.

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Video content recorded and edited by Robert van der Vyver and the Media Production Unit[1]
Web site developed and maintained by Greg Trounson[2] and John Harraway[2]
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1: University of Otago
2: Department of Mathematics and Statistics, University of Otago