Academic researchers and experts from an animal health company have developed an electronic system for diagnosing lymphoma in dogs in the early stages and for remission monitoring.

Nearly one in four dogs will develop cancer and 20% of those will be lymphoma cases.

Led by Alexander Gorban, professor of applied mathematics at the University of Leicester, the researchers, together with experts from Avacta Animal Health, developed the canine lymphoma blood test (cLBT), the first of its kind to track the remission monitoring status of a dog after chemotherapy.

The test detects the levels of two biomarkers, the acute phase proteins C-reactive protein and haptoglobin.

Avacta has been actively involved in developing new tests for canine lymphoma. It has collected biological samples to conduct research and tested the data by working closely with the university and its statistical and data processing techniques. Researchers analysed clinical data, tested various machine learning methods and selected the best approach to these problems.

Avacta’s chief scientific officer Kevin Slater said the collaboration would have a dramatic impact on the types of new tests it could offer to vets and dog owners.

“We are already widening the application of multivariate analysis to other diseases that commonly affect our pets, and subsequently, this work could also have benefits to human health,” he said.

Prof Gorban said it had been a very interesting project. “Avacta is a very dedicated, focused company, with clear goals and objectives,” he said.

“The project was very successful, and we would be very glad to welcome more partnerships of this type. It involved full academic and commercial success, which has included a full academic cycle as well as full software development, which makes it an incredibly diverse project to have worked on.”

During the study, funded by the university’s Innovation Partnership project, the academic team selected the best method to work with the data collected by Avacta and prepared the online diagnostic system over a period of six months. These methods included further development of the system for canine lymphoma differential diagnosis and for remission monitoring.

The paper Computational diagnosis and risk evaluation for canine lymphoma has been published in academic journal Computers for Biology and Medicine and is available at

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