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THE PROBLEM: Lymphoma, a prevalent and deadly cancer, affects the lymph nodes of dogs. It's essential to quickly determine how well a dog with lymphoma responds to treatment, as this significantly impacts survival. Typically, this response is assessed by extracting cells from the lymph node and examining them under a microscope to check if the cancer has disappeared after treatment. However, this method has a limitation: the disease must be quite advanced to be detectable under a microscope. A more specialized molecular test is available to overcome this limitation. These tests are more effective than microscopes in identifying cancer cells but can be expensive. This high cost can lead to less frequent testing, potentially causing delays in detecting cancer recurrence or response to treatment.    

THE PROJECT: Convoluted neural networks, a computer analysis tool, show great promise in medical image analysis, especially for early cancer detection. This study explores the possibility of using CNNs to analyze cells from dog lymph nodes. The goal is to see if CNNs can quickly and cost-effectively determine the presence or absence of cancer in affected dogs. 

POTENTIAL IMPACT: If successful, this approach could enhance early detection, monitor treatment responses, and identify relapses in dogs with lymphoma. Moreover, the results could lead to the development of similar computer-based tools for other cancer types, potentially extending survival times for dogs with cancer.   

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Grant amount awarded
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Virginia Polytechnic Institute and State University   
Study country
United States
Christina Pacholec, DVM, Resident and PhD candidate   
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