Implementation of a 3D Computational Mouse Atlas for Detection of Pancreatic Tumours in Transgenic Mice

Pancreatic cancer is extremely difficult to treat with only about 5 % of patients surviving for 5 years. New treatments are urgently needed and many cancer researchers are developing and testing new therapies. These researchers often use specially bred mice to study pancreatic cancer. These mice are born with a healthy pancreas but develop pancreatic tumours deep within their body as they age. Its possible to detect these pancreatic tumours from outside the body using imaging and this can be done so it has very little effect on the animal. Imaging allows scientists to study the tumour growth and whether or not it responds to new therapies.

However, pancreatic tumours can be extremely hard to detect for scientists who are not experts in imaging. Therefore we have developed an artificial intelligence computer program that can find pancreatic tumours on MRI scans automatically with very little input from the researcher. This allows for faster and more accurate tumour detection. Because the tumour can be measured more accurately we can reduce the numbers of animals used in the study and detect tumours earlier when they have less impact on the animal's health.

The small MRI instrument that was used to develop the computer program allows scientist to perform the scans themselves after minimal training. In this project, we are transferring the previously developed method to accurately measure the tumours to three other cancer institutes in the UK. We think that using the artificial intelligence program will allow them also to improve the accuracy of their studies while also reducing the numbers of animals they use in pancreatic cancer research.

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Skills and Knowledge Transfer grant

Status:

Not yet active

Principal investigator

Mr Joseph Brook

Institution

Queen Mary University of London

Co-Investigator

Professor Kairbaan Hodivala-Dilke
Dr Jane Sosabowski

Grant reference number

NC/T00133X/1

Award date:

Jun 2019 - May 2020

Grant amount

£74,986