Implementation of a 3D Computational Mouse Atlas for Detection of Pancreatic Tumours in Transgenic Mice
Lead Research Organisation:
Queen Mary University of London
Department Name: Barts Cancer Institute
Abstract
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 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.
Technical Summary
High impact cancer research demands the use of clinically relevant disease models. One approach is to use genetically modified mouse models (GEMM's) and this has seen an increase in the number of animals bred for scientific research, a high proportion of which are wasted as they don't have the correct phenotype. Tumour development in GEMMs is difficult to evaluate as their disease develops over long periods of time, usually deep within the body and so tumour detection and measurement of response is challenging unless the animal is sacrificed.
The mouse pancreas is an ill-defined organ and in GEMMs of pancreatic cancer, the development of tumour is extremely difficult to assess non-invasively. Magnetic resonance imaging (MRI) and ultrasound (US) are both useful but their potential to reduce animal numbers by providing early detection of tumour and accurate longitudinal imaging data is limited for a number of reasons. MRI is preferred but can be expensive, less widely available and accurate determination of tumour volume via image analysis of the abdominal area is challenging. However, we have developed a 3D computational mouse atlas (3D-CAMMP) that can automatically detect pancreas and pancreatic tumour (with an accuracy of 95%) in MR images of a commonly-used pancreatic GEMM (the KPC mouse). This machine learning model uses imaging data collected on a relatively inexpensive low field MRI instrument. Automatic detection allows a non-expert user to perform MRI imaging and analysis with minimal training. In this project we will transfer use of the 3D-CAMMP mouse atlas to three cancer institutes in the UK, all with high numbers of KPC mice. We are confident that early and accurate identification of tumours will allow optimised evaluation of potential treatments to reduce variability, experiment duration and ultimately have the effect of reducing numbers of required animals used in pancreatic cancer research.
The mouse pancreas is an ill-defined organ and in GEMMs of pancreatic cancer, the development of tumour is extremely difficult to assess non-invasively. Magnetic resonance imaging (MRI) and ultrasound (US) are both useful but their potential to reduce animal numbers by providing early detection of tumour and accurate longitudinal imaging data is limited for a number of reasons. MRI is preferred but can be expensive, less widely available and accurate determination of tumour volume via image analysis of the abdominal area is challenging. However, we have developed a 3D computational mouse atlas (3D-CAMMP) that can automatically detect pancreas and pancreatic tumour (with an accuracy of 95%) in MR images of a commonly-used pancreatic GEMM (the KPC mouse). This machine learning model uses imaging data collected on a relatively inexpensive low field MRI instrument. Automatic detection allows a non-expert user to perform MRI imaging and analysis with minimal training. In this project we will transfer use of the 3D-CAMMP mouse atlas to three cancer institutes in the UK, all with high numbers of KPC mice. We are confident that early and accurate identification of tumours will allow optimised evaluation of potential treatments to reduce variability, experiment duration and ultimately have the effect of reducing numbers of required animals used in pancreatic cancer research.
Planned Impact
The 3 R's impact of this project will be in reduction in numbers of animals used through longitudinal data collection in vivo to replace ex vivo measurements that require sacrifice at serial timepoints. More data will be obtained from the same animal to inform future studies so that further reduction can be achieved.
In addition, by achieving a more accurate assessment of tumour burden prior to entering animals into studies, we will reduce biological variability within groups and therefore be able to achieve a reduction in group size. This is especially relevant to GEMM's where tumours develop over long periods of time (e.g. 3-6 months) and entry into the study is based on tumour burden rather than age. Use of 3D-CAMMP will allow earlier detection when tumours are smaller. Therefore studies can start earlier at a more clinically relevant stage, end earlier and reduce suffering to the animals. Use of MRI scanning rather than ultrasound will reduce stress to the animals as they will not need to be shaved.
Reduction: During the term of the project, the technique will be applied in Barts Cancer Institute, the Beatson Institute in Glasgow, the Francis Crick Institute in London and the Cambridge CRUK Centre. All of these institutes have sizeable colonies of pancreatic GEMM's and achieving our aim of substituting one sacrifice timepoint for a longitudinal imaging data readout combined with a reduction in group size would lead to an overall reduction in 9100 animals bred in the UK p.a. for KPC alone.
Accessibility and transferability: the atlas is designed for non-imaging experts, the 3D-CAMMP is free to use and the whole body mouse atlas is inexpensive to buy (~£3125). Invicro will promote the 3D-CAMMP as a free add-on to their whole body mouse atlas. Support will be available online through video manuals and library data will be freely available to new adopters.
Metrics: we will implement a system for recording numbers of animals saved similar to the system already implemented at BCI.
New uptake of 3R's research methods: We hope to increase the profile of 3R's methods (and the scientific and economic benefits), especially in the partner institutes and the Beatson in particular. There are also other cancer models (lung, colon liver) which could be areas of development for new 3R's methodology, in particular the implementation of this tool.
Publications and dissemination: this will be through publication in high impact journals, outreach and public engagement, seminars within the partner institutes with presentation of the data. The response of scientists to 3D-CAMMP has been extremely positive with researchers actively seeking to use it. We will also continue to present at national and international conferences.
In addition, by achieving a more accurate assessment of tumour burden prior to entering animals into studies, we will reduce biological variability within groups and therefore be able to achieve a reduction in group size. This is especially relevant to GEMM's where tumours develop over long periods of time (e.g. 3-6 months) and entry into the study is based on tumour burden rather than age. Use of 3D-CAMMP will allow earlier detection when tumours are smaller. Therefore studies can start earlier at a more clinically relevant stage, end earlier and reduce suffering to the animals. Use of MRI scanning rather than ultrasound will reduce stress to the animals as they will not need to be shaved.
Reduction: During the term of the project, the technique will be applied in Barts Cancer Institute, the Beatson Institute in Glasgow, the Francis Crick Institute in London and the Cambridge CRUK Centre. All of these institutes have sizeable colonies of pancreatic GEMM's and achieving our aim of substituting one sacrifice timepoint for a longitudinal imaging data readout combined with a reduction in group size would lead to an overall reduction in 9100 animals bred in the UK p.a. for KPC alone.
Accessibility and transferability: the atlas is designed for non-imaging experts, the 3D-CAMMP is free to use and the whole body mouse atlas is inexpensive to buy (~£3125). Invicro will promote the 3D-CAMMP as a free add-on to their whole body mouse atlas. Support will be available online through video manuals and library data will be freely available to new adopters.
Metrics: we will implement a system for recording numbers of animals saved similar to the system already implemented at BCI.
New uptake of 3R's research methods: We hope to increase the profile of 3R's methods (and the scientific and economic benefits), especially in the partner institutes and the Beatson in particular. There are also other cancer models (lung, colon liver) which could be areas of development for new 3R's methodology, in particular the implementation of this tool.
Publications and dissemination: this will be through publication in high impact journals, outreach and public engagement, seminars within the partner institutes with presentation of the data. The response of scientists to 3D-CAMMP has been extremely positive with researchers actively seeking to use it. We will also continue to present at national and international conferences.
