| EPSRC Reference: |
EP/T028017/1 |
| Title: |
Big data for small patients - Building "child-size" individual predictive models for life after childhood cancer |
| Principal Investigator: |
Aznar, Dr MM |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
School of Medical Sciences |
| Organisation: |
University of Manchester, The |
| Scheme: |
EPSRC Fellowship |
| Starts: |
01 March 2021 |
Ends: |
28 February 2026 |
Value (£): |
1,195,833
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| EPSRC Research Topic Classifications: |
| Medical Imaging |
Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
Many children with cancer have radiation treatment as part of their care. As for all cancer treatments, there is a risk of lasting side-effects such as learning problems and reduced growth. Research is needed to reduce such side-effects, which is particularly important for children because of their long life expectancy. Radiation treatment is planned to give maximal dose to the tumour and minimal doses to nearby healthy organs. However, even with the most advanced ways of giving radiation (e.g. using the new Proton Beam Therapy machine in Manchester) it will never be possible to avoid all healthy organs.
This fellowship will find which parts of healthy organs are particularly damaged by radiation ('the important regions'). This knowledge would be incredibly useful when planning radiation treatments, because it is often possible to spare the important regions of an organ close to the tumour but not the whole organ. Hence, finding these important regions would be a step toward allowing reduced side-effects in many children with cancer.
The cancer centre with the most and the best documented children's health data in the world is St Jude Children's Research Hospital in Memphis, Tennessee. Children treated with radiation at St Jude have a very detailed and complete follow-up, and their side effects are measured using the most up-to-date methods.
In this project, we will:
(1) Set up a joint data analysis structure to show that our new method can be used on St Jude's data; with this, we will discover, for example, regions of the brain where radiation causes the most learning problems.
(2) Measure the changes in organ size and shape between children of different ages and sizes. For this we will use images from St Jude patients as well as from 500 healthy children in the United States, aged from 6 months to 16 years that were scanned every 2 years (we have permission to use these data for research). This information will help us make our method even more precise and able to find smaller "important regions". We will also use those images to build models of growth of the organs of interest (e.g. language center, hormone glands) in children, which will be useful for researchers studying other childhood diseases.
(3) Develop new and better ways to measure side-effects, using all the follow-up information obtained about a child's health as they grow into adulthood. This will mean, for example, that we can use images showing the health of each child even though taken many years after treatment.
This will be the first project of this kind focused on understanding side effects in children with cancer. In the future, the results from this project will help doctors give 'smarter' radiation treatments, with fewer side-effects. The models of growing organs will also be useful for research in other childhood diseases.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.man.ac.uk |