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What did the project achieve?
“We have developed a way to measure the degree of joint inflammation in children with juvenile idiopathic arthritis (JIA) more completely – which will help doctors to personalise drug treatment,” says Professor Margaret Hall-Craggs of University College London. “Our method is potentially more robust, quicker and reproducible than current approaches – and we hope it will be absorbed into UK clinical practice in the future.”
Around 12,000 children in the UK have juvenile idiopathic arthritis (JIA) – which is one in every 1,000 children under the age of 16.[1] JIA is an autoimmune condition, meaning the immune system attacks the joint tissues – leading to inflammation, stiffness and pain that can seriously impact on a child’s quality of life.
Powerful new biological drugs are now available that can reduce inflammation and joint damage – but they aren’t always effective at controlling symptoms and can cause serious side effects. Currently, it’s difficult for doctors to know which children are likely to benefit from these drugs as it is hard to assess if a child’s joints are inflamed, particularly if they are located deep inside the body.
The team has now developed a method to accurately measure the areas of inflammation in the sacroiliac joints – which link the pelvis and lower spine – by analysing data from routine magnetic resonance imaging (MRI) scans using artificial intelligence.
“We are developing a colour-coded picture that highlights areas of inflammation in joints and bone and can show whether it is getting worse or better,” says Professor Hall-Craggs. “We hope these detailed images will make it easier for doctors to interpret these complex data – helping to improve clinical decision-making for each child.”
The team has also completed the largest-ever study of whole-body imaging in patients with JIA to measure the amount of inflammation throughout the body, which revealed there was much more inflammation than anticipated. They also held a ‘virtual clinic’ to find out if this information would affect treatment decisions – such as whether or not to prescribe biological drugs.
“The provision of whole-body MRI scan data would be likely to alter how doctors would manage the condition in up to three in 10 children with JIA,” says Professor Hall-Craggs. “This would lead to more personalised treatment for children with JIA – reducing long-term joint damage while lowering their risk of unnecessary side effects.”
This research was completed on
Around 12,000 children in the UK are affected with juvenile idiopathic arthritis (JIA) – one in every 1,000 young people.1 These conditions can have a long-term impact on their health and well-being – causing joint pain, inflammation and stiffness. New biological drugs that fight disease are now an option, but they aren’t always effective and can cause serious side-effects. Professor Margaret Hall-Craggs at University College London is developing specialised magnetic resonance scans to help doctors measure joint inflammation more accurately and to choose the best drug treatment for each child. Their goal is to reduce pain and disability and to improve long-term quality of life for children with JIA.
How are children’s lives affected now?
JIA is an autoimmune condition, meaning the immune system attacks the body’s own joint tissues. To protect itself, the body creates inflammation, but this leads to stiffness and pain – and lasting damage. So many children with JIA will have life-long pain and disability that can seriously impact on their quality of life.
There are a variety of treatments available for children with JIA that aim to control their symptoms, enabling them to lead active, independent lives. These include new biological drugs that can reduce inflammation and joint damage.
“Although it’s good news that these powerful new drugs are now available, they aren’t effective for everyone and children can experience serious unwanted side-effects,” says Professor Hall-Craggs.
But currently, it’s difficult for doctors to identify who might benefit from these drugs as it is can be hard to assess if a child’s joints are inflamed, particularly if they are deeply seated in the body, and blood tests are often unhelpful.
How could this research help?
“Our aim is to develop tools that enable doctors to measure more accurately a child’s joint inflammation to help them decide when a biological drug treatment should start or change,” says Professor Hall-Craggs. “This will help ensure each child receives the best possible treatment – reducing long-term joint damage while lowering their risk of unnecessary side-effects.”
Professor Hall-Craggs’ team have developed specialised magnetic resonance imaging (MRI) scans that can show when joints are inflamed. The team now plan to improve their technique by analysing scan data using artificial intelligence, creating 3-D pictures that are colour-coded to provide a more accurate measurement of the degree of inflammation within a joint.
“We hope that these detailed images will make it much easier for doctors to interpret complex MRI data, helping to improve clinical decision-making,” says Professor Hall-Craggs. “And they will also help patients to see whether joints are improving or deteriorating over time – potentially encouraging them to keep taking an effective drug.”
References
- National Rheumatoid Arthritis Society: https://www.nras.org.uk/jia [website accessed 26 August 2018]
Acknowledgements
Humanimal Trust is kindly helping to support this research.
| Project Leader | Professor Margaret A Hall-Craggs, BA MBBS MRCP FRCR MD |
| Project Team | Professor Daniel AlexanderDr Cozianna Ciurtin, MBBS MSc PhD FRCP |
| Project Location | Centre for Medical Imaging, University College London |
| Project Location Other | Computer Sciences, Centre for Medical Image Computing University College LondonArthritis Research UK Centre for Adolescent Arthritis, University College London |
| Project duration | 3 years |
| Date awarded | 25 July 2018 |
| Project start date | 11 March 2019 |
| Project end date | 31 March 2022 |
| Grant amount | £196,088 |
| Grant code | GN2697 |
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