Addenbrooke’s Hospital in Cambridge will become the first hospital in the world to use cutting-edge artificial intelligence technology from Microsoft that could improve survival rates for some cancers.
The hospital will use the InnerEye project, which was developed at Microsoft’s Cambridge Research Lab, to develop AI models that use the hospital’s own data to automatically highlight tumours and healthy organs on patient scans. These will then be checked and confirmed by a clinical oncologist before the patient receives treatment.
It will shorten the lengthy treatment planning stage, which is critical for cancers of the head and neck, which can grow fast if left untreated. Experts are expecting survival rates to increase, as a result.
Dr Raj Jena (above), an Oncologist at Addenbrooke’s and Co-Lead of InnerEye, said: “The results from InnerEye are a game-changer. To be diagnosed with a tumour of any kind is an incredibly traumatic experience for patients. So as clinicians we want to start radiotherapy promptly to improve survival rates and reduce anxiety. Using machine learning tools can save time for busy clinicians and help get our patients into treatment as quickly as possible.”
Up to half of the people in the UK will be diagnosed with cancer at some point in their lives. Of those, half will be treated with radiotherapy, which involves focusing high-intensity radiation beams to damage the DNA of hard, cancerous tumours while avoiding surrounding healthy organs.
It starts with a 3D CT (Computed Tomography) imaging scan of the part of the body to be targeted. These CT images come in the form of stacks of 2D images, dozens of images deep, each of which must be examined and marked up by a radiation oncologist, clinical oncologist or specialist technician. This process is called contouring. In each image, an expert must manually draw a contour line around the tumours and key healthy organs in the target area using dedicated computer software. For complex cases, this can take several hours in the planning of a single patient’s treatment. InnerEye can do this task 13 times faster.
Addenbrooke’s has been working with Microsoft to develop and pilot InnerEye over the past eight years. When the AI tool is in place, the hospital will be able to use its own data to improve accuracy. It is believed to be the first time an NHS trust has introduced a deep-learning solution trained on their own data.
Yvonne Rimmer, Consultant Clinical Oncologist at Addenbrooke’s, said: “There is no doubt that InnerEye is saving me time. It’s very good at understanding where the prostate gland is and healthy organs surrounding it, such as the bladder. It’s speeding up the process so I can concentrate on looking at a patient’s diagnostic images and tailoring treatment to them.
“But it’s important for patients to know that the AI is helping me in my professional role; it’s not replacing me in the process. I doublecheck everything the AI does and can change it if I need to. The key thing is that most of the time, I don’t need to change anything.”
To ensure that all hospitals can use the InnerEye Deep Learning Toolkit, Microsoft has made it freely available as opensource software. Clinical use of machine learning models is subject to regulatory approval.
Javier Alvarez-Valle, Principal Research Manager at Microsoft Research Cambridge, said: “AI models trained with InnerEye are changing the way cancer is treated, speeding up the process to give patients greater peace of mind and empowering clinical oncologists with an AI assistant.
“The AI works in the background, so clinical oncologists just open up the scans on their computer and they can see what their AI model has highlighted. The clinical oncologist then decides what to do with that information.
“AI models trained with InnerEye will be hosted in Microsoft’s Azure cloud, so all the data is securely held in the UK and only available to the medical staff who need to use it.”
Addenbrooke’s is run by Cambridge University Hospitals NHS Foundation Trust and is a teaching hospital, research centre and designated academic health science centre.