In even the recent past, a brain tumour diagnosis was made solely by a pathologist looking down a microscope at a small slice of the tumour on a glass slide. As we know, new information in both technology and science are always expanding our capabilities. In 2016 the World Health Organisation (WHO) released their new recommendations on how to diagnose brain tumours. In addition to descriptions of how the different tumours look down the microscope, for the first time they included molecular tests for some types of tumour. This shows the value of new technologies in making more accurate diagnoses. With this in mind, Dr Diamandis and his team will be developing a complex artificial intelligence (AI) program to provide a step change towards the next generation of diagnostic tools.
This form of AI is called deep convolutional neural networks (CNN), and is used to find patterns in images. The first aim of the research is to teach the CNN to recognise brain tumours. To do this the team will use more than 10 million images of different brain tumours. These tumours have associated notes detailing the patient’s diagnosis, survival and therapy response. The AI will then pool all this knowledge to make diagnostic predictions on a new set of images (from prospective patients).
The team will then use the AI’s machine learning to find other patterns in the images, and associated data, that are too small or infrequent for clinicians to recognise alone. These new insights could be used to make more accurate diagnoses, better prognostic predictions and suggest the best treatment for each tumour.
Ultimately, Dr Diamandis would like to create an automated, cloud-based program so that the AI is available to everyone to use.
As skilled as they are, neuropathologists are ‘only human’. This means their assessment of a tumour through a microscope is subject to human error. It also means that different pathologists could give different diagnoses for the same tumour. This project opens the door to a new way of making a diagnosis, by using artificial intelligence.
The more we know about a tumour at diagnosis, the better chance we have of being able to target it, with appropriate treatments, faster.
By making the tool widely available, Dr Diamandis would ensure that all clinicians, even those in the most remote settings, have equal access to the very best knowledge to enable them to provide the best care possible.
This research has the potential to help everyone who has a potential brain tumour. It could mean a more accurate and reliable diagnosis, with better indication of prognosis, and even faster delivery of the best treatments.
While histopathology plays an important role in optimising care for the majority of brain tumour patients, it is long overdue for innovations that yield more objective, timely, and personalised diagnostic information.
This officially started in January 2019, but the proof of concept work was underway before that. The investigators are planning to share their initial work with colleagues, and get feedback, at a conference later this year.
In the meantime, they’ll be preparing and loading up the 10 million images and the associated data for the CNN to begin its learning.
Once this is complete, they’ll use the new tumours in the University Health Network’s system to test whether the AI is assessing tumours accurately.
All this will lead to the new question of ‘what can we learn from the AI?’, and ultimately a cloud-based version of the AI for all clinicians to use.
Research is the only way we will discover kinder, more effective treatments and, ultimately, stamp out brain tumours – for good! However, brain tumours are complex and research in to them takes a great deal of time and money.
Across the UK, over 100,000 families are facing the overwhelming diagnosis of a brain tumour and it is only through the generosity of people like you can we continue to help them.
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