News and events

19/09/23

New Funding for the application of AI

It is our pleasure to share the exciting news that Professor Adrienne Flanagan, along with a team of specialist researchers, has received funding from the UK Research Institute (UKRI) and the Engineering & Physical Sciences Research Council (EPSRC).
This funding will be used to delve into the application of Artificial Intelligence (AI) in categorizing sarcomas, including chordoma and related conditions.
A heartfelt thank you to all those who contributed their insights to this project prior to its submission.

Professor Flanagan wanted me to express her profound gratitude and emphasise how instrumental your feedback was in securing this grant.

Please read the following summary for a detailed explanation of what the AI project involves.


Artificial Intelligence (AI) for the classification and predicting outcomes of sarcoma: helping pathologists to make better diagnoses faster. 

Tumours are broken down into two different groups: benign or malignant. While benign tumours can grow to a large size, they do not spread to other parts of the body. This means that if they are completely removed, the patient can be cured of their disease. Malignant tumours are also known as ‘cancer’. They have the potential to spread to other parts of the body, although this does not always happen, but generally requires further treatment. Thanks to dedicated research, new treatments are being developed constantly, and survival continues to improve for many patients with cancer. 

Cancers are classified into three major types

 *About 85% of all cancers are carcinomas. The most common types arise from the lining cells of organs such as the lung, gut, mouth, uterus, bladder, prostate, and breast. 
*About 15% of all cancers are those of blood (leukaemia) and lymphoid tissue (lymphoma). 
*Sarcomas are very rare and represent about 1% of all cancers. Sarcoma can present anywhere in the body and therefore the pathologist who first sees the sample of the tumour may not be a specialist in sarcoma pathology. There are also more than 70 different types of sarcomas making the diagnosis even more challenging for many pathologists.

Advances in medicine and pathology 

With rapid advances in medicine over the last two decades, the sheer volume of new information means it can be difficult for pathologists to remember everything about all the different malignant and benign tumour types. This can then delay the diagnosis being made and can also lead to unnecessary and expensive ‘specialist tests’ being done which results in wasted time and money. There is also a shortage of pathologists in the UK, and the increasing workload could also lead to delays in diagnosis and treatment of cancer. 

Tumour samples seen down a microscope are not always easy to tell apart. 

It is not always straightforward for a pathologist to distinguish different tumour types. For example, a melanoma (skin cancer) can look very similar to an ‘undifferentiated sarcoma’. In some cases, it can also be difficult to distinguish a sarcoma from a benign tumour. For example, a ‘nodular fasciitis’, which is referred to as a ‘sarcoma mimic’ can look like a spindle cell sarcoma. However, the treatment of these two tumours is very different. Making the correct diagnosis is vital to guide the surgeons and oncologists to undertake the most suitable treatment of a specific tumour type for each patient. This is known as ‘personalised medical care’. 

Artificial Intelligence

Artificial intelligence can be used to teach a computer how to recognise different types of tumours. This technology will be used to help pathologists reach a diagnosis more quickly and efficiently. In our AI research we plan to train the computer to distinguish about 50 different types of sarcomas and their mimics. The aim is for the computer to analyse the images of a tumour and suggest a list of the most likely diagnoses which prompts the pathologist to undertake only the relevant ‘specialist’ tests to confirm the diagnosis. This would significantly reduce the workload of pathologists and laboratory staff. This would give pathologists more time to think about the ‘difficult’ diagnoses, as well as to train the next generation of pathologists. AI would also provide pathologists to undertake more research with the aim of improving patient care and survival. 

Chordoma and AI 

We plan to include chordomas in our sarcoma AI research. Chordomas can look like other tumours, such as chondrosarcoma, another bone tumour, and also like different types of cancers, particularly cancer of the kidney. The long-term aim is to collect the images of chordoma from around the UK from the last 40 years or so and to build a library which could be used to train a computer model to help pathologists make the diagnosis of chordoma correctly but also to identify if there are subgroups of chordoma that could benefit from different treatments. 

What is required for training a computer to suggest safe diagnoses? 

For AI to be successful it requires thousands of slides to train the computer. Experience in the field of AI has demonstrated that the larger the dataset, the more accurate the model. 

What will we do with the UKRI funding for ‘Classifying Sarcomas’? 

With the UKRI and other funds awarded we will collect slides from about 50,000 patients with different sarcoma types including chordoma. Because sarcomas are so rare it is necessary to collect the slides from all over the UK and beyond from the last few decades. We will work with AI/ Computer Scientists to train the computer to recognise the different types of sarcoma types and their mimics. For a pathologist to provide a correct diagnosis it is often essential to know information about a patient and the tumour. For example, some tumours only occur in children and nearly all chordomas are present in the spine - having this type of information is important when training the computer. 

Privacy and Trust in AI models for sarcoma 

To ensure that details of patients and their families remain private but at the same time to allow research to be undertaken, personal details of patients will be ‘hidden’/ anonymised to researchers. For AI to be used in clinical practice, both patients and doctors must trust the technology. This trust can be gained first by the rigorous training and testing of these AI models prior to use in ‘the real world’, that is, being used in clinical practice. Furthermore, it is important that the pathologist will ultimately be in the ‘driver’s seat’ and the final diagnosis will rest on them. The pathologist will not be replaced by AI, instead they will use it as a tool to increase efficiency and diagnostic accuracy. 

What is next? 

To ensure that trust between the patient, the medical and the scientific community is developed; we would like to know your views. There will be patient representation on the Steering Committee of this project to ensure that we hear about patients’ views and their concerns. We will listen and respond to them. This information from patients, their partners and their children can be fed back through the Chordoma UK community.
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There is little funding available for research into this relentless bone cancer mainly because it is relatively rare, occurring in only 1 in 800,000 of the population. Without a focused approach to raising money for research into chordoma, little will change quickly.

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