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Details of Grant 

EPSRC Reference: EP/Y020030/1
Title: AI-based diagnosis for improving classification of bone and soft tissue tumours across the UK
Principal Investigator: Collins-Fekete, Dr C
Other Investigators:
Chakraborty, Dr T Royle, Professor GJ Treanor, Dr D
Flanagan, Professor AM
Researcher Co-Investigators:
Dr M Simard
Project Partners:
Belfast Health and Social Care Trust Bone Cancer Research Trust Cambridge University Hospitals Trust
Chordoma UK Christie NHS Foundation Trust Great Ormond Street Hospital
Institute of Cancer Research Manchester University NHS Fdn Trust Newcastle upon Tyne Hosp NHS Fdn Trust
North Bristol NHS Trust Nottingham University Hospitals Oxford Uni. Hosps. NHS Foundation Trust
Robert Jones & Agnes Hunt Orth NHS FT Royal Infirmary of Edinburgh Royal National Orthopaedic Hosp NHS Tr
Royal Orthopaedic Hospital NHS Fdn Trust Sarcoma UK Sheffield Teaching Hospitals NHS Trust
Swansea Bay University Health Board The Royal Marsden NHS Foundation Trust UCL Hospitals NHS Foundation Trust
Uni Hospital Southampton NHS Fdn Trust University College Dublin
Department: Medical Physics and Biomedical Eng
Organisation: UCL
Scheme: Standard Research - NR1
Starts: 02 October 2023 Ends: 01 April 2025 Value (£): 613,172
EPSRC Research Topic Classifications:
Artificial Intelligence Medical Imaging
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
11 Jul 2023 Artificial intelligence innovation to accelerate health research Expert Panel Announced
07 Jun 2023 Artificial intelligence innovation to accelerate health research Sift Panel D Announced
Summary on Grant Application Form
Delivery of pathology tissue diagnoses, most of which are cancer, in the current format is unsustainable. Advances in genomic medicine and immune-oncology have shown that the classification of tumours into subtypes allows selection of patients for specific treatments but also spares patients unnecessary toxic and expensive therapies. Still, making such diagnoses has become more time-consuming, involving the selection and interpretation of ancillary tests which requires an ever-growing specialist knowledge for each cancer type. Whilst the need for diagnostic expertise is increasing, there is already a shortfall of 25% of pathologists who are able to report results: this is set to decline.

We propose that the use of AI can ensure that the delivery of tissue diagnoses by pathologists is sustainable and supports delivery of personalised treatments. The benefits of AI in pathology are beginning to be seen, e.g. identification of high-grade areas of prostate cancer shows a reduction in errors and pathologists' time. The development of AI for diagnoses is timely as full adoption of digitised histological images, allowing them to be interrogated by both humans and artificial intelligence (AI), is expected in the UK by 2025.

AI is a data-hungry process; it is unrealistic to provide 100,000s images that are required to train a model. Even the most common cancers (e.g. breast) have multiple subtypes; identification of these is required for selection of patients for personalised treatments. To address this challenge, we propose to develop a novel AI strategy using a relatively small sample size (~1000 images per class). Such a model could be adapted to any cancer type. A multiple-instance learning framework will be developed, using transformers for feature extraction and classification. A tool that flags samples that cannot be confidently classified will be applied thereby alerting the pathologist of potentially unseen diseases. The deep learning model will be strengthened by the injection of pathologists' domain knowledge.

Soft tissue and bone tumours

We will develop the AI model on tumours of soft tissue (muscle, fat, blood vessels, etc.) and bone, an area considered to be one of the most challenging diagnostically. These tumours comprise approximately 100 different subtypes, and represent some of the most common cancers in children and young adults.

We will build on our existing deep learning model of 15 different subtypes trained on 2122 images, which predicts the correct diagnosis in 87% of cases. Selection of confirmatory ancillary tests is then prompted by the algorithm and streamlines the diagnostic pathway.

17,000 images that have already been scanned will be added to the library and allow the rapid development and extension of the classification model. The image library will be linked to clinical outcomes and expanded to 35,000 images during the project. Added to this is the commitment of the established Sarcoma Network of at least 20 pathologists from across all countries in the UK, to provide the additional 20,000 images mentioned above.

Additional benefits

The study and infrastructure will serve as the framework for the continued development of the model which can rapidly be expanded prospectively with the introduction of digital pathology in the NHS and globally. The model can be developed over time in response to new advances.

The image library will be available for training future pathologists, research, validation of other AI algorithms, and contribute to the Sarcoma Genomics England Clinical Interpretation Partnership (GeCIP) offering a valuable resource for future multi-modal multi-omic research.

Working closely with Sarcoma charities, and partners, we will involve and engage patients, their families, and the public, to build trust in the use of AI in health care.

Development of AI models for digitised pathology images can avert the crisis facing this medical specialty.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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