“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma, who currently have poor outcomes. The disease is very rare - clinicians may only see one or two cases in their career, which means diagnosis can be slow. This type of sarcoma is also difficult to treat as it can grow to large sizes and, due to the tumour’s location in the abdomen, involve complex surgery,” said first author Amani Arthur, Registrar at The Royal Marsden NHS Foundation Trust and Clinical Research Fellow at The Institute of Cancer Research, London.

Researchers used the CT scans of 170 patients with the two most common forms of retroperitoneal sarcoma -- leiomyosarcoma and liposarcoma -- to create an AI algorithm, which was then tested on nearly 90 patients from centres across Europe and the US. They used a technique called radiomics to analyse the CT scan data, which can extract information about the patient’s disease from medical images, including data which can’t be distinguished by the human eye.

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The model accurately graded the risk -- or how aggressive a tumour is likely to be -- of 82 per cent of the tumours analysed, while only 44 per cent were correctly graded using a biopsy. The model also accurately predicted the disease type of 84 per cent of the sarcomas tested -- meaning it can effectively differentiate between leiomyosarcoma and liposarcoma -- compared with radiologists who were not able to diagnose 35 per cent of the cases.

The study suggests that the technology could help clinicians diagnose subtypes of the rare disease, speeding up diagnosis as a result. Researchers believe the technique could be eventually applied to other cancer types too, potentially benefiting thousands of patients every year.

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