Artificial intelligence: improving sarcoma diagnosis and treatment

Researchers at The Royal Marsden have found that artificial intelligence (AI) could be around twice as accurate as a biopsy at grading the aggressiveness of some sarcomas.

Results from the RADSARC-R study, which was supported by funding from The Royal Marsden Cancer Charity, suggest that a new AI algorithm could:

  • tailor treatment of some sarcoma patients more accurately and effectively than a biopsy – an invasive procedure which is currently standard practice
  • 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.

What did the study focus on?

Soft tissue sarcoma is a rare type of cancer that develops in the body’s connective tissues, including fat, muscle, nerves and blood and lymph vessels. There are over 50 different types of soft tissue sarcoma and this study focused on retroperitoneal sarcoma, which develops in the back of the abdomen. Due to its location and rarity, it is currently hard to diagnose and treat.

A Paediatric Registrar smiling and standing in a hospital corridor, wearing a smart shirt with an ID badge and stethoscope around her neck
Dr Amani Arthur at The Royal Marsden

“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma,” said first author Dr Amani Arthur, Registrar at The Royal Marsden and Clinical Research Fellow at The Institute of Cancer Research, London.

“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.”

Gathering vital information using AI

As part of the RADSARC-R study, researchers used the CT scans of 170 patients at The Royal Marsden with the two most common forms of retroperitoneal sarcoma – leiomyosarcoma and liposarcoma – to create an AI algorithm. This was then tested on patients from centres across Europe and the US, in collaboration with international colleagues.

They used a technique called radiomics to analyse the CT scan data, which can uncover information about the patient’s disease, including that which can’t be detected by the human eye. The model accurately graded the risk (how aggressive a tumour is likely to be) of 82% of the tumours analysed, while only 44% were correctly graded using a biopsy.

By giving clinicians a more accurate and effective way of grading tumours, researchers hope to improve the treatment of patients:

  • High-grade tumours can indicate aggressive disease, and this tool could help ensure high-risk patients are identified and get urgent treatment.
  • Low-risk patients could be spared unnecessary treatments, follow-up scans and hospital visits.
  • It could also speed up diagnosis by supporting clinicians – who may never have previously seen a retroperitoneal sarcoma due to its rarity – in more accurately identifying the subtype.

“We’re incredibly excited by the potential of this state-of-the-art technology”

A Consultant Radiologist in a smart blue dress, smiling and standing in front on an MRI machine in a large hospital room
Consultant Radiologist Dr Christina Messiou

 “Through this early research, we’ve developed an innovative AI tool using imaging data that could help us more accurately and quickly identify the type and grade of retroperitoneal sarcomas than current methods,” says Dr Arthur. “In the next phase of the study, we will test this model in clinic on patients with potential retroperitoneal sarcomas to see if it can accurately characterise their disease and measure the performance of the technology over time.”

“This is the largest and most robust study to date that has successfully developed and tested an AI model aimed at improving the diagnosis and grading of retroperitoneal sarcoma using data from CT scans” added study lead Professor Christina Messiou – Consultant Radiologist at The Royal Marsden and Professor in Imaging for Personalised Oncology at The Institute of Cancer Research, London.

“Due to the rarity of the disease, this was a global effort and I’m immensely proud and thankful to the team. We’re incredibly excited by the potential of this state-of-the-art technology, which could lead to patients having better outcomes through faster diagnosis and more effectively personalised treatment.”

“As patients with retroperitoneal sarcoma are routinely scanned with CT, we hope this tool will eventually be used globally, ensuring that not just specialist centres – who see sarcoma patients every day – can reliably identify and grade the disease.”

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