Researchers use AI to enhance and personalise brain tumour imaging

The research found the tool can analyse scans in just three seconds—compared with five minutes needed by experienced neuroradiologists—and could be used to more accurately predict an individual patient’s treatment outcome than with a doctor alone.

A workforce analysis forecast that if deployed nationally, it could save more than £1.5million in NHS costs within the next three years.

Approximately 100,000 people in the UK live with brain tumours, and are both diagnosed and monitored following treatment by scans of their brain. Just as individual patients are unique, so too are the imaging appearances of their tumours. Current NHS care for brain tumours addresses these differences by having specialist imaging doctors (radiologists) analyse the images to assess and describe where the tumour is, its approximate size, and its proximity to vital healthy brain structures.

The research, published in the journal NeuroImage: Clinical, shows that the AI-based tool can perform this analysis effectively and efficiently. In a large group of 1172 patients, the UCLH and UCL Queen Square Institute of Neurology team also showed the tool is accurate across patients of all ages and sexes.

Lead author Dr James Ruffle (Clinical Research Fellow, Brain Repair & Rehabilitation, UCL Queen Square Institute of Neurology) said: “Given that the imaging appearances of an individual’s brain tumour vary greatly from one patient to another, artificial intelligence technologies provide an innovative solution to enhance healthcare workers’ data-driven decision making, improving and personalising care for each individual affected, and at near-zero additional cost to the NHS – and with savings to the NHS over the medium term.”

Senior author Dr Harpreet Hyare, Lead Teenage and Young Adult Neuroradiologist at UCLH, said: “This work shows real promise in enabling neuroradiologists to provide accurate and quantified descriptors of brain tumours. Thanks to an Innovation Fund award from The National Brain Appeal, we now look to translate this tool into clinical practice. By providing objective quantitative assessments of different brain tumour components that can be monitored over time, we can enable clinicians to plan treatment more effectively at critical time points.”

The AI-based imaging tool is one of several the team are currently developing specifically for neuro-oncology applications that they hope to bring into the clinical arena in the near future.

The research was funded by the Medical Research Council, the Wellcome Trust, the NIHR UCLH Biomedical Research Centre, with the translational work being funded by The National Brain Appeal.

Links

• Ruffle, et al VASARI-auto: Equitable, efficient, and economical featurisation of glioma MRI, NeuroImage: Clinical, Volume 44, 2024, https://doi.org/10.1016/j.nicl.2024.103668.
• Dr James Ruffle's academic profile

Source

• NIHR UCLH Biomedical Research Centre