Child brain tumors can be classified by advanced imaging and AI

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Child brain tumors can be classified by advanced imaging and AI

Press releases may be edited for formatting or style | February 15, 2021 Artificial Intelligence MRI Pediatrics
Diffusion weighted imaging and machine learning can successfully classify the diagnosis and characteristics of common types of paediatric brain tumours a UK-based multi-centre study, including WMG at the University of Warwick has found. This means that the tumour can be characterised and treated more efficiently.

The largest cause of death from cancer in children are brain tumours in a particular part of the brain, called the posterior fossa. However, within this area, there are three main types of brain tumour, and being able to characterise them quickly and efficiently can be challenging.

Currently a qualitative assessment of MRI by radiologists is used, however overlapping radiological characteristics can make it difficult to distinguish which type of tumour it is, without the confirmation of biopsy. In the paper, ‘Classification of paediatric brain tumours by diffusion weighted imaging and machine learning’, published in the journal Scientific reports, led by the University of Birmingham including researchers from WMG, University of Warwick. The study found that tumour diagnostic classification can be improved by using non-invasive diffusion weighted imaging, when combined with machine learning (AI).

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Diffusion weighted imaging involves the use of specific advanced MRI sequences, as well as software that generates images from the resulting data that uses the diffusion of water molecules to generate contrast in MR image. One can then extract an Apparent Diffusion Coefficient (ADC) map, analysed values of which can be used to tell you more about the tumour.

The study involved 117 patients from five primary treatment centres across the UK with scans from twelve different hospitals on a total of eighteen different scanners, the images from them were then analysed and region of interests were drawn by both an experienced radiologist and an expert scientist in paediatric neuroimaging. Values from the analysis of Apparent Diffusion Coeffcient maps from these images’ regions have been fed to AI algorithms toProfessor Theo Arvanitis from the Institute of Digital Healthcare at WMG, University of Warwick successfully discriminate the three most common types of paediatric posterior fossa brain tumours, non-invasively.

Professor Theo Arvanitis, Director of the Institute of Digital Health at WMG, University of Warwick and one of the authors of the study explains:

“Using AI and advance Magnetic Resonance imaging characteristics, such as Apparent Diffusion Coefficient (ADC) values from diffusion weighted images, can potentially help distinguish, in a non-invasive way, between the main three different types of paediatric tumours in the posterior fossa, the area of the brain where such tumours are most commonly found in children.

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