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).
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 to 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.
"If this advanced imaging technique, combined with AI technology, can be routinely enrolled into hospitals it means that childhood brain tumours can be characterised and classified more efficiently, and in turn means that treatments can be pursued in a quicker manner with favourable outcomes for children suffering from the disease."
Professor Andrew Peet, NIHR Professor in Clinical Paediatric Oncology at the University of Birmingham and Birmingham Children's Hospital adds: "When a child comes to hospital with symptoms that could mean they have a brain tumour that initial scan is such a difficult time for the family and understandably they want answers as soon as possible. Here we have combined readily available scans with artificial intelligence to provide high levels of diagnostic accuracy that can start to give some answers. Previous studies using these techniques have largely been limited to single expert centres. Showing that they can work across such a large number of hospitals opens the door to many children benefitting from rapid non-invasive diagnosis of their brain tumour. These are very exciting times and we are working hard now to start making these artificial intelligence techniques widely available."
Jan Novak, Niloufar Zarinabad, Heather Rose, Theodoros Arvanitis, Lesley MacPherson, Benjamin Pinkey, Adam Oates, Patrick Hales, Richard Grundy, Dorothee Auer, Daniel Rodriguez Gutierrez, Tim Jaspan, Shivaram Avula, Laurence Abernethy, Ramneek Kaur, Darren Hargrave, Dipayan Mitra, Simon Bailey, Nigel Davies, Christopher Clark, Andrew Peet.
Classification of paediatric brain tumours by diffusion weighted imaging and machine learning.
Scientific Reports volume 11, Article number: 2987, 2021. doi: 10.1038/s41598-021-82214-3
