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A new computational model can now predict lethal brain tumour growth with accuracy, a research from Canada.
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Researchers at the University of Waterloo, Canada, have created a model that will measure the growth of deadly brain tumours and predict its accuracy.
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The research was published in the Journal of Theoretical Biology under the title "Deep learning characterization of brain tumours with diffusion-weighted imaging."
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The research will help in estimating the tumour diffusivity and proliferation rate for neurosurgeons in the case of glioblastoma multiforme (GBM).
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GBM is difficult to treat due to its extremely dense core, rapid growth, and location in the brain, like other lethal brain cancer tumours.
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St. Michael's Hospital in Toronto collaborated with researchers from the Universities of Waterloo and Toronto to analyse MRI data from GBM patients, whose average survival time is just one year.
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During the research, GBM patients underwent two sets of extensive MRIs where they chose not to undergo GBM treatment or any kind of intervention.
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Here, scientists used machine learning to fully analyse a patient’s tumour to better predict cancer progression.
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These MRIs provided the scientists with a unique opportunity to understand how GBM grows when left untreated.
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A deep learning model was used to turn the MRI data into patient-specific parameters for GBM growth prediction.
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Cameron Meaney, the study’s lead researcher, says it was challenging to study growth prediction for GBM tumours as there isn't a long life expectancy.
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The next step in this study will be to expand the model to include the effect of treatment on the tumours.
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"The integration of quantitative analysis into healthcare is the future," says Cameron Meaney.
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Source- News Medical