Waterloo: Researchers at the University of Waterloo have developed a computational model to better accurately anticipate the emergence of lethal brain tumours.
Glioblastoma multiforme (GBM) is a type of brain cancer with a one-year survival rate. Because of its extraordinarily dense core, fast growth, and location in the brain, it is tough to cure. Estimating the diffusivity and proliferation rate of these tumours is useful for clinicians, but this information is difficult to estimate for an individual patient fast and accurately.
Researchers at the University of Waterloo and the University of Toronto have partnered with St. Michael’s Hospital in Toronto to analyze MRI data from multiple GBM sufferers. They’re using machine learning to fully analyze a patient’s tumour, to better predict cancer progression.
Researchers analysed two sets of MRIs from each of five anonymous patients suffering from GBM. The patients underwent extensive MRIs, waited several months, and then received a second set of MRIs. Because these patients, for undisclosed reasons, chose not to receive any treatment or intervention during this time, their MRIs provided the scientists with a unique opportunity to understand how GBM grows when left unchecked.
The researchers used a deep learning model to turn the MRI data into patient-specific parameter estimates that inform a predictive model for GBM growth. This technique was applied to patients’ and synthetic tumours, for which the true characteristics were known, enabling them to validate the model.
“We would have loved to do this analysis on a huge data set,” said Cameron Meaney, a PhD candidate in Applied Mathematics and the study’s lead researcher, adding, “Based on the nature of the illness, however, that’s very challenging because there isn’t a long life expectancy, and people tend to start treatment. That’s why the opportunity to compare five untreated tumours was so rare – and valuable.”
Now that the scientists have a good model of how GBM grows untreated, their next step is to expand the model to include the effect of treatment on the tumours. Then the data set would increase from a handful of MRIs to thousands.
Meaney emphasizes that access to MRI data – and partnership between mathematicians and clinicians – can have huge impacts on patients going forward.
“The integration of quantitative analysis into healthcare is the future,” Meaney said.