(US and Canada) Researchers at the University of Waterloo have created a computational model to accurately predict the growth of deadly brain tumors. Experts at the University of Waterloo and the University of Toronto partnered with St. Michael’s Hospital in Toronto to analyze MRI data from multiple Glioblastoma multiforme (GBM) patients and are using machine learning to analyze a patient’s tumor, to better predict cancer progression.
Glioblastoma multiforme (GBM) is a brain cancer with an average survival rate of only one year. As per experts, it is difficult to treat due to its extremely dense core, rapid growth, and location in the brain. Estimating these tumors’ diffusivity and proliferation rate is useful for clinicians, but the information is hard to predict for an individual patient quickly and accurately.
The research involved analysis of two sets of MRIs from each of five anonymous patients suffering from GBM. The patients chose not to receive any treatment or intervention during this time providing scientists with a unique opportunity to understand how GBM grows when left unchecked.
The researchers used a deep learning model to turn MRI data into patient-specific parameter estimates that inform a predictive model for GBM growth. The technique was applied to patients’ and synthetic tumors, 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. “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 tumors was so rare – and valuable.”
With a good model of how GBM grows untreated, the next step is to expand the model to include the effect of treatment on the tumors. This could expand the data set 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,” he added.