Machine learning can help predict the growth of deadly brain tumours more accurately as a result of a new breakthrough.
Glioblastoma multiforme (GBM) is a particularly aggressive brain cancer with an average survival rate of only one year. It is difficult to treat due to its extremely dense core, rapid growth and location in the brain.
Efforts have long been underway to make a breakthrough in this area, and estimating these tumours’ diffusivity and proliferation rate remains hard to predict for an individual patient quickly and accurately.
Now, researchers at the University of Waterloo and the University of Toronto have partnered with St Michael’s Hospital in Toronto to analyse MRI data from multiple people with GBM.
They are using machine learning to fully analyse a patient’s tumour, to better predict cancer progression.
Researchers analysed two sets of MRIs from each of five anonymous patients with 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 research 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.
“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 emphasises 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.
