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Machine Learning Predicts Brain Tumor Progression

by Colleen Fleiss on Jan 16 2023 5:25 PM
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Researchers used a deep learning model to turn the MRI data into patient-specific parameter estimates that inform a predictive model for brain tumor growth.

Machine Learning Predicts Brain Tumor Progression
A new computational machine learning tool developed by researchers predicts the growth of deadly brain tumors more 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 tumor, to better predict cancer progression.

Brain Tumor: Facts and Figures

Glioblastoma multiforme (GBM) is a 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. Estimating these tumors’ diffusivity and proliferation rate is useful for clinicians, but that information is hard to predict for an individual patient quickly and accurately.

Researchers analyzed 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.

“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 longlife expectancy, and people tend to start treatment. That’s why the opportunity to compare five untreated tumors 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 tumors. 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.

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Source-Eurekalert


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