Deep learning model can accurately predict the growth of deadly brain tumors

Researchers at the University of Waterloo have created a computational model to predict the growth of deadly brain tumors more accurately.

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 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.

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.

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 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. 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."

Cameron Meaney, PhD Candidate in Applied Mathematics and Study's Lead Researcher

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.

Source:
Journal reference:

Meaney, C., et al. (2022) Deep learning characterization of brain tumours with diffusion weighted imaging. Journal of Theoretical Biology. doi.org/10.1016/j.jtbi.2022.111342.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoLifeSciences.
Post a new comment
Post
Azthena logo

AZoM.com powered by Azthena AI

Your AI Assistant finding answers from trusted AZoM content

Your AI Powered Scientific Assistant

Hi, I'm Azthena, you can trust me to find commercial scientific answers from AZoNetwork.com.

A few things you need to know before we start. Please read and accept to continue.

  • Use of “Azthena” is subject to the terms and conditions of use as set out by OpenAI.
  • Content provided on any AZoNetwork sites are subject to the site Terms & Conditions and Privacy Policy.
  • Large Language Models can make mistakes. Consider checking important information.

Great. Ask your question.

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Astrocyte-Rich Brain Organoid Model Unveils Insights into Neuroinflammation