Summary: Combining machine learning technology with neuroimaging data, clinicians will be better able to fully analyze a patient's glioblastoma brain tumor and predict cancer progression. 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.

Waterloo [Canada], January 16 (ANI): 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.