A new machine learning algorithm can predict which tumors were lower-grade gliomas or glioblastoma brain cancer with a high degree of accuracy. An estimated 18,000 people in the United States will die of brain and spinal cord tumors in 2020. To help doctors differentiate between the severity of cancers in the brain, an international team of researchers led by Dr. Murat Günel, Chair of Neurosurgery at Yale School of Medicine, and Nixdorff-German Professor of Neurosurgery, built a machine learning model that uses complex mathematics to learn how various types of brain tumors look in the brain. The model is designed to “learn” from this gathered data to make predictions and help doctors diagnose the stage of brain cancers faster and more accurately.
The team found significant differences in how the cancers looked, their volumes in various regions of the brain, and their locations. When taken together, the model could predict which tumors were lower-grade gliomas or glioblastomas with a high degree of accuracy.