An international team of researchers led by Dr. Murat Günel, Chair of Neurosurgery at Yale School of Medicine, and Nixdorff-German Professor of Neurosurgery, recently developed a machine learning model that uses clinically relevant features of tumor location and tumor volume to differentiate lower-grade glioma and glioblastoma.
The model uses complex mathematics to determine how various types of brain tumors look in the brain. Based on that, it will help doctors diagnose the stage of brain cancers faster and more accurately.
Scientists tested their model on 229 patients with brain tumors. Using the model, they extracted tumor location and tumor volume features.
Hang Cao, a medical student from Xiangya Hospital and lead author of the study, said, “Our machine learning models used to differentiate the tumor types were very accurate.”
For the study, the data was compiled from a public tumor machine resonance imaging (MRI) database called The Cancer Imaging Archive. Board-certified neuro-radiologists then identified and selected glioma cases, which the researchers used for their model.
Scientists detected 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.
Dr. Jennifer Moliterno, Assistant Professor of Neurosurgery at Yale School of Medicine and Clinical Program Leader of the Brain Tumor Program said, “This work is fundamentally important to our understanding of brain tumors and a great example of the collaborative, multidisciplinary effort we use to advance the field and provide the best care to brain tumor patients.”
- A quantitative model based on clinically relevant MRI features differentiates lower-grade gliomas and glioblastoma. DOI: 10.1007/s00330-019-06632-8