Machine learning, radiomics differentiates glioma

An automated method based on a machine-learning algorithm and MRI radiomics can differentiate between low-grade and high-grade gliomas, according to research presented at the annual Society for Imaging Informatics in Medicine (SIIM) conference in Kissimmee, FL. After developing a workflow to support it, researchers from Yale School of Medicine created an automated approach that segments gliomas on brain MR exams, performs radiomics analysis, and then predicts if the tumor is high or low grade. In testing, their approach yielded an area under the curve (AUC) of 0.86. "We were able to develop a PACS-based auto-segmentation tool, which was linked to a high- versus low-grade glioma prediction tool," said Sara Merkaj, a postgraduate research fellow. "This algorithm could potentially be incorporated into clinical practice."