Deep Learning and Radiomics: A Game-changer for Identifying Glioblastoma and Brain Metastases

According to a recent study from Karl Landsteiner University of Health Sciences (KL Krems), using radiomics and deep learning algorithms can quickly and accurately distinguish between glioblastoma (primary tumors) and brain metastases. The study, which was published in Metabolites, discovered that magnetic resonance-based radiological data of tumor oxygen metabolism provide a solid foundation for discrimination via neural networks. This combination of oxygen metabolic radiomics and AI analysis was discovered to be vastly superior to human expert evaluations in all critical criteria, even when essential oxygen values did not differ significantly between tumor types. The neural networks' ability to make clear distinctions based on these values demonstrates the method's potential. Glioblastoma (GB) and brain metastasis (BM) are the most commonly occurring types of brain tumors in adults.