Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is an aggressive brain cancer with a poor prognosis, necessitating improved diagnostic and treatment strategies. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) like U-Net, ResNet, and EfficientNet, applied to multi-modal magnetic resonance imaging (MRI) data for accurate tumor segmentation, classification (including grading and molecular subtyping), and prediction of treatment response and survival. These AI-driven approaches aim to improve the accuracy and speed of diagnosis, personalize treatment planning (e.g., predicting MGMT promoter methylation status), and ultimately enhance patient outcomes by enabling earlier and more effective interventions.