Genetic Mutation Identification in Brain Tumors

This lecture provides an in-depth overview of how advanced AI-driven neuroimaging is transforming the pre-operative classification of brain tumors, specifically gliomas and meningiomas. The lecture demonstrates how machine learning and deep learning models can non-invasively identify critical molecular biomarkers—such as IDH and TERT mutations in gliomas or NF2 loss and S100 expression in meningiomas—directly from standard and advanced MRI modalities. The lecture underscores the necessity of multi-modal data fusion while critically examining the hurdles of real-world clinical translation, including data heterogeneity, the "black box" nature of AI models, and the vital importance of external validation across multi-center datasets.

Learning objectives

By the end of this lecture, students will be able to:

• Understand the diagnostic gain achieved by synthesizing disparate imaging features—such as perfusion, metabolic spectroscopy, and susceptibility-weighted imaging (SWI)—rather than relying on single-modality reads.
• Understand  the workflow of using radiomic scores and pre-trained deep learning models (e.g., ResNet, EfficientNet) to predict clinical outcomes and survival trajectories
• Analyze the challenges inherent in implementing AI software in clinical environments, specifically addressing generalization, data imbalance, ethical data privacy, and the need for explainable AI (XAI)
• Utilize visual explanation techniques like Grad-CAM to ensure that AI models are focusing on biologically relevant structures (e.g., specific metabolites, necrotic tissue, or edema) rather than noise.