Skip to main content

CNS Tumors

0:00 / 0:00
Difficulty level
Advanced
Type
Duration
29:24

This lecture provides a comprehensive introduction to Central Nervous System (CNS) tumors, emphasizing the critical transition from purely histological assessments to integrated molecular diagnostics. It walks students through the 2021 WHO Classification (CNS5) and details the specific MRI protocols—including advanced physiological and metabolic techniques—necessary for tumor characterization, surgical planning, and differentiating treatment effects from recurrence.

Learning Objectives:
At the end of this lecture, students will be able to:

  • Classify CNS tumors into primary and secondary (metastatic) types and identify their prevalence across different age groups.
  • Explain the integrated diagnostic approach of the 2021 WHO CNS5 classification, highlighting the priority of molecular markers (e.g., IDH mutation).
  • Describe the BTIP (Brain Tumor Imaging Protocol) standards, distinguishing between minimum and ideal imaging protocols.
  • Analyze specific imaging markers, such as the T2-FLAIR mismatch sign and various enhancement patterns, to suggest tumor histology.
  • Evaluate the role of advanced techniques like Perfusion (DSC/DCE), MR Spectroscopy, and Amino Acid PET in distinguishing tumor progression from radiation necrosis.
Topics covered in this lesson
  • Overview of gliomas, meningiomas, and the high morbidity associated with malignant vs. non-malignant growths.
  • The shift toward molecularly defined entities and the "layered" diagnosis format.
  • Utilizing T1 pre/post-contrast, T2/FLAIR, and DWI to assess cellularity, necrotic cores, and infiltrative margins.
  • Clinical applications of DSC Perfusion for blood volume mapping and DTI for white matter tractography during surgical planning.
  • The use of MR Spectroscopy (MRS), CEST (acidity/protein mapping), and Amino Acid PET (FET/MET/DOPA).
  • Emerging tools such as Optical Coherence Tomography (OCT) and AI-driven radiomics for automated segmentation and molecular prediction.
Back to the course