MR-guided Non-invasive Brain Glioma Typing
Abstract
This study presents a machine learning approach for non-invasive glioma typing using MRI data. We develop classifiers that can predict tumor molecular subtypes from imaging features, supporting clinical decision-making in neurosurgery.
Clinical Applications
- Pre-surgical planning: Better informed treatment strategies
- Non-invasive subtyping: Molecular classification without biopsy
- Prognosis prediction: Early outcome estimation
Related Topics
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