Keywords: Radiomics, Radiomics, Diagnosis/Prediction, Tumors
Motivation: Identifying medulloblastoma subtypes is critical for effective treatment, as different subtypes exhibit unique prognostic profiles.
Goal(s): This study aims to develop a novel set of features derived from ADC MRI images that can be used by a machine learning model to classify medulloblastoma subtypes.
Approach: Using retrospective MRI data from medulloblastoma patients, we extracted ADC-based features and trained a classifier to distinguish among medulloblastoma subtypes.
Results: Our developed features found significant relationships between the different subtypes and were able to independently predict tumor subtype with 68% accuracy.
Impact: This AI-based approach could enable early, non-invasive classification of medulloblastoma subtypes, aiding personalized treatment planning. It provides novel features that have interoperable meaning. This work adds another tool to aid in medulloblastoma tumor classification.
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