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Abstract #3250

AI-Driven Classification of Medulloblastoma Tumor Subtypes Using Novel Features Derived From Apparent Diffusion Coefficient MRI

Joseph Holtrop1, Silu Zhang1, Giles Robinson2, Amar Gajjar3, and Asim Bag1
1Department of Radiology, St Jude Children's Research Hospital, Memphis, TN, United States, 2Oncology, St Jude Children's Research Hospital, Memphis, TN, United States, 3Pediatric Medicine, St Jude Children's Research Hospital, Memphis, TN, United States

Synopsis

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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