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Abstract #4624

Machine-Learning Segmentation and Classification of Pediatric Brain Tumors Based on Preclinical Multiparametric Advanced Fast Imaging (MAFI)

Marina Stukova1, Samuel Henehan2, Jenna Steiner2, Alaina Marquardt2, and Natalie Julie Serkova3
1Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, 2University of Colorado Anschutz, Aurora, CO, United States, 3Radiology, University of Colorado Anschutz, Denver, CO, United States

Brain tumors are the second most common malignancy in childhood (exceeded only by leukemia). Clinically, multiparametric MRI is now considered to be the neuroimaging standard for detecting brain tumors. Pediatric brain tumors have a diverse array of clinical manifestations, cellular and molecular phenotypes, and tumor habitats. Previously, we reported on the initial development of a non-gadolinium, Multiparametric Advanced Fast Imaging (MAFI) protocol at the 9.4 Tesla in patient derived xenografts models (PDX, ISMRM 2019). Here, we expanded our study to include all existing pediatrics PDX mouse models for machine learning based segmentation and classification of four major brain tumor subtypes.

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