Leveraging Existing Pediatric Low-Grade Tumor Specimens, Clinical and Imaging Outcome Data, and Artificial Intelligence Innovations to Develop Integrated Biomarkers of Response for Children With Low-Grade Glioma

The overall hypothesis is that by using state of the art molecular profiling and artificial intelligence (AI) methods, we will develop imaging biomarkers that predict underlying subtype and response to therapies for pediatric low grade glioma (pLGG). The knowledge gap that will be addressed is our current limited understanding of integrated imaging and molecular characteristics; such increased insights will allow us to better match an individual child’s tumor to a specific therapy.