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

Email Principal Investigator
Default project image

Benjamin H. Kann

Harvard Medical School
Boston, MA


About this


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.

Ask The


Ask the scientists

What are the goals of this project?

To develop and validate (a) imaging-based correlates for multi-omic LGG/BRAF signatures, and (b) prognostic radiomic and radiogenomic biomarkers for progression and survival. We will develop non-invasive, imaging-based signatures to predict underlying BRAF-alterations and molecular subtype. We will determine if radiomic signatures can predict tumor aggressiveness, progression and survival in pLGG.

Explore the data in these informatics portals