Radiogenomics Signatures in Key Driver Genes in Glioblastoma Evaluated With And Without the Presence of Co-occuring Mutations

Published in
Anahita Fathi Kazerooni, Hamed Akbari, Spyridon Bakas, Erik Toorens, Chiharu Sako, Elizabeth Mamourian, Vikas Bommineni, Nina Thakur, Costas Koumenis, Stephen J Bagley, Robert A Lustig, Donald M O’Rourke, Tapan Ganguly, MacLean Nasrallah, Christos Davatzikos
Default publication image


Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations.