Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes
This work addresses the challenge of non-invasively classifying glioblastoma subtypes for medical diagnosis, though it is incremental as it builds on existing radiomics and genomics methods.
The study tackled the problem of identifying molecular subtypes of glioblastoma by analyzing radiomics features from tumor subregions, finding that fractal dimensions significantly differ between subtypes and achieving an average accuracy of 79% for subtype prediction using radiomics.
Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture features from the tumor subregions to identify the correlations with gene expression features and overall survival. Consequently, we utilize the radiomics to find associations with the subtypes of glioblastoma. Accordingly, the fractal dimensions of the whole tumor, tumor core, and necrosis regions show a significant difference between the Proneural, Classical and Mesenchymal subtypes. Additionally, the subtypes of GBM are predicted with an average accuracy of 79% utilizing radiomics and accuracy over 90% utilizing gene expression profiles.