Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity

Differences in the biologic distribution of genes and molecular subtypes in glioma and suggested analytic methods of imaging genomics. For genes and molecular subtypes that are stable and ubiquitous, imaging genomics using averaged values or a simple imaging phenotype such as T2-FLAIR mismatch sign is applicable. For genes and molecular subtypes with a skewed distribution or those that include different subtypes, a histogram analysis or supervised learning using AI or radiomics is applicable. For genes and molecular subtypes that are dynamic and heterogeneous, pattern-wise analysis using unsupervised learning or subregional analysis to explain intratumoral heterogeneity needs to be applied.

Illustration of the use of an unsupervised learning method and a subregional analysis to account for an EGFRvIII mutation. A, Histogram analysis of multiparametric MR imaging enables depiction of the differences between the EGFRvIII-mutant and wild group (reproduced with permission from Bakas et al⁵⁷). B, For EGFRvIII-mutant tumors, there is no heterogeneity in the perfusion pattern distribution between the far and near ROIs, while there is considerable heterogeneity between the far and near ROIs for patients who have EGFRvIII– tumors (reproduced with permission from Akbari et al⁵⁶).