Statistical analysis was performed using R statistical software (R Foundation, Vienna, Austria) with the “survival” (v 2.38-2), “randomForestSRC” (v 2.0.5), and “DirichletReg” (v 0.6-3) packages. Random survival forest (RSF) regression was applied to the imaging habitat proportions to determine if any habitats were associated with OS [16, 38, 39]. The random forest regression identified a subset of habitats that were deemed “important.” Cox proportional hazard regression analysis was then used to determine the association of these (RSF-derived) important habitats with OS (p-values less than 0.05) [40–42], after adjusting for clinical covariates (age, volume, karnofsky score: KPS, IDH1 mutation). Image habitats that were deemed important based on random survival forest regression, and significant from the Cox proportional hazard regression were designated “relevant”. The process of obtaining relevant habitats is depicted in Figure 2. These relevant habitats were then correlated with BraTumIA-derived tumor volume segmentations (edema, necrosis, enhancing regions, and non-enhancing regions) via pair-wise Spearman rank correlation tests. In addition, Dirichlet regression was applied to each relevant habitat to determine if that habitat was associated with pathway activation (PARADIGM) scores. This analysis generated molecular signatures associated with each relevant imaging habitat. The significance of the Dirichlet regression for each pathway was determined by calculating a Bonferroni-adjusted P value, and the strength of association was determined from the absolute value of the Dirichlet regression coefficient.