Our study has some potential limitations. This retrospective study relied on publicly available imaging data acquired across multiple clinical sites. Thus there were some variations in imaging acquisition/sequence parameters (e.g., Relaxation Time and Echo Time) and hardware (e.g., magnetic field strength and receiver coil geometry). In order to mitigate these effects, and in accordance with standard practice, intensity normalization and registration were applied to each image prior to habitat analysis. However, any remaining intensity variations could have potentially affected initial tumor segmentation. A systematic study with standardized image acquisition protocols is needed in order to validate the robustness of these imaging-derived habitat characteristics. Another aspect of uncertainty in this study is derived from the patient outcome data. Because these patients were treated at different institutions, aspects of their treatment regimens may not have been fully standardized, which could potentially affect patient OS duration. Additionally, an inherent challenge with all genomic information derived from tumor specimens is the inability to account for spatial heterogeneity of genomic alterations and tumor cell clones. As with the quality of the imaging data described above, the robustness of the genomic data is also subject to similar uncertainties that can influence the overall generalizability of our conclusions. Also, the presented relationships between habitats and genetic pathway alterations are inferred based on statistical regression methods. These findings could potentially form the basis for targeted perturbation experiments in-vivo to illuminate the mechanistic or causative nature of the relationships between habitats and pathway activity. While outside the scope of this study, research efforts in this direction [27, 28] are essential before these associations can be exploited therapeutically. However, the process of habitat inference and its association mining for prognostic intent can be done quite readily in the current clinical scenario since these 4 MR sequences (T1, T1c, T2, FLAIR) are used routinely.