Over the past decades, imaging in oncology has been undergoing a “quiet” revolution to treat images as data, not as pictures. This revolution has been sparked by technological advances that enable capture of images that reflect not only anatomy, but also of tissue metabolism and physiology in situ. Important advances along this path have been the increasing power of magnetic resonance imaging (MRI), which can be used to measure spatially dependent differences in cell density, tissue organization, perfusion, and metabolism. In parallel, positron emission tomography (PET) imaging allows quantitative assessment of the spatial localization of positron emitting compounds, and it has also been constantly improving in the number of imageable tracers to measure metabolism and expression of macromolecules. Recent years have witnessed another technological advance, wherein these two powerful modalities have been physically merged into combined PET/MRI systems; appropriate for both pre-clinical or clinical imaging. As with all new enabling technologies driven by engineering physics, the full extent of potential applications is rarely known at the outset. In the work of Schmitz et al. in this volume, the authors have combined multiparametric MRI and PET imaging to address the important issue of intratumoral heterogeneity in breast cancer using both pre-clinical and clinical data. With combined PET and MRI and sophisticated machine learning tools, they have been able identify multiple co-existing regions (“habitats”) within living tumors and, in some cases, have been able to assign these habitats to known histologies. This work addresses an issue of fundamental importance to both cancer biology and cancer care. As with most new paradigm shifting applications, it is not the last word on the subject, and introduces a number of new avenues of investigation to pursue.