Recent advances in digital pathology have led to an explosion in high-content multidimensional imaging approaches. Yet, our ability to gainfully process, visualize, integrate and mine the resulting mass of bioimaging data remains a challenge. We have developed Hourglass, an open access user-friendly software that streamlines complex biology-driven post-processing and visualization of multiparametric data. Directed at datasets derived from tissue microarrays or imaging methods that analyze multiple regions of interest per patient specimen, Hourglass systematically organizes observations across spatial and global levels as well as within patient subgroups. Application of Hourglass to our large and complex pancreatic cancer bioimaging dataset (540,617 datapoints derived from 26 bioimaging analyses applied to 596 specimens from 165 patients) consolidated a breadth of known IL-6 functions in a well-annotated human pancreatic cancer cohort and uncovered new unprecedented insights into a sex-linked Interleukin-6 (IL-6) association with immune phenotypes. Specifically, regional effects of IL-6 on the intratumoral T cell response were restricted to male patients only. In conclusion, Hourglass facilitates multi-layered knowledge extraction from complex multiparametric bioimaging datasets and provides tailored analytical means to productively harness heterogeneity at the sample and patient level.