Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time-space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node- and/or edge-valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre-clinical and clinical impact.