Image-based systems biology is emerging as a powerful new paradigm in cancer research. However, there is an exigent need for a new image-based systems biology framework to better understand the role of the vasculature in cancer progression, metastasis and therapeutic response. In spite of recent advances in vascular imaging, there is a dearth of methods that make blood vessels simultaneously visible in the primary imaging modalities, i.e. magnetic resonance imaging (MRI), computed tomography (CT) and optical microscopy. This poses a hurdle to the integration of vascular data across multiple spatial scales or with tumor microenvironment (TME) data acquired using complementary image contrast mechanisms,such as white matter fiber from MRI (~40μm), bone contrast from CT (9μm), protein expression from multiphoton (MPM) (<1μm), all of which can function as invaluable inputs to image-based systems biology models. Moreover, since their spatial resolution span several orders of magnitude, integrating these data via conventional image co-registration approaches also remains a challenge. Therefore, we developed a “multicontrast” vascular contrast agent combination that makes vasculature visible in MRI, CT and optical imaging. This vascular contrast agent combination does not hinder conventional imaging contrast mechanisms and also provides ‘internal’ fiducials that enable co-registration of these multiscale, multimodality imaging data. Moreover, the high-fidelity 3D microvascular data generated from this imaging pipeline can be combined with computational hemodynamic modeling to simulate functional TME properties (e.g. tumor blood flow). We demonstrate the utility of our pipeline by conducting multiscale, multimodality imaging of the TME in an orthotopic breast cancer model. Finally, our imaging pipeline has direct applications in image-based systems biology of healthy tissues, and in other disease models involving the vasculature (e.g. stroke and traumatic brain injury).