Computational oncology integrates mathematical modeling, medical imaging, and data analysis to improve the understanding and prediction of cancer progression and treatment response. A central mathematical challenge in this field is the rigorous description of transport phenomena operating across multiple spatial, temporal, and biological scales, ranging from molecular drug release and cellular uptake to tissue-level perfusion, tumor-scale dynamics, and whole-body system behavior. This talk presents a multiphysics, multiscale modeling framework based on coupled systems of differential equations for characterizing transport processes in oncology, with particular emphasis on biophysical transport mechanisms and their interactions across scales. The framework focuses on the formulation, analysis, and parameterization of models using patient-specific information derived from medical imaging, enabling mathematically consistent representations of individual tumor geometry, heterogeneity, and physiological constraints. Illustrative case studies highlight advanced mathematical modeling problems arising in the tumor microenvironment, chemotherapy and nanoparticle-based drug transport, and hybrid treatments. By systematically linking local transport mechanisms to system-level tumor behavior and incorporating patient-derived data, this framework advances toward patient-specific digital twins capable of supporting quantitative prediction and optimization of cancer therapies within a precision oncology framework.