Integrating imaging and mathematical modeling to predict and optimize patient outcomes in oncology

Biology-based mathematical models calibrated to patient-specific imaging data can accurately predict and optimize therapeutic response for individual cancer patients. We present the framework, motivation, and examples of using imaging data to calibrate mathematical models and predict patient outcomes. We then discuss patient-specific digital twins for optimizing interventions and outcomes. We aim to demonstrate the importance of imaging-based mathematical models in continuing the paradigm shift from population-based to individual-based cancer care.