Well calibrated mathematical and computational models enable the prediction and control of complex systems. These models can be utilized to design engineering systems or to develop treatment protocols. In contrast to one-size-fits-all approaches that seek to mitigate risk at the population level, digital twins enable personalized modeling that seeks to improve decisions at the level of the individual to improve cohort outcomes. This tailored approach is crucial in applications such as precision oncology. In particular, high grade gliomas exhibit significant heterogeneity in physiology and response to treatment that result in low median survival rates despite an aggressive-standard-of-care. We develop a computational pipeline that utilizes longitudinal non-invasive imaging to generate a patient-specific computational geometry and estimate of the tumor state. The data are then used to inform the spatially varying parameters of mechanistic models for tumor growth through the solution of an inverse problem. The high-consequence nature of downstream decisions prompts a rigorous approach to uncertainty quantification. We utilize a Bayesian framework with a focus on scalable and efficient methods to characterize the uncertainty in the model inputs from the sparse, noisy imaging data. Furthermore, we will discuss promising results for therapy planning using a risk-based formulation for optimization under uncertainty.
