Presentation
Towards Predictive Digital Twins with Applications to Precision Oncology
DescriptionWell 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 longitudinally collected MRI data to generate a patient-specific computational geometry and estimate the tumor cellularity. The data are then used to inform the spatially varying parameters of mathematical 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 show promising results for therapy planning using a risk-based formulation for optimization under uncertainty.
We develop a computational pipeline that utilizes longitudinally collected MRI data to generate a patient-specific computational geometry and estimate the tumor cellularity. The data are then used to inform the spatially varying parameters of mathematical 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 show promising results for therapy planning using a risk-based formulation for optimization under uncertainty.
Event Type
Doctoral Showcase
TimeThursday, 20 November 20253:30pm - 3:45pm CST
Location230
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