100 citations · 113 across the 8 of their papers we have counts for
4 papers · 1 filter
Dynamic Image-Informed Selection of Biomechanical Tumor Growth Models
Abdullah Al Noman, Pratyush Kumar Singh, David A Hormuth +1
Glioblastoma progression is strongly influenced by evolving mechanical interactions between the tumor and surrounding brain tissue. However, the extent to which finite-deformation…
Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology
Graham Pash, Umberto Villa, David A. Hormuth +2
Quantifying the uncertainty in predictive models is critical for establishing trust and enabling risk-informed decision making for personalized medicine. In contrast to one-size-fi…
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas
Anirban Chaudhuri, Graham Pash, David A. Hormuth +6
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipa…
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth
Baoshan Liang, Jingye Tan, Luke Lozenski +4
Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor gro…