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DTSTAMP:20260202T201229Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20251118T080000
DTEND;TZID=America/Chicago:20251118T170000
UID:submissions.supercomputing.org_SC25_sess537_drs108@linklings.com
SUMMARY:Towards Predictive Digital Twins with Applications to Precision On
 cology
DESCRIPTION:Graham Pash (The University of Texas at Austin, Oden Institute
  for Computational Engineering and Sciences)\n\nWell calibrated mathematic
 al and computational models enable the prediction and control of complex s
 ystems. These models can be utilized to design engineering systems or to d
 evelop treatment protocols. In contrast to one-size-fits-all approaches th
 at seek to mitigate risk at the population level, digital twins enable per
 sonalized modeling that seeks to improve decisions at the level of the ind
 ividual to improve cohort outcomes. This tailored approach is crucial in a
 pplications 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. \n\nWe develop a computational pipeline that utilizes longitudinall
 y 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 throu
 gh the solution of an inverse problem. The high-consequence nature of down
 stream decisions prompts a rigorous approach to uncertainty quantification
 . We utilize a Bayesian framework with a focus on scalable and efficient m
 ethods to characterize the uncertainty in the model inputs from the sparse
 , noisy imaging data. Furthermore, we show promising results for therapy p
 lanning using a risk-based formulation for optimization under uncertainty.
 \n\nTag: Research & ACM SRC Posters\n\nRecording: Not Livestreamed, Not Re
 corded\n\nRegistration Category: Technical Program Reg Pass\n\nSession Cha
 irs: Kento Sato (RIKEN Center for Computational Science (R-CCS)); Chris Sc
 hlipalius (Pawsey Supercomputing Research Centre; Commonwealth Scientific 
 and Industrial Research Organisation (CSIRO), Australia); and Anja Gerbes 
 (Georg-August-Universität Göttingen)\n\n
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