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DTSTART;TZID=America/Chicago:20211114T163000
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UID:submissions.supercomputing.org_SC21_sess434_ws_cafcw105@linklings.com
SUMMARY:Leveraging High-Performance Computing and Quantitative Imaging for
  Personalized Spatial-Temporal Forecasts of High-Grade Glioma Treatment Re
 sponse
DESCRIPTION:Workshop\n\nLeveraging High-Performance Computing and Quantita
 tive Imaging for Personalized Spatial-Temporal Forecasts of High-Grade Gli
 oma Treatment Response\n\nHormuth, Farhat, Curl, Yankeelov, Chung\n\nMaxim
 al safe resection followed by combination radiotherapy and chemotherapy is
  the standard treatment approach for patients with high-grade gliomas to t
 arget residual and infiltrative tumor. Response to therapy depends on the 
 ability to target the tumor and on the treatment sensitivity influenced by
  factors including tumor physiology and phenotypic behavior. While adaptiv
 e radiotherapy is possible, identifying subregions of disease that are lik
 ely to progress during the course of therapy would allow for anticipatory 
 adjustments in the radiotherapy treatment to target more aggressive tumor 
 areas.  Towards realizing the goal of timely, personalized treatment adapt
 ions, we have developed a family of biologically-based, mathematical model
 s of tumor growth and response which are initialized and calibrated using 
 patient-specific multi-parametric magnetic resonance imaging (mpMRI) data.
  mpMRI enables non-invasive measurement of tumor morphology, vascularity, 
 and cellularity. In this report, we leverage high-performance computing re
 sources to calibrate a family of models in patients with high-grade glioma
 s.\n\nTag: Applications, Computational Science, Education and Training and
  Outreach, HPC Community Collaboration, HPC Training and Education, Machin
 e Learning and Artificial Intelligence, Performance, Workforce\n\nRegistra
 tion Category: Workshop Reg Pass
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