Leveraging High-Performance Computing and Quantitative Imaging for Personalized Spatial-Temporal Forecasts of High-Grade Glioma Treatment Response
Event Type
Workshop
Applications
Computational Science
Education and Training and Outreach
HPC Community Collaboration
HPC Training and Education
Machine Learning and Artificial Intelligence
Performance
Workforce
W
TimeSunday, 14 November 20214:30pm - 4:45pm CST
Location223
DescriptionMaximal safe resection followed by combination radiotherapy and chemotherapy is the standard treatment approach for patients with high-grade gliomas to target 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 adaptive radiotherapy is possible, identifying subregions of disease that are likely 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 adaptions, we have developed a family of biologically-based, mathematical models 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 resources to calibrate a family of models in patients with high-grade gliomas.
