Leveraging High-Performance Computing and Quantitative Imaging for Personalized Spatial-Temporal Forecasts of High-Grade Glioma Treatment Response

Workshop:CAFCW21: Computational Approaches for Cancer Workshop 2021

Authors: David Hormuth (University of Texas, Oden Institute; Livestrong Cancer Institutes, University of Texas); Maguy Farhat and Brandon Curl (MD Anderson Cancer Center); Thomas Yankeelov (University of Texas, Oden Institute); and Caroline Chung (MD Anderson Cancer Center)

Abstract: Maximal 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.

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