Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

David A Hormuth 2nd; Maguy Farhat; Chase Christenson; Brandon Curl; C Chad Quarles; Caroline Chung; Thomas E Yankeelov

doi:10.1016/j.addr.2022.114367

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Adv Drug Deliv Rev. 2022 Aug:187:114367. doi: 10.1016/j.addr.2022.114367. Epub 2022 May 30.

Authors

David A Hormuth 2nd¹, Maguy Farhat², Chase Christenson³, Brandon Curl², C Chad Quarles⁴, Caroline Chung², Thomas E Yankeelov⁵

Affiliations

¹ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address: david.hormuth@utexas.edu.
² Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA.
³ Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
⁴ Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
⁵ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA.

PMID: 35654212
DOI: 10.1016/j.addr.2022.114367

Abstract

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Keywords: Computational oncology; Magnetic resonance imaging; Optimized therapy; Ordinary differential equations; Partial differential equations; Predictions; Reaction-diffusion.

Publication types

Review
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Brain Neoplasms* / diagnostic imaging
Brain Neoplasms* / radiotherapy
Humans
Immunologic Factors
Immunotherapy
Models, Theoretical
Radiation Oncology*
Treatment Outcome

Substances

Immunologic Factors

Abstract

Publication types

MeSH terms

Substances

Grants and funding