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

[Display omitted] 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 synergi...

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Bibliographic Details
Published in:Advanced drug delivery reviews 2022-08, Vol.187, p.114367-114367, Article 114367
Main Authors: Hormuth, David A., Farhat, Maguy, Christenson, Chase, Curl, Brandon, Chad Quarles, C., Chung, Caroline, Yankeelov, Thomas E.
Format: Article
Language:English
Subjects:
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - radiotherapy
Computational oncology
Humans
Immunologic Factors
Immunotherapy
Magnetic resonance imaging
Models, Theoretical
Optimized therapy
Ordinary differential equations
Partial differential equations
Predictions
Radiation Oncology
Reaction-diffusion
Treatment Outcome
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Summary:[Display omitted] 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.
ISSN:0169-409X
1872-8294
DOI:10.1016/j.addr.2022.114367