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Open AccessReview

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

1
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
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Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
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Department of Mechanical and Aerospace Engineering, The University at Buffalo, Buffalo, NY 14260, USA
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Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
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Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
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Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Texas Oncology, Austin, TX 78731, USA
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Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(5), 1314; https://doi.org/10.3390/jcm9051314
Received: 13 April 2020 / Revised: 25 April 2020 / Accepted: 28 April 2020 / Published: 2 May 2020
Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory. View Full-Text
Keywords: mathematical model; cancer treatment; predicting response; optimizing response mathematical model; cancer treatment; predicting response; optimizing response
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Jarrett, A.M.; Faghihi, D.; Hormuth, D.A., II; Lima, E.A.B.F.; Virostko, J.; Biros, G.; Patt, D.; Yankeelov, T.E. Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J. Clin. Med. 2020, 9, 1314.

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