Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning
Authored by Ammar Jalalimanesh, Hamidreza Shahabi Haghighi, Abbas Ahmadi, Madjid Soltani
Date Published: 2017
DOI: 10.1016/j.matcom.2016.05.008
Sponsors:
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Platforms:
NetLogo
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Abstract
Along with surgery and chemotherapy, radiotherapy is an effective way to
treat cancer. Many cancer patients take delivery of radiation. The goal
of radiotherapy is to destroy the tumor without damaging healthy tissue.
Due to the complexity of the procedure, modeling and simulation can be
useful for radiotherapy. In this research we propose a new approach to
optimize dose calculation in radiotherapy. We consider fix schedule of
irradiation and varying the fraction size during the treatment. The
proposed approach contains two steps. At the first step, we develop an
agent-based simulation of vascular tumor growth based on biological
evidences. We consider a multi-scale model in which cellular and
subcellular scales are observed. We consider heterogeneity of tumor
oxygen diffusion and also the effects of cancer cells hypoxia on
radiotherapy. Besides, different radiosensitivity of cells related to
their cell-cycle phase is modeled. The agent-based model was implemented
in NetLogo package. Based on this model, we simulate different scenarios
of radiotherapy. At the second step, we propose an algorithm for the
optimization of radiotherapy. Radiation dose and fractionation scheme
are considered as two key elements of radiation therapy. To optimize the
therapy we apply Q-learning algorithm. Finally, we combine the
simulation and optimization compartments together using R-NetLogo
package. By tuning the parameters of learning algorithm optimal
treatment plans are achieved to cure tumor together with minimum side
effects. Our research presents the power of agent-based approach
combined with reinforcement learning for simulating and optimizing
complex biological problems such as radiotherapy. The proposed modeling
approach lets us to study different scenarios of tumor growth and
radiotherapy. Furthermore, our optimization algorithm works fast and
finds the best treatment plan. (C) 2016 International Association for
Mathematics and Computers in Simulation (IMACS). Published by Elsevier
B.V. All rights reserved.
Tags
Agent-based modeling
algorithms
Q-learning
reinforcement learning
radiotherapy
growth
Computer-simulation
In-vivo
Therapy
Radiation oncology
Tumor response
Cancer treatment
Irradiation