Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation
Authored by Ammar Jalalimanesh, Hamidreza Shahabi Haghighi, Abbas Ahmadi, Hossein Hejazian, Madjid Soltani
Date Published: 2017
DOI: 10.1080/0952813x.2017.1292319
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Abstract
Radiotherapy (RT) is among the regular techniques for the treatment of
cancerous tumours. Many of cancer patients are treated by this manner.
Treatment planning is the most important phase in RT and it plays a key
role in therapy quality achievement. As the goal of RT is to irradiate
the tumour with adequately high levels of radiation while sparing
neighbouring healthy tissues as much as possible, it is a
multi-objective problem naturally. In this study, we propose an
agent-based model of vascular tumour growth and also effects of RT.
Next, we use multi-objective distributed Q-learning algorithm to find
Pareto-optimal solutions for calculating RT dynamic dose. We consider
multiple objectives and each group of optimizer agents attempt to
optimise one of them, iteratively. At the end of each iteration, agents
compromise the solutions to shape the Pareto-front of multi-objective
problem. We propose a new approach by defining three schemes of
treatment planning created based on different combinations of our
objectives namely invasive, conservative and moderate. In invasive
scheme, we enforce killing cancer cells and pay less attention about
irradiation effects on normal cells. In conservative scheme, we take
more care of normal cells and try to destroy cancer cells in a less
stressed manner. The moderate scheme stands in between. For
implementation, each of these schemes is handled by one agent in
MDQ-learning algorithm and the Pareto optimal solutions are discovered
by the collaboration of agents. By applying this methodology, we could
reach Pareto treatment plans through building different scenarios of
tumour growth and RT. The proposed multi-objective optimisation
algorithm generates robust solutions and finds the best treatment plan
for different conditions.
Tags
Agent-based modelling
algorithms
glioblastoma
Model
radiotherapy
growth
Computer-simulation
Cells
In-vivo
Multi-objective optimisation
Reinforcement
learning
Mdq-learning
Simulation-based optimisation
Tumour treatment
Radiation oncology
Tumor response