Journal
BIOMEDICINES
Volume 9, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/biomedicines9020214
Keywords
reinforcement learning; automatic treatment planning; cellular simulation
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Funding
- ProtherWal [7289]
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This study investigates the use of deep reinforcement learning approaches in external beam radiotherapy cancer treatment and demonstrates the advantages of this method in optimizing dose fractionation.
External beam radiotherapy cancer treatment aims to deliver dose fractions to slowly destroy a tumor while avoiding severe side effects in surrounding healthy tissues. To automate the dose fraction schedules, this paper investigates how deep reinforcement learning approaches (based on deep Q network and deep deterministic policy gradient) can learn from a model of a mixture of tumor and healthy cells. A 2D tumor growth simulation is used to simulate radiation effects on tissues and thus training an agent to automatically optimize dose fractionation. Results show that initiating treatment with large dose per fraction, and then gradually reducing it, is preferred to the standard approach of using a constant dose per fraction.
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