Reinforcement Learning for Radiotherapy Dose Fractioning Automation
| Autor: | Paul Desbordes, Grégoire Moreau, Benoît Macq, Vincent François-Lavet |
|---|---|
| Přispěvatelé: | UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
reinforcement learning
cellular simulation Computer science medicine.medical_treatment Dose fractionation Medicine (miscellaneous) Dose per fraction Article General Biochemistry Genetics and Molecular Biology 030218 nuclear medicine & medical imaging Cancer treatment 03 medical and health sciences 0302 clinical medicine lcsh:Biology (General) 030220 oncology & carcinogenesis Large dose medicine Radiotherapy dose Reinforcement learning automatic treatment planning Tumor growth External beam radiotherapy lcsh:QH301-705.5 Biomedical engineering |
| Zdroj: | Biomedicines, Vol 9, Iss 214, p 214 (2021) Biomedicines Volume 9 Issue 2 Biomedicines, Vol. 9, no.2, p. 214 (2021) |
| ISSN: | 2227-9059 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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