Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling
<b>Objective:</b> Radiotherapy is a primary method for cancer treatment, wherein radiation doses are divided into multiple sessions or fractions to effectively target tumors and minimize damage to surrounding tissues. <b>Methods:</b> In this study, we leverage reinforcement l...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2227-9059/13/6/1367 |
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| author | Mélanie Ghislain Florian Martin Manon Dausort Damien Dasnoy-Sumell Ana Maria Barragan Montero Benoît Macq |
| author_facet | Mélanie Ghislain Florian Martin Manon Dausort Damien Dasnoy-Sumell Ana Maria Barragan Montero Benoît Macq |
| author_sort | Mélanie Ghislain |
| collection | DOAJ |
| description | <b>Objective:</b> Radiotherapy is a primary method for cancer treatment, wherein radiation doses are divided into multiple sessions or fractions to effectively target tumors and minimize damage to surrounding tissues. <b>Methods:</b> In this study, we leverage reinforcement learning (RL) to enhance treatment planning with the aim of improving the adaptability and robustness of RL agents given the inherent inaccuracies in tumor growth models. A 2D simulation model of tumor growth is employed, where tabular RL techniques are used to determine the optimal treatment strategies. We emphasize the significance of tissue damage predictions and incorporate the Lyman NTCP model to assess treatment outcomes, analyzing complications across three simulated body sites: the rectum, head and neck and lung. <b>Results:</b> For all the tumor sites, the RL approach significantly reduces healthy tissue damage by 10.7%, 49.1% and 37.5%, respectively, for rectal, head and neck and lung cancers compared with the baseline treatment. <b>Conclusions:</b> The RL-based approach in radiotherapy not only achieves tumor eradication but also significantly reduces healthy tissue damage compared with traditional treatment methods. This study demonstrates the potential of reinforcement learning to optimize treatment planning in radiotherapy, offering a promising path towards more personalized and effective cancer treatments. |
| format | Article |
| id | doaj-art-37f6b1ef4abc4655b9dddc27e5485575 |
| institution | Kabale University |
| issn | 2227-9059 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj-art-37f6b1ef4abc4655b9dddc27e54855752025-08-20T03:27:26ZengMDPI AGBiomedicines2227-90592025-06-01136136710.3390/biomedicines13061367Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome ModelingMélanie Ghislain0Florian Martin1Manon Dausort2Damien Dasnoy-Sumell3Ana Maria Barragan Montero4Benoît Macq5Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, 1348 Louvain-La-Neuve, BelgiumInstitute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, 1348 Louvain-La-Neuve, BelgiumInstitute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, 1348 Louvain-La-Neuve, BelgiumInstitute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, 1348 Louvain-La-Neuve, BelgiumMolecular Imaging, Radiotherapy and Oncology Institute, UCLouvain, 1200 Woluwe-Saint-Lambert, BelgiumInstitute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, 1348 Louvain-La-Neuve, Belgium<b>Objective:</b> Radiotherapy is a primary method for cancer treatment, wherein radiation doses are divided into multiple sessions or fractions to effectively target tumors and minimize damage to surrounding tissues. <b>Methods:</b> In this study, we leverage reinforcement learning (RL) to enhance treatment planning with the aim of improving the adaptability and robustness of RL agents given the inherent inaccuracies in tumor growth models. A 2D simulation model of tumor growth is employed, where tabular RL techniques are used to determine the optimal treatment strategies. We emphasize the significance of tissue damage predictions and incorporate the Lyman NTCP model to assess treatment outcomes, analyzing complications across three simulated body sites: the rectum, head and neck and lung. <b>Results:</b> For all the tumor sites, the RL approach significantly reduces healthy tissue damage by 10.7%, 49.1% and 37.5%, respectively, for rectal, head and neck and lung cancers compared with the baseline treatment. <b>Conclusions:</b> The RL-based approach in radiotherapy not only achieves tumor eradication but also significantly reduces healthy tissue damage compared with traditional treatment methods. This study demonstrates the potential of reinforcement learning to optimize treatment planning in radiotherapy, offering a promising path towards more personalized and effective cancer treatments.https://www.mdpi.com/2227-9059/13/6/1367reinforcement learningradiotherapy treatment planningtumor growth simulationcancer complication |
| spellingShingle | Mélanie Ghislain Florian Martin Manon Dausort Damien Dasnoy-Sumell Ana Maria Barragan Montero Benoît Macq Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling Biomedicines reinforcement learning radiotherapy treatment planning tumor growth simulation cancer complication |
| title | Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling |
| title_full | Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling |
| title_fullStr | Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling |
| title_full_unstemmed | Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling |
| title_short | Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling |
| title_sort | optimal fractionation scheduling for radiotherapy treatments with reinforcement learning tumor growth modeling and outcome modeling |
| topic | reinforcement learning radiotherapy treatment planning tumor growth simulation cancer complication |
| url | https://www.mdpi.com/2227-9059/13/6/1367 |
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