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Deep reinforcement learning for automated radiation adaptation in lung cancer
PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). METHO...
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| Publicado no: | Med Phys |
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| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
John Wiley and Sons Inc.
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5734677/ https://ncbi.nlm.nih.gov/pubmed/29034482 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/mp.12625 |
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