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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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Detalhes bibliográficos
Publicado no:Med Phys
Main Authors: Tseng, Huan‐Hsin, Luo, Yi, Cui, Sunan, Chien, Jen‐Tzung, Ten Haken, Randall K., Naqa, Issam El
Formato: Artigo
Idioma:Inglês
Publicado em: John Wiley and Sons Inc. 2017
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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