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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Bibliographic Details
Published in:Med Phys
Main Authors: Tseng, Huan‐Hsin, Luo, Yi, Cui, Sunan, Chien, Jen‐Tzung, Ten Haken, Randall K., Naqa, Issam El
Format: Artigo
Language:Inglês
Published: John Wiley and Sons Inc. 2017
Subjects:
BIOLOGICAL PHYSICS AND RESPONSE PREDICTION
Online Access: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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