An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.

Wang, Hanlin; Bai, Xue; Wang, Yajuan; Lu, Yanfei; Wang, Binbing

Wang, Hanlin; Bai, Xue; Wang, Yajuan; Lu, Yanfei; Wang, Binbing.

Afiliación

Wang H; Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Bai X; Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Wang Y; Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Lu Y; Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Wang B; Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.

Front Oncol ; 13: 1124458, 2023.

Article en En | MEDLINE | ID: mdl-36816929

Palabras clave

automatic treatment planning; deep reinforcement learning; integrated solution; intensity-modulated radiation therapy; multi-objectives adjustment policy network

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Oncol Año: 2023 Tipo del documento: Article País de afiliación: China

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