Deep learning-based multiple-CT optimization: An adaptive treatment planning approach to account for anatomical changes in intensity-modulated proton therapy for head and neck cancers.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

PMID: 39581351

BACKGROUNDS: Intensity-modulated proton therapy (IMPT) is particularly susceptible to range and setup uncertainties, as well as anatomical changes.

Muyu Liu

Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China.
Bo Pang

College of Water Sciences, Beijing Normal University; Beijing 100875, China; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China. Electronic address: pb@bnu.edu.cn.
Shuoyan Chen

Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan 430072, China.
Yiling Zeng

Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China.
Qi Zhang

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Hong Quan

School of Physics Science and Technology, Wuhan University, Wuhan 430072, P.R.China.
Yu Chang

Department of Neurology, Tianjin First Central Hospital, Tianjin, China.
Zhiyong Yang

Algorithms Cone-Beam Computed Tomography Deep Learning Head and Neck Neoplasms Humans Organs at Risk Proton Therapy Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies

View on PubMed Access via DOI PubMed (39581351)