Error detection for radiotherapy planning validation based on deep learning networks.

Journal: Journal of applied clinical medical physics
Published Date: May 6, 2024

Abstract

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.

Authors

  • Shupeng Liu
  • Jianhui Ma
    National Cancer Center/Cancer Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China. Electronic address: majianhui@csco.org.cn.
  • Fan Tang
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.
  • Yuqi Liang
    Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • Yanning Li
    Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
  • Zihao Li
    School of Mechanical Engineering and Automation, Harbin Institute of Technology(Shenzhen), Shenzhen, 518055, China.
  • Tingting Wang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Meijuan Zhou
    Department of Radiation Medicine, School of Public Health, Southern Medical University, Guangzhou, China.

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