Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.

Journal: International journal of radiation oncology, biology, physics
Published Date:

Abstract

PURPOSE: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans.

Authors

  • Jiaqi Li
    Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, People's Republic of China.
  • Le Wang
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Xile Zhang
    Department of Radiation Oncology, Peking University Third Hospital, Beijing, China.
  • Lu Liu
    College of Pharmacy, Harbin Medical University, Harbin, China.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Maria F Chan
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
  • Jing Sui
    The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Ruijie Yang
    Department of Radiation Oncology, Peking University Third Hospital, Beijing, China. Electronic address: ruijyang@yahoo.com.