Deep learning for patient-specific quality assurance of volumetric modulated arc therapy: Prediction accuracy and cost-sensitive classification performance.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PMID:

Abstract

PURPOSE: To evaluate a deep learning model's performance in predicting and classifying patient-specific quality assurance (PSQA) results for volumetric modulated arc therapy (VMAT), aiming to streamline PSQA workflows and reduce the onsite measurement workload.

Authors

  • Guangjun Li
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. gjnick829@sina.com.
  • Lian Duan
    Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
  • Lizhang Xie
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, China.
  • Ting Hu
    Memorial University of Newfoundland, St. John's, Canada.
  • Weige Wei
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Long Bai
    State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China. bailong@cqu.edu.cn.
  • Qing Xiao
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Wenjie Liu
    School of Chemical Science and Engineering, Tongji University, 1239 Siping Rd, Shanghai, 200092, PR China. tmyao@tongji.edu.cn ao.huang@tcichemicals.com.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sen Bai
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China. Electronic address: baisen@scu.edu.cn.
  • Zhang Yi