A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy.

Journal: Cancer medicine
PMID: 34811957

Abstract

BACKGROUND: Delineation of clinical target volume (CTV) for radiotherapy is a time-consuming and labor-intensive work. This study aims to propose a novel convolutional neural network (CNN)-based model for fast auto-segmentation of CTV. To evaluate its performance and clinical utility, a blind randomized validation method was used.

Authors

  • Yijun Wu
    Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
  • Kai Kang
    Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, United States of America.
  • Chang Han
    Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
  • Shaobin Wang
    MedMind Technology Co., Ltd., Beijing 100080, China.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Fuquan Zhang
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
  • Zhikai Liu
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.

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