Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.

Journal: Radiation oncology (London, England)
Published Date: Oct 6, 2023

Abstract

PURPOSE: Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-intensive. This study aims to propose a deep learning (DL)-based workflow towards fully automated clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy.

Authors

  • Jianhao Geng
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Xianggao Zhu
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Zhiyan Liu
    Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Lu Bai
    College of Chemical Engineering, Department of Pharmaceutical Engineering, Northwest University, Taibai North Road 229, Xi'an 710069, Shaanxi, China.
  • Shaobin Wang
    MedMind Technology Co., Ltd., Beijing 100080, China.
  • Yongheng Li
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Haizhen Yue
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital, Beijing, China.
  • Yi Du
    Computer Network Information Center, Chinese Academy of Sciences, Beijing, China.

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