Application of deep learning in radiation therapy for cancer.

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Published Date: Mar 21, 2024

Abstract

In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.

Authors

  • X Wen
    Department of Nursing, Sichuan Provincial People's Hospital, Chengdu, China.
  • C Zhao
    School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, China.
  • B Zhao
    Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.
  • M Yuan
    Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
  • J Chang
    Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China.
  • W Liu
    Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • J Meng
  • L Shi
    Key Laboratory of National Health Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Public Health Research Center of Jiangnan University, Wuxi 214064, China.
  • S Yang
    Neural Engineering Data Consortium, Temple University, Philadelphia, Pennsylvania, USA, {scott.yang, silvia.lopez, meysam, obeid, picone}@temple.edu.
  • J Zeng
    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Y Yang
    Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot 010030, China.

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