Artificial Intelligence in Radiation Therapy.

Journal: IEEE transactions on radiation and plasma medical sciences
Published Date: Aug 24, 2021

Abstract

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Authors

  • Yabo Fu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Eric D Morris
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA 90095, USA.
  • Carri K Glide-Hurst
    Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA.
  • Suraj Pai
    Maastricht University Medical Centre, Netherlands.
  • Alberto Traverso
    Maastricht University Medical Centre, Netherlands.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.
  • Ibrahim Hadzic
    Maastricht University Medical Centre, Netherlands.
  • Per-Ivar Lønne
    Department of Medical Physics, Oslo University Hospital, PO Box 4953 Nydalen, 0424 Oslo, Norway.
  • Chenyang Shen
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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