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Artificial intelligence in image-guided radiotherapy: A review of treatment target localization

  • Wei Zhao*
  • , Liyue Shen
  • , Md Tauhidul Islam
  • , Wenjian Qin
  • , Zhicheng Zhang
  • , Xiaokun Liang
  • , Gaolong Zhang
  • , Shouping Xu
  • , Xiaomeng Li*
  • *Corresponding author for this work
  • Stanford University
  • Shenzhen Institute of Advanced Technology
  • General Hospital of People's Liberation Army
  • Hong Kong University of Science and Technology

Research output: Contribution to journalReview articlepeer-review

Abstract

Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

Original languageEnglish
Pages (from-to)4881-4894
Number of pages14
JournalQuantitative Imaging in Medicine and Surgery
Volume11
Issue number12
DOIs
StatePublished - Dec 2021

Keywords

  • Artificial intelligence (ai)
  • Convolutional neural network
  • Deep learning
  • Image-guided radiotherapy (igrt)
  • Machine learning
  • Target positioning

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