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Review
. 2021 Dec;11(12):4881-4894.
doi: 10.21037/qims-21-199.

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

Wei Zhao  1 , Liyue Shen  2 , Md Tauhidul Islam  2 , Wenjian Qin  3 , Zhicheng Zhang  2 , Xiaokun Liang  3 , Gaolong Zhang  1 , Shouping Xu  4 , Xiaomeng Li  5
Affiliations
Review

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

Wei Zhao et al. Quant Imaging Med Surg. 2021 Dec.

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.

Keywords: Artificial intelligence (AI); convolutional neural network; deep learning; image-guided radiotherapy (IGRT); machine learning; target positioning.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-199). The special issue “Artificial Intelligence for Image-guided Radiation Therapy” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare.

Figures

Figure 1
Figure 1
Number of papers according to topic search on “Web of knowledge” by using keywords “Artificial intelligence”, “Machine learning”, “Deep learning”, “Radiotherapy”, and “Image”.
Figure 2
Figure 2
Results of target localization for prostate cancer patients pretreatment setup and real-time tracking using a deep learning approach. The deep learning model predicted target positions are shown in yellow, and their corresponding ground truth is in blue. AP, anteroposterior; L-Lat, Left-lateral. Adapted with permission from reference (22). Copyright 2019 Elsevier.
Figure 3
Figure 3
Results of synthetic CT images from daily cone-beam CT images using a deep learning approach. Evaluation studies using prostate cancer patients show the deep learning approach can synthesize CT-quality images with accurate CT numbers from CBCT images. The first, second, and third rows are the daily CBCT, the predicted synthetic CT, and the deformed planning CT images, respectively. sCT, synthesized CT; dpCT, deformed planning CT. Adapted with permission from reference (85). Copyright 2019 John Wiley and Sons.
See this image and copyright information in PMC

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