Artificial Intelligence in Radiation Therapy

Y.B. Fu; H. Zhang; E.D. Morris; C.K. Glide-Hurst; S. Pai; A. Traverso; L. Wee; I. Hadzic; P.I. Lonne; C.Y. Shen; T. Liu; X.F. Yang

doi:10.1109/TRPMS.2021.3107454

Artificial Intelligence in Radiation Therapy

Y.B. Fu, H. Zhang, E.D. Morris, C.K. Glide-Hurst, S. Pai, A. Traverso, L. Wee, I. Hadzic, P.I. Lonne, C.Y. Shen, T. Liu, X.F. Yang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), 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.

Original language	English
Pages (from-to)	158-181
Number of pages	24
Journal	IEEE Transactions on Radiation and Plasma Medical Sciences
Volume	6
Issue number	2
DOIs	https://doi.org/10.1109/TRPMS.2021.3107454
Publication status	Published - 1 Feb 2022

Keywords

Artificial intelligence (AI)
image reconstruction
image registration
image segmentation
image synthesis
radiotherapy
treatment planning
DEFORMABLE IMAGE REGISTRATION
MODULATED ARC THERAPY
HEAD-AND-NECK
KNOWLEDGE-BASED PREDICTION
BEAM COMPUTED-TOMOGRAPHY
ANATOMIC CHANGES
NEURAL-NETWORK
PLAN QUALITY
MULTICRITERIA OPTIMIZATION
AUTOMATIC SEGMENTATION

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title = "Artificial Intelligence in Radiation Therapy",

abstract = "Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), 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.",

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author = "Y.B. Fu and H. Zhang and E.D. Morris and C.K. Glide-Hurst and S. Pai and A. Traverso and L. Wee and I. Hadzic and P.I. Lonne and C.Y. Shen and T. Liu and X.F. Yang",

note = "Funding Information: The work of Leonard Wee was supported in part by the Dutch Research Council NWO (STW-Perspectief STRaTegy 14930, Indo- Dutch Projects BIONIC 629.002.205 and TRAIN 629.002.212) Publisher Copyright: {\textcopyright} 2017 IEEE.",

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AU - Wee, L.

AU - Hadzic, I.

AU - Lonne, P.I.

AU - Shen, C.Y.

AU - Liu, T.

AU - Yang, X.F.

N1 - Funding Information: The work of Leonard Wee was supported in part by the Dutch Research Council NWO (STW-Perspectief STRaTegy 14930, Indo- Dutch Projects BIONIC 629.002.205 and TRAIN 629.002.212) Publisher Copyright: © 2017 IEEE.

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N2 - Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks (DNNs), 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.

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Artificial Intelligence in Radiation Therapy

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