Artificial intelligence in radiotherapy: a technological review
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial in...
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| Published in | Frontiers of medicine Vol. 14; no. 4; pp. 431 - 449 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Beijing
Higher Education Press
01.08.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned. |
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| AbstractList | Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned. |
| Author | Sheng, Ke |
| Author_xml | – sequence: 1 givenname: Ke surname: Sheng fullname: Sheng, Ke email: ksheng@mednet.ucla.edu organization: Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32728877$$D View this record in MEDLINE/PubMed |
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| Keywords | outcome prediction treatment planning quality assurance artificial intelligence medical imaging radiation therapy |
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| Snippet | Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as... |
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| SubjectTerms | Artificial Intelligence Humans Machine Learning Medical imaging Medicine Medicine & Public Health Neoplasms - radiotherapy outcome prediction quality assurance Quality control Radiation therapy Review Robotics treatment planning |
| Title | Artificial intelligence in radiotherapy: a technological review |
| URI | https://journal.hep.com.cn/fmd/EN/10.1007/s11684-020-0761-1 https://link.springer.com/article/10.1007/s11684-020-0761-1 https://www.ncbi.nlm.nih.gov/pubmed/32728877 https://www.proquest.com/docview/2438395527 https://search.proquest.com/docview/2429061355 |
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