{"title":"革命性的放射治疗:人工智能在临床实践中的作用。","authors":"Mariko Kawamura, Takeshi Kamomae, Masahiro Yanagawa, Koji Kamagata, Shohei Fujita, Daiju Ueda, Yusuke Matsui, Yasutaka Fushimi, Tomoyuki Fujioka, Taiki Nozaki, Akira Yamada, Kenji Hirata, Rintaro Ito, Noriyuki Fujima, Fuminari Tatsugami, Takeshi Nakaura, Takahiro Tsuboyama, Shinji Naganawa","doi":"10.1093/jrr/rrad090","DOIUrl":null,"url":null,"abstract":"<p><p>This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.</p>","PeriodicalId":16922,"journal":{"name":"Journal of Radiation Research","volume":" ","pages":"1-9"},"PeriodicalIF":1.9000,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10803173/pdf/","citationCount":"0","resultStr":"{\"title\":\"Revolutionizing radiation therapy: the role of AI in clinical practice.\",\"authors\":\"Mariko Kawamura, Takeshi Kamomae, Masahiro Yanagawa, Koji Kamagata, Shohei Fujita, Daiju Ueda, Yusuke Matsui, Yasutaka Fushimi, Tomoyuki Fujioka, Taiki Nozaki, Akira Yamada, Kenji Hirata, Rintaro Ito, Noriyuki Fujima, Fuminari Tatsugami, Takeshi Nakaura, Takahiro Tsuboyama, Shinji Naganawa\",\"doi\":\"10.1093/jrr/rrad090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. 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Revolutionizing radiation therapy: the role of AI in clinical practice.
This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.
期刊介绍:
The Journal of Radiation Research (JRR) is an official journal of The Japanese Radiation Research Society (JRRS), and the Japanese Society for Radiation Oncology (JASTRO).
Since its launch in 1960 as the official journal of the JRRS, the journal has published scientific articles in radiation science in biology, chemistry, physics, epidemiology, and environmental sciences. JRR broadened its scope to include oncology in 2009, when JASTRO partnered with the JRRS to publish the journal.
Articles considered fall into two broad categories:
Oncology & Medicine - including all aspects of research with patients that impacts on the treatment of cancer using radiation. Papers which cover related radiation therapies, radiation dosimetry, and those describing the basis for treatment methods including techniques, are also welcomed. Clinical case reports are not acceptable.
Radiation Research - basic science studies of radiation effects on livings in the area of physics, chemistry, biology, epidemiology and environmental sciences.
Please be advised that JRR does not accept any papers of pure physics or chemistry.
The journal is bimonthly, and is edited and published by the JRR Editorial Committee.