학술논문
Revolutionizing radiation therapy: the role of AI in clinical practice
Document Type
Academic Journal
Author
Kawamura, Mariko; Kamomae, Takeshi; Yanagawa, Masahiro; Kamagata, Koji; Fujita, Shohei; Ueda, Daiju; Matsui, Yusuke; Fushimi, Yasutaka; Fujioka, Tomoyuki; Nozaki, Taiki; Yamada, Akira; Hirata, Kenji; Ito, Rintaro; Fujima, Noriyuki; Tatsugami, Fuminari; Nakaura, Takeshi; Tsuboyama, Takahiro; Naganawa, Shinji
Source
Journal of Radiation Research. January 2024, Vol. 65 Issue 1, p1, 9 p.
Subject
Language
English
ISSN
0449-3060
Abstract
INTRODUCTION Radiotherapy has a history of improvement along with the advances in diagnostic imaging. With the advent of computed tomography (CT), the ability to depict tumors not as shadows but [...]
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 ofAI 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. Knowledgebased 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 interobserver 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 ofAI technology hold promise for further advancements in radiation oncology. Keywords: radiotherapy; artificial intelligence; auto-segmentation; auto-planning
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 ofAI 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. Knowledgebased 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 interobserver 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 ofAI technology hold promise for further advancements in radiation oncology. Keywords: radiotherapy; artificial intelligence; auto-segmentation; auto-planning