革命性的放射治疗:人工智能在临床实践中的作用。

IF 1.9 4区 医学 Q2 BIOLOGY
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
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引用次数: 0

摘要

本文从放射肿瘤学家的角度综述了人工智能(AI)在放射治疗中的应用。多年来,诊断成像的进步显著提高了放射治疗的效率和有效性。人工智能的引入进一步优化了肿瘤和危险器官的分割,从而为放射肿瘤学家节省了大量的时间。人工智能也被用于治疗计划和优化,将计划时间从几天缩短到几分钟甚至几秒钟。基于知识的治疗计划和深度学习技术已被用于制定与人类产生的治疗计划相当的治疗计划。此外,人工智能在治疗计划的质量控制和保证、图像引导RT的优化以及治疗过程中移动肿瘤的监测方面具有潜在的应用前景。利用人工智能进行预后评估和预测的研究越来越多,放射组学是一个突出的研究领域。人工智能在放射肿瘤学领域的未来,通过最小化观察者之间的分割差异和改进剂量充分性评估,提供了建立治疗标准化的潜力。通过人工智能实现的RT标准化可能具有全球影响,即使在资源有限的情况下也能提供世界标准的治疗。然而,在积累大数据方面存在挑战,包括患者背景信息和将治疗计划与疾病结果相关联。尽管挑战依然存在,但正在进行的研究和人工智能技术的整合为放射肿瘤学的进一步发展带来了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
3.60
自引率
5.00%
发文量
86
审稿时长
4-8 weeks
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.
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