{"title":"综合放射治疗效率工具以提高患者流量:全面文献综述。","authors":"Duvern Ramiah, Daniel Mmereki","doi":"10.1177/11795549241303606","DOIUrl":null,"url":null,"abstract":"<p><p>The promise of novel technologies to increase access to radiotherapy in low- and middle-income countries (LMICs) is crucial, given that the cost of equipping new radiotherapy centres or upgrading existing machinery remains a major obstacle to expanding access to cancer treatment. The study aims to provide a thorough analysis overview of how technological advancement may revolutionize radiotherapy (RT) to improve level of care provided to cancer patients. A comprehensive literature review following some steps of systematic review (SLR) was performed using the Web of Science (WoS), PubMed, and Scopus databases. The study findings are classified into different technologies. Artificial intelligence (AI), knowledge-based planning, remote planning, radiotherapy, and scripting are all ways to increase patient flow across radiation oncology, including initial consultation, treatment planning, delivery, verification, and patient follow-up. This review found that these technologies improve delineation of organ at risks (OARs) and considerably reduce waiting times when compared with conventional treatment planning in RT. In this review, AI, knowledge-based planning, remote radiotherapy treatment planning, and scripting reduced waiting times and improved organ at-risk delineation compared with conventional RT treatment planning. A combination of these technologies may lower cancer patients' risk of disease progression due to reduced workload, quality of therapy, and individualized treatment. Efficiency tools, such as the application of AI, knowledge-based planning, remote radiotherapy planning, and scripting, are urgently needed to reduce waiting times and improve OAR delineation accuracy in cancer treatment compared with traditional treatment planning methods. The study's contribution is to present the potential of technological advancement to optimize RT planning process, thereby improving patient care and resource utilization. The study may be extended in the future to include digital integration and technology's impact on patient safety, outcomes, and risk. 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引用次数: 0
摘要
鉴于装备新的放射治疗中心或升级现有设备的成本仍然是扩大癌症治疗覆盖面的主要障碍,新技术在增加中低收入国家(LMICs)放射治疗覆盖面方面的前景至关重要。本研究旨在全面分析技术进步如何彻底改变放射治疗(RT),从而提高癌症患者的治疗水平。本研究利用 Web of Science (WoS)、PubMed 和 Scopus 数据库,按照系统性综述 (SLR) 的一些步骤进行了全面的文献综述。研究结果分为不同的技术。人工智能(AI)、基于知识的计划、远程计划、放射治疗和脚本都是提高整个放射肿瘤学患者流量的方法,包括初步咨询、治疗计划、交付、验证和患者随访。本综述发现,与传统的放射治疗计划相比,这些技术改善了危险器官(OAR)的划分,并大大缩短了等待时间。在本综述中,与传统的 RT 治疗计划相比,人工智能、基于知识的计划、远程放疗治疗计划和脚本缩短了等待时间,并改善了危险器官的划定。这些技术的结合可减少工作量、提高治疗质量和个性化治疗,从而降低癌症患者的疾病进展风险。与传统的治疗计划方法相比,目前迫切需要高效的工具,如应用人工智能、基于知识的计划、远程放疗计划和脚本等,以减少癌症治疗中的等待时间并提高OAR划定的准确性。本研究的贡献在于展示了技术进步在优化 RT 计划流程方面的潜力,从而改善患者护理和资源利用。未来,这项研究可能会扩展到数字整合以及技术对患者安全、疗效和风险的影响。因此,在放射治疗领域,研究更高效的工具是为癌症患者开发和实施高精度放射治疗的先驱。
Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review.
The promise of novel technologies to increase access to radiotherapy in low- and middle-income countries (LMICs) is crucial, given that the cost of equipping new radiotherapy centres or upgrading existing machinery remains a major obstacle to expanding access to cancer treatment. The study aims to provide a thorough analysis overview of how technological advancement may revolutionize radiotherapy (RT) to improve level of care provided to cancer patients. A comprehensive literature review following some steps of systematic review (SLR) was performed using the Web of Science (WoS), PubMed, and Scopus databases. The study findings are classified into different technologies. Artificial intelligence (AI), knowledge-based planning, remote planning, radiotherapy, and scripting are all ways to increase patient flow across radiation oncology, including initial consultation, treatment planning, delivery, verification, and patient follow-up. This review found that these technologies improve delineation of organ at risks (OARs) and considerably reduce waiting times when compared with conventional treatment planning in RT. In this review, AI, knowledge-based planning, remote radiotherapy treatment planning, and scripting reduced waiting times and improved organ at-risk delineation compared with conventional RT treatment planning. A combination of these technologies may lower cancer patients' risk of disease progression due to reduced workload, quality of therapy, and individualized treatment. Efficiency tools, such as the application of AI, knowledge-based planning, remote radiotherapy planning, and scripting, are urgently needed to reduce waiting times and improve OAR delineation accuracy in cancer treatment compared with traditional treatment planning methods. The study's contribution is to present the potential of technological advancement to optimize RT planning process, thereby improving patient care and resource utilization. The study may be extended in the future to include digital integration and technology's impact on patient safety, outcomes, and risk. Therefore, in radiotherapy, research on more efficient tools pioneers the development and implementation of high-precision radiotherapy for cancer patients.
期刊介绍:
Clinical Medicine Insights: Oncology is an international, peer-reviewed, open access journal that focuses on all aspects of cancer research and treatment, in addition to related genetic, pathophysiological and epidemiological topics. Of particular but not exclusive importance are molecular biology, clinical interventions, controlled trials, therapeutics, pharmacology and drug delivery, and techniques of cancer surgery. The journal welcomes unsolicited article proposals.