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自动化治疗计划能否在临床实践中得到应用?

Can automated treatment plans gain traction in the clinic?

机构信息

Department of Radiation Oncology, Cone Health Cancer Center, Greensboro, NC, USA.

出版信息

J Appl Clin Med Phys. 2019 Aug;20(8):29-35. doi: 10.1002/acm2.12674. Epub 2019 Jul 16.

Abstract

Recently, there has been an increased interest in the feasibility and impact of automation within the field of medical dosimetry. While there have been many commercialized solutions for automatic treatment planning, the use of an application programming interface to achieve complete plan generation for specific treatment sites is a process only recently available for certain commercial vendors. Automatic plan generation for 20 prostate patients was achieved via a stand-alone automated planning script that accessed a knowledge-based planning solution. Differences between the auto plans and clinically treated, baseline plans were analyzed and compared. The planning script successfully initialized a treatment plan, accessed the knowledge-based planning model, optimized the plan, assessed for constraint compliance, and normalized the treatment plan for maximal coverage while meeting constraints. Compared to baseline plans, the auto-generated plans showed significantly improved rectal sparing with similar coverage for targets and comparable doses to the remaining organs-at-risk. Utilization of a script, with its associated time saving and integrated process management, can quickly and automatically generate an acceptable clinical treatment plan for prostate cancer with either improved or similar results compared to a manually created plan.

摘要

最近,人们对医学剂量学领域自动化的可行性和影响产生了浓厚的兴趣。虽然已经有许多商业化的自动治疗计划解决方案,但使用应用程序编程接口为特定治疗部位生成完整的计划,对于某些商业供应商来说,这是一个最近才出现的过程。通过访问基于知识的计划解决方案的独立自动化计划脚本,实现了 20 例前列腺患者的自动计划生成。分析并比较了自动计划与临床治疗的基线计划之间的差异。该计划脚本成功初始化了治疗计划,访问了基于知识的计划模型,对计划进行了优化,评估了约束条件的遵守情况,并对治疗计划进行了归一化处理,以实现最大覆盖范围,同时满足约束条件。与基线计划相比,自动生成的计划显示出明显改善的直肠保护,同时目标覆盖率相似,对其他危及器官的剂量也相似。脚本的使用及其相关的节省时间和集成的流程管理,可以快速自动地为前列腺癌生成可接受的临床治疗计划,与手动创建的计划相比,结果要么得到改善,要么相似。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e375/6698763/5f43a6a7cb39/ACM2-20-29-g001.jpg

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