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模板引导的自动化计划常规的临床应用。

Clinical application of a template-guided automated planning routine.

机构信息

Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.

Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA.

出版信息

J Appl Clin Med Phys. 2023 Mar;24(3):e13837. doi: 10.1002/acm2.13837. Epub 2022 Nov 8.

DOI:10.1002/acm2.13837
PMID:36347220
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10018666/
Abstract

PURPOSE

Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application.

METHODS

A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan.

RESULTS

The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″.

CONCLUSIONS

Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

摘要

目的

确定使用基于模板的自动化计划应用程序时的剂量学质量和计划时间减少。

方法

通过治疗计划系统应用程序接口集成了一个软件应用程序QuickPlan，以方便使用可配置的模板进行轮廓勾画、基于知识的计划结构匹配、射野设计和算法设置的自动化计划。在计划过程的各个级别进行验证，并协助评估 CT 图像、结构集和计划布局的自动化准备情况。将 QuickPlan 进行剂量学评估，共涉及 22 例海马回避全脑放疗患者。对验证集以及 10 例已通过 QuickPlan 实现自动化的前瞻性患者的治疗计划生成所需时间进行比较。

结果

将 22 例自动化治疗计划与使用相同流程的手动重新计划进行比较，结果显示剂量学差异具有较小的临床意义。与自动化计划相比，目标剂量达到 2%体积和均匀性指数的显著降低，而其他剂量评估则无显著差异。所有自动化计划的生成时间均缩短，中位数差异为 9'50″±4'33″。

结论

基于模板的自动化计划允许通过一致的优化结构创建、射野创建、计划优化和剂量计算来减少治疗计划时间，同时保持相似的剂量学质量。该过程具有扩展到多个疾病部位的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/bd3c944927a8/ACM2-24-e13837-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/828a5f29b2c0/ACM2-24-e13837-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/ee1c547d0bc0/ACM2-24-e13837-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/414beba66048/ACM2-24-e13837-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/1437cf38b94c/ACM2-24-e13837-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/75e9b68a3cb5/ACM2-24-e13837-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/bd3c944927a8/ACM2-24-e13837-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/828a5f29b2c0/ACM2-24-e13837-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/ee1c547d0bc0/ACM2-24-e13837-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/414beba66048/ACM2-24-e13837-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/1437cf38b94c/ACM2-24-e13837-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/75e9b68a3cb5/ACM2-24-e13837-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ba1/10018666/bd3c944927a8/ACM2-24-e13837-g005.jpg

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Automated contouring and planning pipeline for hippocampal-avoidant whole-brain radiotherapy.
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Hippocampal Avoidance During Whole-Brain Radiotherapy Plus Memantine for Patients With Brain Metastases: Phase III Trial NRG Oncology CC001.全脑放疗联合美金刚治疗脑转移瘤患者时对海马的回避：NRG 肿瘤学 CC001 期临床试验。
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