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临床委托并引入内部人工智能(AI)平台用于头颈部自动调强放射治疗(IMRT)治疗计划。

Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.

作者信息

Li Xinyi, Sheng Yang, Wu Qingrong Jackie, Ge Yaorong, Brizel David M, Mowery Yvonne M, Yang Dongrong, Yin Fang-Fang, Wu Qiuwen

机构信息

Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, United States.

Department of Information Systems, University of North Carolina at Charlotte, Charlotte, North Carolina, United States.

出版信息

J Appl Clin Med Phys. 2025 Jan;26(1):e14558. doi: 10.1002/acm2.14558. Epub 2024 Nov 6.

DOI:10.1002/acm2.14558
PMID:39503512
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11712748/
Abstract

BACKGROUND AND PURPOSE

To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).

MATERIALS AND METHODS

The AI planning platform has three components: (1) a graphical user interface (GUI) is built within the framework of a commercial treatment planning system (TPS). The GUI allows AI models to run remotely on a designated workstation configured with GPU acceleration. (2) A template plan is automatically prepared involving both clinical and AI considerations, which include contour evaluation, isocenter placement, and beam/collimator jaw placement. (3) A well-orchestrated suite of AI models predicts optimal fluence maps, which are imported into TPS for dose calculation followed by an optional automatic fine-tuning. Six AI models provide flexible tradeoffs in parotid sparing and Planning Target Volume (PTV)-organ-at-risk (OAR) preferences. Planners could examine the plan dose distribution and make further modifications as clinically needed. The performance of the AI plans was compared to the corresponding clinical plans.

RESULTS

The average plan generation time including manual operations was 10-15  min per case, with each AI model prediction taking ∼1 s. The six AI plans form a wide range of tradeoff choices between left and right parotids and between PTV and OARs compared with corresponding clinical plans, which correctly reflected their tradeoff designs.

CONCLUSION

The in-house AI IMRT treatment planning platform was developed and is available for clinical use at our institution. The process demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation.

摘要

背景与目的

描述用于头颈部(HN)调强放射治疗(IMRT)的内部人工智能(AI)治疗计划平台的临床应用情况。

材料与方法

人工智能计划平台由三个部分组成:(1)在商业治疗计划系统(TPS)框架内构建图形用户界面(GUI)。该GUI允许人工智能模型在配置了GPU加速的指定工作站上远程运行。(2)自动准备一个模板计划,其中涉及临床和人工智能方面的考虑因素,包括轮廓评估、等中心放置以及射束/准直器光阑放置。(3)一套精心编排的人工智能模型预测最佳注量图,将其导入TPS进行剂量计算,随后可进行可选的自动微调。六个人工智能模型在腮腺保护和计划靶区(PTV)-危及器官(OAR)偏好方面提供了灵活的权衡。计划者可以检查计划剂量分布,并根据临床需要进行进一步修改。将人工智能计划的性能与相应的临床计划进行比较。

结果

包括手动操作在内,平均每个病例的计划生成时间为10 - 15分钟,每个人工智能模型预测耗时约1秒。与相应的临床计划相比,六个人工智能计划在左右腮腺之间以及PTV和OAR之间形成了广泛的权衡选择,正确反映了它们的权衡设计。

结论

已开发出内部人工智能IMRT治疗计划平台,并在我们机构可供临床使用。该过程展示了人工智能平台出色的性能和稳健性,并提供了充分的验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/c42c0b3c8460/ACM2-26-e14558-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/2c003df607f0/ACM2-26-e14558-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/14c4b440c50e/ACM2-26-e14558-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/67371c222fc9/ACM2-26-e14558-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/c42c0b3c8460/ACM2-26-e14558-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/2c003df607f0/ACM2-26-e14558-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/14c4b440c50e/ACM2-26-e14558-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/67371c222fc9/ACM2-26-e14558-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4fe/11712748/c42c0b3c8460/ACM2-26-e14558-g001.jpg

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本文引用的文献

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Phys Imaging Radiat Oncol. 2023 Nov 17;28:100515. doi: 10.1016/j.phro.2023.100515. eCollection 2023 Oct.
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Artificial intelligence guided physician directive improves head and neck planning quality and practice Uniformity: A prospective study.人工智能引导的医生指令可提高头颈放疗计划质量和实践的一致性:一项前瞻性研究。
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Treatment plan prediction for lung IMRT using deep learning based fluence map generation.
基于深度学习的剂量图生成的肺部调强放疗治疗计划预测。
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Clinical implementation of automated treatment planning for whole-brain radiotherapy.全脑放射治疗自动化治疗计划的临床实施。
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Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies.基于体素剂量预测和优化后策略的直肠调强放射治疗自动治疗计划的临床应用
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Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.基于深度学习的放射治疗计划结构自动分割的实现:两个癌症中心的工作流程研究。
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