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磁共振引导下口咽大体肿瘤体积和高危临床靶区勾画变异性的综合定量评估:一项 R-IDEAL 阶段 0 前瞻性研究。

Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study.

机构信息

Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.

Department of Radiation Oncology, Klinikum Kassel, Kassel, Germany.

出版信息

Int J Radiat Oncol Biol Phys. 2022 Jun 1;113(2):426-436. doi: 10.1016/j.ijrobp.2022.01.050. Epub 2022 Feb 4.

DOI:10.1016/j.ijrobp.2022.01.050
PMID:35124134
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9119288/
Abstract

PURPOSE

Tumor and target volume manual delineation remains a challenging task in head and neck cancer radiation therapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation magnetic resonance scans of patients with oropharyngeal cancer.

METHODS AND MATERIALS

We retrospectively collected pretreatment T1-weighted, T1-weighted with gadolinium contrast, and T2-weighted magnetic resonance imaging scans for 4 patients with oropharyngeal cancer under an institution review board-approved protocol. We provided the scans to 26 radiation oncologists from 7 international cancer centers that participated in this delineation study. We also provide the patients' clinical history and physical examination findings, along with a medical photographic image and radiologic results. We used both the Simultaneous Truth and Performance Level Estimation algorithm and pair-wise comparisons of the contours, using overlap/distance metrics. Lastly, to assess experience and CTV delineation institutional practices, we had participants complete a brief questionnaire.

RESULTS

Large variability was measured between observers' delineations for GTVs and CTVs. The mean Dice similarity coefficient values across all physicians' delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for Simultaneous Truth and Performance Level Estimation algorithm comparison, and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on magnetic resonance imaging was 3.5 years. The GTV-to-CTV margin used was 10 mm for 6 of 7 participant institutions. One institution used 8 mm, and 3 participants (from 3 different institutions) used a margin of 5 mm.

CONCLUSIONS

The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of magnetic resonance-based treatment planning for head and neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.

摘要

目的

在头颈部癌症放射治疗中,肿瘤和靶区的手动勾画仍然是一项具有挑战性的任务。本研究的目的是对接受过治疗的磁共振扫描的头颈部癌症患者的大体肿瘤体积(GTV)、高危临床靶区(CTV)、腮腺和颌下腺进行多机构评估。

方法和材料

我们根据机构审查委员会批准的方案,回顾性地收集了 4 例口咽癌患者的预处理 T1 加权、钆增强 T1 加权和 T2 加权磁共振成像扫描。我们将这些扫描提供给了参与这项勾画研究的来自 7 个国际癌症中心的 26 名放射肿瘤学家。我们还提供了患者的临床病史和体格检查结果,以及医学摄影图像和放射学结果。我们使用同时真实性和性能水平估计算法以及基于重叠/距离度量的轮廓对比较来进行分析。最后,为了评估经验和 CTV 勾画的机构实践,我们让参与者完成了一份简短的问卷。

结果

观察者对 GTV 和 CTV 的勾画存在很大的差异。所有医生勾画的 GTVp、GTVn、CTVp 和 CTVn 的平均 Dice 相似系数值分别为 0.77、0.67、0.77 和 0.69,Simultaneous Truth and Performance Level Estimation 算法比较结果为 0.67、0.60、0.67 和 0.58,对比较结果为。当考虑重叠/距离度量时,正常组织轮廓的定义更加一致。放射肿瘤学临床经验中位数为 7 年。勾画磁共振成像的中位数经验为 3.5 年。7 个参与机构中有 6 个使用了 10mm 的 GTV 到 CTV 边界,1 个机构使用了 8mm,3 个参与者(来自 3 个不同的机构)使用了 5mm 的边界。

结论

这项研究的数据表明,对于采用基于磁共振的头颈部癌症治疗计划,仍然需要适当的指南、勾画质量保证会议和培训。这些努力应该在减少勾画差异和确保靶区设计在临床实践中的标准化方面发挥关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/f3a9d497af75/nihms-1801452-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/a14d7bffe3fb/nihms-1801452-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/20a898e9c1cd/nihms-1801452-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/ecb58b396060/nihms-1801452-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/6b69d6a7bd47/nihms-1801452-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/f3a9d497af75/nihms-1801452-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/a14d7bffe3fb/nihms-1801452-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/20a898e9c1cd/nihms-1801452-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/ecb58b396060/nihms-1801452-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/6b69d6a7bd47/nihms-1801452-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e04d/9119288/f3a9d497af75/nihms-1801452-f0005.jpg

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2
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3
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4
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Initial Feasibility and Clinical Implementation of Daily MR-Guided Adaptive Head and Neck Cancer Radiation Therapy on a 1.5T MR-Linac System: Prospective R-IDEAL 2a/2b Systematic Clinical Evaluation of Technical Innovation.
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