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一种用于锥形束计算机断层扫描引导下肺癌放射治疗的图像配准新型评估模型。

A novel evaluation model of image registration for cone-beam computed tomography guided lung cancer radiotherapy.

作者信息

Liu Yimei, Chen Meining, Fang Jianlan, Xiao Liangjie, Liu Songran, Li Qiwen, Qiu Bo, Huang Runda, Zhang Jun, Peng Yinglin

机构信息

State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Radiation Oncology, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.

出版信息

Thorac Cancer. 2024 Jun;15(17):1333-1342. doi: 10.1111/1759-7714.15320. Epub 2024 Apr 30.

Abstract

BACKGROUND

The aim of the study was to establish a weighted comprehensive evaluation model (WCEM) of image registration for cone-beam computed tomography (CBCT) guided lung cancer radiotherapy that considers the geometric accuracy of gross target volume (GTV) and organs at risk (OARs), and assess the registration accuracy of different image registration methods to provide clinical references.

METHODS

The planning CT and CBCT images of 20 lung cancer patients were registered using diverse algorithms (bony and grayscale) and regions of interest (target, ipsilateral, and body). We compared the coverage ratio (CR) of the planning target volume (PTV) to GTV, as well as the dice similarity coefficient (DSC) of the GTV and OARs, considering the treatment position across various registration methods. Furthermore, we developed a mathematical model to assess registration results comprehensively. This model was evaluated and validated using CRFs across four automatic registration methods.

RESULTS

The grayscale registration method, coupled with the registration of the ipsilateral structure, exhibited the highest level of automatic registration accuracy, the DSC were 0.87 ± 0.09 (GTV), 0.71 ± 0.09 (esophagus), 0.74 ± 0.09 (spinal cord), and 0.91 ± 0.05 (heart), respectively. Our proposed WCEM proved to be both practical and effective. The results clearly indicated that the grayscale registration method, when applied to the ipsilateral structure, achieved the highest CRF score. The average CRF scores, excellent rates, good rate and qualification rates were 58 ± 26, 40%, 75%, and 85%, respectively.

CONCLUSIONS

This study successfully developed a clinically relevant weighted evaluation model for CBCT-guided lung cancer radiotherapy. Validation confirmed the grayscale method's optimal performance in ipsilateral structure registration.

摘要

背景

本研究的目的是建立一种用于锥形束计算机断层扫描(CBCT)引导的肺癌放疗图像配准的加权综合评价模型(WCEM),该模型考虑大体肿瘤体积(GTV)和危及器官(OARs)的几何准确性,并评估不同图像配准方法的配准准确性,以提供临床参考。

方法

使用不同算法(骨算法和灰度算法)和感兴趣区域(靶区、同侧和身体)对20例肺癌患者的计划CT图像和CBCT图像进行配准。我们比较了计划靶体积(PTV)与GTV的覆盖率(CR),以及GTV和OARs的骰子相似系数(DSC),同时考虑了各种配准方法下的治疗体位。此外,我们开发了一个数学模型来综合评估配准结果。该模型使用四种自动配准方法的CRF进行评估和验证。

结果

灰度配准方法结合同侧结构配准表现出最高水平的自动配准准确性,DSC分别为0.87±0.09(GTV)、0.71±0.09(食管)、0.74±0.09(脊髓)和0.91±0.05(心脏)。我们提出的WCEM被证明是实用且有效的。结果清楚地表明,灰度配准方法应用于同侧结构时,获得了最高的CRF分数。平均CRF分数、优秀率、良好率和合格率分别为58±26、40%、75%和85%。

结论

本研究成功开发了一种用于CBCT引导的肺癌放疗的临床相关加权评价模型。验证证实了灰度方法在同侧结构配准中的最佳性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6595/11168913/9c2f5c6eb755/TCA-15-1333-g003.jpg

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