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定量平面肺灌注闪烁显像和断层扫描在识别晚期慢性阻塞性肺疾病气肿型患者目标肺叶中的作用:一项回顾性横断面研究

Role of quantitative planar lung perfusion scintigraphy and tomography in identifying target lobes in patients with emphysematous phenotype of advanced chronic obstructive pulmonary disease: a retrospective cross-sectional study.

作者信息

Demirkol Baris, Cortuk Mustafa, Tanriverdi Elif, Gul Sule, Eren Ramazan, Alcin Goksel, Baydili Kursad Nuri, Cetinkaya Erdogan

机构信息

Department of Chest Diseases, University of Health Sciences Turkey, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.

Department of Chest Diseases, University of Health Sciences Turkey, Yedikule Chest Diseases and Thoracic Surgery Education and Research Hospital, Istanbul, Turkey.

出版信息

Quant Imaging Med Surg. 2024 Sep 1;14(9):6425-6435. doi: 10.21037/qims-24-125. Epub 2024 Aug 19.

DOI:10.21037/qims-24-125
PMID:39281166
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11400645/
Abstract

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease. Despite optimal medical therapy and pulmonary rehabilitation, bronchoscopic and surgical lung volume reduction may still be necessary. Identifying the target lobe is crucial for the success of these treatments. This study aims to compare the role of quantitative planar lung perfusion scintigraphy (QPLPS) with quantitative lung computed tomography (StratX), which is used in identifying the target lobe before the Zephyr endobronchial valve (EBV) placement in patients with the emphysematous phenotype of advanced COPD.

METHODS

A single-center retrospective cross-sectional study was performed in the Department of Pulmonology at the University of Health Sciences Turkey, Yedikule Chest Diseases and Thoracic Surgery Education and Research Hospital between June 2019 and June 2022. The study included 46 patients with the emphysematous phenotype of advanced COPD, who were all candidates for Zephyr EBV therapy. The target lobes were assessed using the QPLPS and StratX and the agreement between the methods was analyzed by the Kappa statistic method. Additionally, demographic characteristics, respiratory function tests, distributions of emphysema, and 6-minute walk test results of patients were recorded.

RESULTS

The median age was 67 (42-80) years and 42 (91.3%) were male. In QPLPS, the perfusion percentages were 7.47%±3.31%, 9.59%±2.67%, and 13.32%±2.59% for the 1st, 2nd and 3rd target lobes, respectively while in StratX, the voxel densities were 68.28%±9.16%, 63.79%±7.42%, and 60.69%±5.35%. In StratX, the fissure integrity (FI) at the target lobe was 76.25%±21.18%, 84.68%±17.67%, and 86.19%±13.19%, respectively. There was a significant agreement between the methods in identifying the first, second, and third target lobes in all patients (Kappa coefficient: 0.897, 0.700, and 0.522), and also in identifying the first and second target lobes in patients with heterogeneous (Kappa coefficient: 0.879, and 0.735), and homogeneous subgroups (Kappa coefficient: 0.919, and 0.672).

CONCLUSIONS

There is an agreement between QPLPS and StratX in identifying the target lobe in patients with severe emphysema, including those with homogeneous diseases. However, StratX may be preferred, considering that it also predicts FI.

摘要

背景

慢性阻塞性肺疾病(COPD)是一种常见、可预防且可治疗的疾病。尽管采用了最佳药物治疗和肺康复措施,但支气管镜检查和手术性肺减容术可能仍有必要。确定目标肺叶对于这些治疗的成功至关重要。本研究旨在比较定量平面肺灌注闪烁显像(QPLPS)与定量肺计算机断层扫描(StratX)在晚期COPD肺气肿表型患者中于Zephyr支气管内瓣膜(EBV)置入术前确定目标肺叶方面的作用。

方法

2019年6月至2022年6月在土耳其健康科学大学叶迪库勒胸科疾病与胸外科教育与研究医院肺病科进行了一项单中心回顾性横断面研究。该研究纳入了46例晚期COPD肺气肿表型患者,他们均为Zephyr EBV治疗的候选者。使用QPLPS和StratX评估目标肺叶,并通过Kappa统计方法分析两种方法之间的一致性。此外,记录患者的人口统计学特征、呼吸功能测试、肺气肿分布及6分钟步行试验结果。

结果

中位年龄为67(42 - 80)岁,42例(91.3%)为男性。在QPLPS中,第1、2和3个目标肺叶的灌注百分比分别为7.47%±3.31%、9.59%±2.67%和13.32%±2.59%,而在StratX中,体素密度分别为68.28%±9.16%、63.79%±7.42%和60.69%±5.35%。在StratX中,目标肺叶的裂完整性(FI)分别为76.25%±21.18%、84.68%±17.67%和86.19%±13.19%。在所有患者中,两种方法在确定第1、2和3个目标肺叶方面有显著一致性(Kappa系数:0.897、0.700和0.522),在异质性(Kappa系数:0.879和0.735)及同质性亚组患者中确定第1和2个目标肺叶时也有显著一致性(Kappa系数:0.919和- 0.672)。

结论

在确定重度肺气肿患者(包括疾病同质性患者)的目标肺叶方面,QPLPS和StratX之间存在一致性。然而,考虑到StratX还能预测FI,可能更倾向于使用StratX。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/5618372887c2/qims-14-09-6425-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/72055d0362f7/qims-14-09-6425-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/d757ce423576/qims-14-09-6425-f3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/5618372887c2/qims-14-09-6425-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/72055d0362f7/qims-14-09-6425-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/bfe8bec936a6/qims-14-09-6425-f2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/29e5b775ce65/qims-14-09-6425-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f3f/11400645/5618372887c2/qims-14-09-6425-f5.jpg

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