肺结节气道导航失败因素的三维气道几何分析。

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-07-04 DOI:10.1186/s40644-024-00730-7
Hwan-Ho Cho, Junsu Choe, Jonghoon Kim, Yoo Jin Oh, Hyunjin Park, Kyungjong Lee, Ho Yun Lee
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引用次数: 0

摘要

背景:本研究旨在通过三维(3D)空间的几何分析,定量揭示径向探头支气管内超声(R-EBUS)过程中气道导航失败的诱因,并探讨气道导航失败预测模型的临床可行性:我们回顾性分析了2017年1月至2018年12月期间接受R-EBUS检查的患者。使用开源 python 库(包括 Vascular Modeling Toolkit ( http://www.vmtk.org )、simple insight toolkit ( https://sitk.org )和 sci-kit image ( https://scikit-image.org ))构建的内部软件对几何量化进行了分析。我们使用基于机器学习的方法来探索这些重要因素的效用:在符合分析条件的 491 名患者(平均年龄 65 岁 +/- 11 [标准差];274 名男性)中,434 人达到目标病灶,57 人未达到目标病灶。根据倾向评分,27 名失败组患者与 27 名成功组患者进行了配对。目标分支的分叉角、最后一段的最小直径和最后一段的曲率是气道导航失败的最重要和最稳定的因素。支持向量机可以预测气道导航失败,平均曲线下面积为 0.803:三维空间几何分析表明,分叉角大、距离病变最近的支气管结构狭窄迂曲与 R-EBUS 过程中气道导航失败有关。利用定量计算机断层扫描成像建立的模型显示了预测气道导航失败的潜力。
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3D airway geometry analysis of factors in airway navigation failure for lung nodules.

Background: This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.

Methods: We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors.

Results: Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803.

Conclusions: Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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