3D airway geometry analysis of factors in airway navigation failure for lung nodules

Abstract

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.

Keywords: Bronchoscopy; Solitary pulmonary nodule; Tomography scanners; X-Ray computed.

Fig. 1

Flow chart of patient

Fig. 2

Overall workflow of the study

Fig. 3

Airway geometry features that exhibited significant differences between the success and failure groups

Fig. 4

Averaged receiver operating characteristic curve of each classifier in the test phase The area under the curve was 0.803, 0.781, and 0.750 for SVM, RF, and Logistic Lasso classifiers, respectively

Fig. 5

Comparison of representative cases. (a) Anterior, lateral, and posterior views of a failure case where the lesion is located in the superior segment of the right lower lobe, (b) Anterior, lateral, and posterior views of a success case where the lesion is located in the posterior segment of the right lower lobe. (c) Enlarged 10 mm × 10 mm × 10 mm region around tumor of failure case. (d) Enlarged 10 mm × 10 mm × 10 mm region around tumor of success case. The white numbers correspond to the unique index of each branch section