E. Maneas*a (Dr), R. Thakrarb (Dr), N. Navania (Dr), A. Desjardinsa (Prof)

a University College London, London, UNITED KINGDOM ; b University College London Hospitals, London, UNITED KINGDOM

* efthymios.maneas@ucl.ac.uk

Background: Endobronchial ultrasound (EBUS) requires considerable training to attain proficiency in navigation and ultrasound image interpretation. Tissue-mimicking phantoms are essential for training in bronchoscopy as they replicate aspects of human anatomy and are well-suited for learners to gain necessary procedural skills. However, there are limitations in currently available EBUS training models, which include limited anatomical complexity, prohibitive costs, and incompatibility with needle insertions. Here, we present a new framework for developing anatomically realistic EBUS phantoms that are designed for mediastinal lymph node sampling.

Methods: The bronchial tree was extracted from anonymised patient Computed Tomography data. Computer aided design software was used for post-processing of the segmented structures. Lymph node structures of various sizes were created with 3D-printed moulds and tissue-mimicking materials (TMMs) with soft mechanical properties based on polyvinyl alcohol. Additives were added to imbue the TMMs with ultrasonic scattering at concentrations that were specific to different tissue structures. Lymph node structures were positioned adjacent to the bronchial tree to create training models for mediastinal staging. Phantom evaluation included correct identification and aspiration of lymph nodes of the paratracheal, subcarinal and hilar regions.

Results: The fabricated EBUS phantoms had realistic endobronchial and acoustic appearances. Lymph nodes appeared as homogenous, hypoechoic structures with distinct margins that could be readily differentiated from the relatively hyperechoic surrounding TMM mimicking lung parenchyma. Needles could clearly be visualised, and the phantoms allowed for practicing multiple aspirations of lymph nodes as needle tracks were not visible due to the unique self-healing property of the material.

Conclusion: This study addresses a prominent gap in EBUS training with novel methods for fabricating anatomically realistic lung phantoms. The framework developed in this study will lead to high-performance training phantoms that have strong potential to improve EBUS training.

Disclosure of funding source(s):

This work was funded by the Wellcome (203145Z/16/Z) and the Engineering and Physical Sciences Research Council (NS/A000050/1) in the United Kingdom.