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CC BY-NC-ND 4.0 · Thorac Cardiovasc Surg
DOI: 10.1055/a-2702-2239
Original Thoracic

A Novel Competency-Based Simulation Model for Thoracoscopic Lung Resection

Ganwei Liu   *
1   Department of Thoracic Surgery, Peking University People's Hospital, Beijing, People's Republic of China
2   Institute of Advanced Clinical Medicine, Peking University, Beijing, People's Republic of China
,
Feng Yang   *
1   Department of Thoracic Surgery, Peking University People's Hospital, Beijing, People's Republic of China
2   Institute of Advanced Clinical Medicine, Peking University, Beijing, People's Republic of China
,
Zuli Zhou
1   Department of Thoracic Surgery, Peking University People's Hospital, Beijing, People's Republic of China
2   Institute of Advanced Clinical Medicine, Peking University, Beijing, People's Republic of China
,
Guanchao Jiang
1   Department of Thoracic Surgery, Peking University People's Hospital, Beijing, People's Republic of China
2   Institute of Advanced Clinical Medicine, Peking University, Beijing, People's Republic of China
› Institutsangaben
Funding This work was supported by the National Natural Science Foundation of China (61877001) and the Peking University People's Hospital Scientific Research Development Funds (RDE2024-10).
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Abstract

Background

Simulation-based thoracic surgery training is increasingly incorporating physical models to enhance traditional learning methods. Conventional box trainers, though useful for basic skills, often lack anatomical accuracy and tactile feedback, limiting their relevance for complex procedures like thoracoscopic lung resection. High-fidelity 3D-printed lung models offer realistic anatomy and procedural flow, but their educational impact remains underexplored.

Methods

Fifty-two surgical residents without prior thoracoscopic experience were randomly assigned to a high-fidelity lung model group or a conventional Fundamentals of Laparoscopic Surgery (FLS) box trainer group. All participants completed a baseline thoracic anatomy test and received standardized educational materials. The lung model group received structured simulation training on procedural anatomy and operative steps, while the FLS group practiced fundamental laparoscopic tasks. After training, participants repeated the anatomy test and performed a thoracoscopic lung wedge resection in a live animal model. Performance was assessed using the Objective Structured Assessment of Technical Skill (OSATS) and a 5-point confidence scale.

Results

A total of 52 surgical residents participated in the study, with 26 assigned to the high-fidelity lung model group and 26 to the FLS trainer group. Baseline anatomy scores were similar between groups (65.42  ±  6.10 vs. 66.12  ±  5.92; p  =  0.710). Posttraining, the lung model group showed greater gains in anatomy comprehension (87.60  ±  4.75 vs. 78.19  ±  5.54; p  <  0.001), higher OSATS scores (19.18  ±  2.43 vs. 15.41  ±  2.41; p  <  0.001), and increased confidence (3.13  ±  0.61 vs. 2.27  ±  0.68; p  =  0.002).

Conclusion

High-fidelity 3D-printed lung models significantly enhance anatomical understanding, thoracoscopic skills, and confidence compared with conventional box trainers. These results support integrating anatomically accurate simulation into thoracic surgical education to improve both cognitive and psychomotor outcomes.

Keywords

high-fidelity simulation - thoracoscopic wedge resection - surgical education - technical skill acquisition - trainee confidence

Data Availability Statement

All original data are available upon reasonable request to the corresponding author.


Ethical Approval Statement

The project was reviewed and approved by the Institutional Review Board (IRB) of Peking University People's Hospital, Beijing, China.


Authors' Contribution

G.L. and F.Y. designed this study. G.L., F.Y., and Z.Z. collected the data. G.L. and Z.Z. analyzed and interpreted the data. G.L. and F.Y. wrote the manuscript. G.J. revised the manuscript. All authors have provided final approval for the version of the manuscript.


* These authors contributed equally to the paper.




Publikationsverlauf

Eingereicht: 28. Juni 2025

Angenommen: 09. September 2025

Accepted Manuscript online:
16. September 2025

Artikel online veröffentlicht:
25. September 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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