A Novel Lung Lobectomy Surgical Simulation Model

by Mary Sheridan Bilbao, PA-C, MPAS
January 30, 2025

Natalie Lui, MD, Assistant Professor of Cardiothoracic Surgery (Thoracic Surgery), Brandon Guenthart, MD, Clinical Assistant Professor of Cardiothoracic Surgery (Thoracic Surgery), and Carolyn Chang, MD, Postdoctoral Research Fellow in Thoracic Surgery, were recently awarded the 2024-2025 Stanford Teaching and Mentoring Academy (TMA) Innovation Grant. With this support, they developed an innovative, low-cost robotic lobectomy model. 

Featuring realistic elements like a heartbeat and vascular distention, this high-fidelity model offers surgeons at all levels a low-risk environment to hone their techniques and enhance their learning experience. With 14 trainee and faculty participants having tested the model so far, preliminary data show that participants thought most aspects of the model had high fidelity and participant confidence in performing robotic lobectomy significantly improved. Drs. Chang, Guenthart, and Lui were featured in the TMA’s recent announcement and Community Profiles.

The Academy is an inclusive, school-wide entity available to all those who teach and mentor our trainees – MD students, PhD students, residents, fellows, postdoctoral scholars and faculty. The TMA 2024-2025 Innovation Grants support projects aimed at innovation and scholarship in teaching, learning, and mentoring at Stanford Medicine.

About the Project: A Novel Lung Lobectomy Surgical Simulation Model

Simulation-based education is a powerful way to build confidence and refine skills. With support from the Teaching and Mentoring Grant at Stanford University, we developed an innovative, low-cost robotic lung lobectomy model. Featuring realistic elements like a heartbeat and vascular distention, this high-fidelity model offers surgeons at all levels a low-risk environment to hone their techniques and enhance their learning experience.

We obtained porcine heart and lung blocks from Animal Technologies. After performing a right pneumonectomy, we cannulated the SVC and LA to fill the vessels with a blood substitute. A linear actuator, positioned beneath the tissue block (resting on a perforated plastic crate), simulated a heartbeat by moving a metal rod up and down, while blood was infused to distend the vessels. The da Vinci Xi robotic system (Intuitive Surgical, Inc.) was docked above the model, allowing participants to perform three key steps of left lung lobectomy: dissecting and stapling the left upper lobe pulmonary vein, artery, and bronchus. The model was evaluated through surveys assessing fidelity, key features, and its value as a training tool on a scale of 1 to 5 (strongly disagree to strongly agree).

So far, we have had 14 trainee and faculty participants test our model. Preliminary data show that participants thought most aspects of the model had high fidelity, including stapling experience, lung tissue quality and handling, airway dissection, heartbeat, and vascular distention. Following the simulation, participants confidence in performing robotic lobectomy significantly improved [pre-simulation (3.09 ± 1.22) vs post-simulation (4.09 ± 0.70), p=0.0004]. Overall, our robotic lung lobectomy simulation model has demonstrated high fidelity and value as a training tool suggesting its potential effectiveness for surgeons with a wide range of experience. We have submitted our findings to the Western Thoracic Surgical Association and are excited to share our model and results.