Daniel J Kiely1, Walter H Gotlieb, Kris Jardon, Susie Lau, Joshua Z Press. 1. From the Departments of Gynecologic Oncology (D.J.K., W.H.G.), and Surgical Oncology (W.H.G.), McGill University-Jewish General Hospital; and Department of Gynecologic Oncology (K.J.), McGill University-Royal Victoria Hospital; and Department of Gynecologic Oncology (D.J.K.), Centre hospitalier de l'Université de Montréal, Hôspital Notre-Dame, Montreal, Quebec, Canada; (J.Z.P.) Division of Oncology and Pelvic Surgery, Pacific Gynecology Specialists, Seattle, WA.
Abstract
INTRODUCTION: Pelvic lymphadenectomy is a key component of the surgical treatment of several gynecologic cancers and involves mastery of complex anatomic relationships. Our aim was to demonstrate that the anatomy relevant to robotic pelvic lymphadenectomy can be modeled using low-cost techniques, thereby enabling simulation focused on surgical dissection, a task that integrates technical skills and anatomic knowledge. METHODS: A model of pelvic lymphadenectomy was constructed through experimentation with several different materials and a number of prototypes. In the final version, blood vessels were simulated by rubber tubing stented with wire and lymph nodes by cotton balls. Adipose and areolar tissue were simulated by a gelatin solution poured into the model and then allowed to cool and semisolidify. Three gynecologic oncologists and 2 gynecologic oncology fellows dissected the model using the surgical robot (da Vinci Surgical System) and completed a structured questionnaire. Five additional gynecologic oncologists assessed the model at a national conference. RESULTS: The model received high ratings for face and content validity. Median ratings were almost all 4 of 5 or higher (range, 3-5). Participants who dissected the model (n = 5) unanimously rated it as "useful for training throughout residency and fellowship." CONCLUSIONS: A novel low-cost inanimate model of pelvic lymphadenectomy has been developed and rated highly for face and content validity. This model may permit more regular simulation sessions compared with alternatives such as cadaveric dissection and animal laboratories, thereby complementing them and facilitating distributed practice.
INTRODUCTION: Pelvic lymphadenectomy is a key component of the surgical treatment of several gynecologic cancers and involves mastery of complex anatomic relationships. Our aim was to demonstrate that the anatomy relevant to robotic pelvic lymphadenectomy can be modeled using low-cost techniques, thereby enabling simulation focused on surgical dissection, a task that integrates technical skills and anatomic knowledge. METHODS: A model of pelvic lymphadenectomy was constructed through experimentation with several different materials and a number of prototypes. In the final version, blood vessels were simulated by rubber tubing stented with wire and lymph nodes by cotton balls. Adipose and areolar tissue were simulated by a gelatin solution poured into the model and then allowed to cool and semisolidify. Three gynecologic oncologists and 2 gynecologic oncology fellows dissected the model using the surgical robot (da Vinci Surgical System) and completed a structured questionnaire. Five additional gynecologic oncologists assessed the model at a national conference. RESULTS: The model received high ratings for face and content validity. Median ratings were almost all 4 of 5 or higher (range, 3-5). Participants who dissected the model (n = 5) unanimously rated it as "useful for training throughout residency and fellowship." CONCLUSIONS: A novel low-cost inanimate model of pelvic lymphadenectomy has been developed and rated highly for face and content validity. This model may permit more regular simulation sessions compared with alternatives such as cadaveric dissection and animal laboratories, thereby complementing them and facilitating distributed practice.
Authors: Daniel J Kiely; Walter H Gotlieb; Susie Lau; Xing Zeng; Vanessa Samouelian; Agnihotram V Ramanakumar; Helena Zakrzewski; Sonya Brin; Shannon A Fraser; Pira Korsieporn; Laura Drudi; Joshua Z Press Journal: J Robot Surg Date: 2015-05-16
Authors: Michael I Hanzly; Tareq Al-Tartir; Syed Johar Raza; Atif Khan; Mohammad Manan Durrani; Thomas Fiorica; Phillip Ginsberg; James L Mohler; Boris Kuvshinoff; Khurshid A Guru Journal: Curr Urol Rep Date: 2015-06 Impact factor: 3.092
Authors: Daniel M Costello; Isabel Huntington; Grace Burke; Brooke Farrugia; Andrea J O'Connor; Anthony J Costello; Benjamin C Thomas; Philip Dundee; Ahmed Ghazi; Niall Corcoran Journal: J Robot Surg Date: 2021-09-03