Vivian E Strong1, Ashley E Russo2, Masaya Nakauchi2, Mark Schattner3, Luke V Selby2, Gabriel Herrera2, Laura Tang4, Mithat Gonen5. 1. Departments of Surgery, Gastric and Mixed Tumor Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. strongv@mskcc.org. 2. Departments of Surgery, Gastric and Mixed Tumor Service, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. 3. Departments of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4. Departments of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 5. Departments of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Abstract
BACKGROUND: While multiple Asian and a few Western retrospective series have demonstrated the feasibility and safety of robotic-assisted gastrectomy for gastric cancer, its reliability for thorough resection, especially for locoregional disease, has not yet been firmly established, and reported learning curves vary widely. To support wider implementation of robotic gastrectomy, we evaluated the learning curve for this approach, assessed its oncologic feasibility, and created a selection model predicting the likelihood of conversion to open surgery in a US patient population. PATIENTS AND METHODS: We retrospectively reviewed data on all consecutive patients who underwent robotic gastrectomy at a high-volume institution between May 2012 and March 2019. RESULTS: Of the 220 patients with gastric cancer selected to undergo curative-intent robotic gastrectomy, surgery was completed using robotics in 159 (72.3%). The median number of removed lymph nodes was 28, and ≥ 15 lymph nodes were removed in 94% of procedures. Surgical time decreased steadily over the first 60-80 cases. Complications were generally minor: 7% of patients experienced complications of grade 3 or higher, with an anastomotic leak rate of 2% and mortality rate 0.9%. Factors predicting conversion to open surgery included neoadjuvant chemotherapy, BMI ≥ 31 kg/m2, and tumor size ≥ 6 cm. CONCLUSIONS: These findings support the safety and oncologic feasibility of robotic gastrectomy for selected patients with gastric cancer. Proficiency can be achieved by 20 cases and mastery by 60-80 cases. Ideal candidates for this approach are patients with few comorbidities, BMI < 31 kg/m2, and tumors < 6 cm.
BACKGROUND: While multiple Asian and a few Western retrospective series have demonstrated the feasibility and safety of robotic-assisted gastrectomy for gastric cancer, its reliability for thorough resection, especially for locoregional disease, has not yet been firmly established, and reported learning curves vary widely. To support wider implementation of robotic gastrectomy, we evaluated the learning curve for this approach, assessed its oncologic feasibility, and created a selection model predicting the likelihood of conversion to open surgery in a US patient population. PATIENTS AND METHODS: We retrospectively reviewed data on all consecutive patients who underwent robotic gastrectomy at a high-volume institution between May 2012 and March 2019. RESULTS: Of the 220 patients with gastric cancer selected to undergo curative-intent robotic gastrectomy, surgery was completed using robotics in 159 (72.3%). The median number of removed lymph nodes was 28, and ≥ 15 lymph nodes were removed in 94% of procedures. Surgical time decreased steadily over the first 60-80 cases. Complications were generally minor: 7% of patients experienced complications of grade 3 or higher, with an anastomotic leak rate of 2% and mortality rate 0.9%. Factors predicting conversion to open surgery included neoadjuvant chemotherapy, BMI ≥ 31 kg/m2, and tumor size ≥ 6 cm. CONCLUSIONS: These findings support the safety and oncologic feasibility of robotic gastrectomy for selected patients with gastric cancer. Proficiency can be achieved by 20 cases and mastery by 60-80 cases. Ideal candidates for this approach are patients with few comorbidities, BMI < 31 kg/m2, and tumors < 6 cm.
Authors: Kaitlyn J Kelly; Luke Selby; Joanne F Chou; Katerina Dukleska; Marinela Capanu; Daniel G Coit; Murray F Brennan; Vivian E Strong Journal: Ann Surg Oncol Date: 2015-01-29 Impact factor: 5.344
Authors: Chang Hak Yoo; Hyung Ook Kim; Sang Il Hwang; Byung Ho Son; Jun Ho Shin; Hungdai Kim Journal: Surg Endosc Date: 2009-01-27 Impact factor: 4.584
Authors: Vivian E Strong; Sepideh Gholami; Manish A Shah; Laura H Tang; Yelena Y Janjigian; Mark Schattner; Luke V Selby; Sam S Yoon; Erin Salo-Mullen; Zsofia K Stadler; David Kelsen; Murray F Brennan; Daniel G Coit Journal: Ann Surg Date: 2017-12 Impact factor: 12.969
Authors: Andrea Coratti; Mario Annecchiarico; Michele Di Marino; Edoardo Gentile; Francesco Coratti; Pier Cristoforo Giulianotti Journal: World J Surg Date: 2013-12 Impact factor: 3.352
Authors: Yuki Hirata; Russell G Witt; Laura R Prakash; Elsa M Arvide; Kristen A Robinson; Vijaya Gottumukkala; Ching-Wei D Tzeng; Paul Mansfield; Brian D Badgwell; Naruhiko Ikoma Journal: Ann Surg Oncol Date: 2022-05-04 Impact factor: 4.339
Authors: Tom Mala; Dag Førland; Caroline Skagemo; Tom Glomsaker; Hans Olaf Johannessen; Egil Johnson Journal: BMC Surg Date: 2022-04-09 Impact factor: 2.102