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From Laparoscopy to Robotics: Navigating the Learning Curve in Colon Cancer Surgery Within a High-Volume East London
Valentin Butnari1,2, Jatinder Hayre2, Olivia Pestrin2
1National Bowel Research Centre, The Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
Surgical Innovation
|October 28, 2025
Summary
Surgeons need to complete approximately 30 robotic-assisted colectomies to achieve proficiency. This learning curve analysis for robotic surgery in colon cancer shows distinct learning, competence, and proficiency phases.
Area of Science:
- Surgical Oncology
- Minimally Invasive Surgery
- Medical Education
Background:
- Robotic-assisted surgery (RAS) is increasingly adopted in colectomies for malignancy.
- Understanding the learning curve (LC) is vital for surgeon training and credentialing in RAS.
- Characterizing the transition from laparoscopy to robotic approaches is essential for optimizing surgical practice.
Purpose of the Study:
- To characterize the learning curve for robotic-assisted colectomies in colon cancer surgery.
- To identify the number of cases required for surgeons to reach proficiency in this procedure.
- To analyze operative time, blood loss, and readmission rates during the learning curve.
Main Methods:
- Retrospective analysis of 184 consecutive robotic colon cancer resections performed between February 2020 and April 2025.
- Exclusion of rectal cancer, palliative, multivisceral, and beyond TME cases.
- Utilization of cumulative summation (CUSUM) methodology to assess the colectomy learning curve based on total operative time.
Main Results:
- The CUSUM analysis revealed three phases: learning (cases 1-16), competence (cases 17-29), and proficiency (beyond case 30).
- Median operative time decreased from 240 minutes in the learning phase to 218 minutes in the proficiency phase.
- Trends showed reduced estimated blood loss and lower 30-day readmission rates in the proficiency phase.
Conclusions:
- A minimum of 30 robotic cases are necessary for a surgeon to achieve proficiency in robotic-assisted colectomies for colon cancer.
- The findings provide a benchmark for the learning curve in robotic colorectal surgery.
- This data supports the development of training programs and credentialing standards for robotic surgery.
