From Laparoscopy to Robotics: Navigating the Learning Curve in Colon Cancer Surgery Within a High-Volume East London District General Hospital

Abstract

Background

Robotic-assisted surgery (RAS) is widely adopted, and understanding the learning curve (LC) is crucial for surgeons self-assessment and credentialling. We characterised the transition from laparoscopy to robotic approach for three experienced surgeons based on analysis of colectomies performed for malignancy.

Methods

We analysed retrospectively all consecutive robotic colon cancer surgeries performed between February 2020 and April 2025. We excluded rectal cancer resections, palliative, multivisceral and beyond TME cases. The cumulative summation (CUSUM) methodology was utilised to graphically represent the colectomy LC based on total operative time.

Results

Our analysis included 184 colectomies: 75 performed by surgeon 1, 65 by surgeon 2, and 44 by surgeon 3. Demographics data and tumour characteristics were comparable between surgeons, exempt from operative time and previous abdomino-pelvic surgery (P < .05). The CUSUM curve revealed a three-phase pattern: a learning phase (cases 1–16), a competence phase (cases 17–29), and a proficiency phase (beyond case 30). The median total operative time trended downward from the learning phase (240 min) to the proficiency phase (218 min), though this was not statistically significant (P = .13). We also observed a trend towards reduced estimated blood loss (109 mL to 86 mL, P = .066) and a decrease in 30-day re-admission rates from 14% to 4% (P = .11) in the proficiency phase.

Conclusion

We present our department LC with the DaVinci Xi Robot for surgical treatment of colonic cancer. Based on our calculations, it is necessary for a surgeon to perform 30 robotic cases to reach the proficiency part of the LC of RAS for colonic resections.

Keywords

colectomy colonic neoplasms adenocarcinoma robotic surgical procedures learning curve

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