Background <p>Complete mesocolic excision (CME) for right-sided colon cancer offers oncological advantages but remains technically demanding, which limits its widespread adoption. Understanding the learning curve (LC) for this complex procedure is essential to ensure safe implementation and to guide structured surgical training. This systematic review aims to evaluate the learning curve of minimally invasive CME for right-sided colon cancer, focusing on operative metrics and complications, and estimating the number of cases required to achieve proficiency.</p> Methods <p>This systematic review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024615748). A comprehensive search across six databases included studies utilizing CUSUM analysis to evaluate the LC of minimally invasive CME. Outcomes of interest included operative time, complications, pathological quality, and oncological metrics. Studies with fewer than ten cases or lacking LC focus were excluded. Risk of bias was assessed using the ROBINS-I framework.</p> Results <p>Seven studies were included, comprising laparoscopic and robotic CME. Proficiency thresholds ranged from 21 to 32 cases, with consistent reductions in operative time across studies. Complication rates varied (3.6–13%) but remained within acceptable limits in most series. Lymph node harvest was adequate even during early phases, with minor variations between studies. Heterogeneity in study design and reporting limited meta-analysis but highlighted the utility of standardized CUSUM analysis for evaluating LCs.</p> Conclusions <p>This review establishes a consistent proficiency plateau for minimally invasive CME at 21–32 cases, supporting its safety and efficacy even during initial phases. However, methodological variability and lack of standardized reporting underscore the need for further research and validated training programs to optimize outcomes and ensure broader adoption.</p>

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Learning curve of minimally invasive complete mesocolic excision for right-sided colon cancer: a systematic review

  • Cristián Jarry,
  • Christophe Riquoir,
  • Javier Vela,
  • Felipe Bellolio,
  • Satish Warrier,
  • Alexander Heriot,
  • José Tomás Larach

摘要

Background

Complete mesocolic excision (CME) for right-sided colon cancer offers oncological advantages but remains technically demanding, which limits its widespread adoption. Understanding the learning curve (LC) for this complex procedure is essential to ensure safe implementation and to guide structured surgical training. This systematic review aims to evaluate the learning curve of minimally invasive CME for right-sided colon cancer, focusing on operative metrics and complications, and estimating the number of cases required to achieve proficiency.

Methods

This systematic review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024615748). A comprehensive search across six databases included studies utilizing CUSUM analysis to evaluate the LC of minimally invasive CME. Outcomes of interest included operative time, complications, pathological quality, and oncological metrics. Studies with fewer than ten cases or lacking LC focus were excluded. Risk of bias was assessed using the ROBINS-I framework.

Results

Seven studies were included, comprising laparoscopic and robotic CME. Proficiency thresholds ranged from 21 to 32 cases, with consistent reductions in operative time across studies. Complication rates varied (3.6–13%) but remained within acceptable limits in most series. Lymph node harvest was adequate even during early phases, with minor variations between studies. Heterogeneity in study design and reporting limited meta-analysis but highlighted the utility of standardized CUSUM analysis for evaluating LCs.

Conclusions

This review establishes a consistent proficiency plateau for minimally invasive CME at 21–32 cases, supporting its safety and efficacy even during initial phases. However, methodological variability and lack of standardized reporting underscore the need for further research and validated training programs to optimize outcomes and ensure broader adoption.