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The Multi-Dimensional Learning Curve in Robotic-Assisted vs. Laparoscopic Rectal Cancer Resection: A Systematic
Greeshma Arun Kumar1, Gargi Sharma2, Brahmini Arun Kumar3
1College of Medicine, Gulf Medical University, Ajman, UAE. greeshmaaarun@gmail.com.
None:
The adoption of robotic-assisted surgery (RAS) for rectal cancer is governed by a steep learning curve (LC). This systematic review evaluates the "Price of Proficiency" by comparing RAS to conventional laparoscopic surgery (CLS) across temporal, oncological, and ergonomic dimensions. A systematic search of PubMed, Embase, and Scopus (2014-2026) identified 24 studies utilizing Cumulative Sum (CUSUM) or Risk-Adjusted CUSUM (RA-CUSUM) modeling. Methodological quality was appraised using MINORS and Cochrane RoB 2.0 tools. Synthesis of >3,500 procedures revealed a triphasic LC. Phase I (Learning: cases 1-25) demonstrated high operative times but remarkably low conversion rates (1.0%-3.2%) compared to historical CLS averages (12.2%- 15.0%). Technical proficiency (Phase II) stabilized by case 35, while Phase III (Mastery: >50 cases) was characterized by "Complexity Drift," where surgeons tackled high-BMI and post-radiation cases without compromising outcomes. Oncological quality, including TME completeness (91.9%-97.0%) and CRM negativity, remained stable throughout all LC phases. Mastery was associated with higher nodal yield and an increased likelihood of achieving a "Textbook Outcome". The robotic platform decouples procedural speed from surgical safety, providing an immediate "safety net" that protects oncological integrity during the initial learning phase. While RAS requires a significant temporal investment, its superior ergonomics and lower conversion rates support its transition as the standard for minimally invasive rectal cancer resection.
