2026/07/28 by Sean Hormozian, Angel Guan, So Un Kim +8
paper · doi:10.1177/00031348261472845
crossref issued 2026/07/28 · crossref published 2026/07/28 · crossref published-online 2026/07/28 · crossref created 2026/07/28 · crossref deposited 2026/07/30 · crossref indexed 2026/07/30
Background Low rectal tumors present significant technical challenges due to confined pelvic anatomy and limited maneuverability. Robotic surgery offers improved visualization and instrument articulation, which may mitigate these challenges. Studies have shown comparable results between robotic, laparoscopic, or open surgery for low rectal tumors; however, the impact of tumor height on outcomes within robotic colorectal surgery alone remains unclear. Methods A retrospective cohort study was conducted on patients undergoing robotic rectal resection at a single institution. Tumors were categorized by distance from the anal verge into low/medium high groups. Outcomes included oncologic measures (distal margin, radial margin positivity, and lymph node yield >12), intraoperative variables (diversion, port number, operative time, and estimated blood loss), and postoperative outcomes (anastomotic leak, 30-day readmission, and length of stay). Multivariable models adjusted for age, BMI, comorbidity burden, and whether patients received neoadjuvant therapy. Results Tumor height was not associated with differences in radial margin positivity ( P = 0.809) or lymph node yield ≥ 12 ( P = 0.652). High tumors were associated with a greater distal margin ( P = 0.019) and shorter operative time ( P = 0.047). Other intraoperative and postoperative outcomes were comparable between groups. Length of stay trended shorter in high tumors but did not reach statistical significance ( P = 0.056). Discussion Robotic rectal cancer resection achieves equivalent oncologic and perioperative outcomes across tumor heights, despite the increased technical complexity of lower rectal tumors, supporting its use in challenging pelvic surgery. In our cohort, tumor distance predicts operative demands without compromising outcomes or oncological adequacy.