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Related Experiment Video

Updated: Nov 18, 2025

Robot-assisted Total Mesorectal Excision and Lateral Pelvic Lymph Node Dissection for Locally Advanced Middle-low Rectal Cancer
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Learning Curve for Robotic-Assisted Laparoscopic Retroperitoneal Lymph Node Dissection.

Sophia M V Schermerhorn1, Matthew S Christman1, Nicholas R Rocco1

  • 1Urology Department, Naval Medical Center San Diego, San Diego, California, USA.

Journal of Endourology
|February 9, 2021
PubMed
Summary
This summary is machine-generated.

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Surgical experience with robotic retroperitoneal lymph node dissection (R-RPLND) improves operative times and reduces complications. Learning this complex procedure leads to better outcomes over time.

Area of Science:

  • Urology
  • Surgical Oncology
  • Robotic Surgery

Background:

  • Robotic retroperitoneal lymph node dissection (R-RPLND) is a complex surgical procedure.
  • The learning curve for R-RPLND may impact surgical efficiency and patient outcomes.
  • Understanding factors influencing R-RPLND performance is crucial for optimizing its application.

Purpose of the Study:

  • To investigate the impact of surgical experience on operative times and complication rates in R-RPLND.
  • To identify predictors of surgical efficiency and complications in R-RPLND procedures.

Main Methods:

  • Retrospective review of 121 R-RPLND cases performed by 4 fellowship-trained robotic surgeons (2008-2018).
  • Linear and logistic regression analyses were used to assess the relationship between sequential case number, surgeon, and various clinical factors with operative outcomes.
Keywords:
R-RPLNDlearning curverobotic

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  • Outcomes analyzed included setup time, operative time, lymph node yield, open conversions, and complication rates (overall and high-grade).

    • Main Results:

    • Operative times significantly decreased with increasing case number (p < 0.001), with an estimated 1-hour reduction after 44 cases.
    • Overall complication rates decreased with experience (p = 0.001), but high-grade complications were not predictable.
    • Setup time showed no change with experience but varied between surgeons; lymph node counts were surgeon-dependent.

    Conclusions:

    • Surgical experience is a key factor in improving operative times and reducing overall complications for R-RPLND.
    • Consistent with other advanced robotic techniques, a learning curve exists for R-RPLND, leading to enhanced efficiency and safety.
    • Further research may focus on standardizing R-RPLND techniques to mitigate variability and predict high-grade complications.