Establishing a sentinel lymph node mapping algorithm for the treatment of early cervical cancer. Academic Article uri icon

Overview

abstract

  • OBJECTIVE: To establish an algorithm that incorporates sentinel lymph node (SLN) mapping to the surgical treatment of early cervical cancer, ensuring that lymph node (LN) metastases are accurately detected but minimizing the need for complete lymphadenectomy (LND). METHODS: A prospectively maintained database of all patients who underwent SLN procedure followed by a complete bilateral pelvic LND for cervical cancer (FIGO stages IA1 with LVI to IIA) from 03/2003 to 09/2010 was analyzed. The surgical algorithm we evaluated included the following: 1. SLNs are removed and submitted to ultrastaging; 2. any suspicious LN is removed regardless of mapping; 3. if only unilateral mapping is noted, a contralateral side-specific pelvic LND is performed (including inter-iliac nodes); and 4. parametrectomy en bloc with primary tumor resection is done in all cases. We retrospectively applied the algorithm to determine how it would have performed. RESULTS: One hundred twenty-two patients were included. Median SLN count was 3 and median total LN count was 20. At least one SLN was identified in 93% of cases (114/122), while optimal (bilateral) mapping was achieved in 75% of cases (91/122). SLN correctly diagnosed 21 of 25 patients with nodal spread. When the algorithm was applied, all patients with LN metastasis were detected; with optimal mapping, bilateral pelvic LND could have been avoided in 75% of cases. CONCLUSIONS: In the surgical treatment of early cervical cancer, the algorithm we propose allows for comprehensive detection of all patients with nodal disease and spares complete LND in the majority of cases.

publication date

  • May 13, 2011

Research

keywords

  • Algorithms
  • Sentinel Lymph Node Biopsy
  • Uterine Cervical Neoplasms

Identity

PubMed Central ID

  • PMC4996075

Scopus Document Identifier

  • 79960449671

Digital Object Identifier (DOI)

  • 10.1016/j.ygyno.2011.04.023

PubMed ID

  • 21570713

Additional Document Info

volume

  • 122

issue

  • 2