Abstract
BACKGROUND: Indocyanine green (ICG) fluorescence with high-definition, three-dimensional imaging systems is emerging as the latest strategy to reduce trauma and improve surgical outcomes during oncosurgery. MATERIALS AND METHODS: This is a prospective study involving 100 patients with carcinoma endometrium who underwent robotic-assisted Type 1 pan-hysterectomy, with ICG-directed sentinel lymph node (SLN) biopsy from November 2017 to December 2019. The aim was to assess the feasibility and diagnostic accuracy of SLN algorithm and to evaluate the location and distribution of SLN in pelvic, para-aortic and unusual areas and the role of frozen section. RESULTS: The overall SLN detection rate was 98%. Bilateral detection was possible in 92% of the cases. Right side was detected in 98% of the cases and left side was visualised in 92% of the cases. Complete node dissection was done where SLN mapping failed. The most common location for SLN in our series was obturator on the right hemipelvis and internal iliac on the left hemipelvis. SLN in the para-aortic area was detected in 14% of cases. In six cases, SLN was found in atypical locations, that is pre-sacral area. Eight patients had SLN positivity for metastasis and underwent complete retroperitoneal lymphadenectomy. Comparison of final histopathological report with frozen section reports showed no false negatives. CONCLUSIONS: SLN mapping holds a great promise as a modern staging strategy for endometrial cancer. In our experience, cervical injection was an optimal method of mapping the pelvis. ICG showed a high overall detection rate, and bilateral mapping appears to be a feasible alternative to the more traditional methods of SLN mapping in patients with endometrial cancer. The ICG fluorescence imaging system is simple and safe and may become a standard in oncosurgery in view of its staging and anatomical imaging capabilities. This approach can reduce the morbidity, operative times and costs associated with complete lymphadenectomy while maintaining prognostic and predictive information.