It is unknown if sentinel lymph node biopsy (SLNB) can substitute lymphadenectomy for surgical staging in individuals with advanced endometrial cancer (EC). The purpose of this study was to look at the diagnostic accuracy, performance parameters, and morbidity related to SLNB utilizing indocyanine green in patients with intermediate- and high-grade EC.
From July 1, 2015, to June 30, 2019, accrual occurred in this prospective, multicenter cohort research (Sentinel Lymph Node Biopsy versus Lymphadenectomy for Intermediate- and High-Grade Endometrial Cancer Staging [SENTOR] study), with early termination according to prespecified accuracy requirements. Patients with clinical stage I grade 2 endometrioid or high-grade EC scheduled for laparoscopic or robotic hysterectomy with the purpose to complete staging at three recognized cancer facilities in Toronto, Ontario, Canada was included in the research. The main result was the sensitivity of the SLNB algorithm. Additional metrics of diagnostic accuracy, sentinel lymph node detection rates, and adverse events were secondary outcomes.
The trial included 156 patients (median age, 65.5 years; range, 40-86 years; median BMI [weight in kilograms divided by height in meters squared], 27.5; range, 17.6-49.3), including 126 with high-grade EC. All patients received SLNB and PLND, and 101 patients (80%) with high-grade EC additionally received PALND. Sentinel lymph node detection rates were 97.4% per patient (95% CI, 93.6% -99.3%), 87.5% per hemipelvis (95% CI, 83.3% -91.0%), and 77.6% bilaterally (95 % CI, 83.3% -91.0%) (95% CI, 70.2% -83.8% ). The SLNB algorithm successfully detected 26 of 27 (17%) patients with nodal metastases, producing a sensitivity of 96% (95% CI, 81% -100%), a false-negative rate of 4% (95% CI, 0% -19%), and a negative predictive value of 99% (95% CI, 96% -100% ). The SLNB algorithm misclassified only one patient (0.6%). Seven (26%) of the 27 patients with node-positive malignancy were detected outside of typical PLND limits or required immunohistochemistry for identification.