Eitan R, Sabah G, Krissi H, Raban O, Ben-Haroush A, Goldschmit C, Levavi H, Peled Y
Gynecologic Oncology Division, Rabin Medical Center, Beilinson Campus, Petach Tikva 49100, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Eur J Surg Oncol. 2015 Dec;41(12):1659-63. doi: 10.1016/j.ejso.2015.09.006. Epub 2015 Sep 25.
Sentinel lymph node (SLN) mapping has emerged as a viable option for the treatment of patients with endometrial cancer. We report our initial experience with SLN mapping algorithm, and examine the factors predicting successful SLN mapping.
We analyzed all data recorded in our institute on robotic blue-dye SLN detection mapping from the time it was first introduced to our department in January 2012-December 2014. Data included patient demographics, SLN allocation, operating room times, and pathology results.
During the study period, 74 patients had robotic assisted surgery for endometrial cancer with attempted SLN mapping. SLN was found overall in 46 patients (62.1%). At first, SLN was detected in only 50% of cases, but after performing 30 cases, detection rates rose to 84.6% (OR = 3.34, CI 1.28-8.71; p = 0.003). Univariate analysis showed a higher detection rate with methylene blue than patent blue dye, 74.3% vs. 52.3% (OR = 2.744, 95% CI 1.026-7.344; p = 0.042). In multivariate analysis, high body mass index (BMI) was associated with failed mapping (OR = 0.899; 95% CI 0.808-1.00), as was the presence of lymph-vascular space invasion (LVSI) (OR = 0.126; 95% CI 0.24-0.658) and few cases per surgeon (OR = 1.083, 95% CI 1.032-1.118). Factors related to uterine pathology itself, including tumor histology, grade, method of diagnosis, the presence of an endometrial polyp, and lower uterine segment involvement were not found to be associated with successful mapping.
Surgeon experience, BMI and LVSI may affect the success rate of SLN mapping for endometrial cancer. These factors should be investigated further in future studies.
前哨淋巴结（SLN）定位已成为子宫内膜癌患者治疗的一种可行选择。我们报告了我们在前哨淋巴结定位算法方面的初步经验，并研究了预测前哨淋巴结定位成功的因素。
我们分析了自2012年1月首次引入我科室至2014年12月期间，我所记录的关于机器人辅助蓝色染料前哨淋巴结检测定位的所有数据。数据包括患者人口统计学信息、前哨淋巴结定位情况、手术时间以及病理结果。
在研究期间，74例患者接受了机器人辅助子宫内膜癌手术并尝试进行前哨淋巴结定位。总体上在46例患者（62.1%）中发现了前哨淋巴结。起初，仅在50%的病例中检测到前哨淋巴结，但在完成30例病例后，检测率升至84.6%（比值比 = 3.34，可信区间1.28 - 8.71；p = 0.003）。单因素分析显示亚甲蓝的检测率高于专利蓝染料，分别为74.3%和52.3%（比值比 = 2.744，95%可信区间1.026 - 7.344；p = 0.042）。多因素分析中，高体重指数（BMI）与定位失败相关（比值比 = 0.899；95%可信区间0.808 - 1.00），淋巴管间隙浸润（LVSI）的存在（比值比 = 0.126；95%可信区间0.24 - 0.658）以及每位外科医生的病例数少（比值比 = 1.083，95%可信区间1.032 - 1.118）也与定位失败相关。未发现与子宫病理本身相关的因素，包括肿瘤组织学类型、分级、诊断方法、子宫内膜息肉的存在以及子宫下段受累情况与定位成功相关。
外科医生经验、BMI和LVSI可能影响子宫内膜癌前哨淋巴结定位的成功率。这些因素应在未来研究中进一步探讨。
