4.4 Article

Sentinel lymph node mapping using indocyanine green in patients with uterine and cervical neoplasms: restrictions of the method

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ARCHIVES OF GYNECOLOGY AND OBSTETRICS
卷 299, 期 5, 页码 1373-1384

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SPRINGER HEIDELBERG
DOI: 10.1007/s00404-019-05063-6

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Endometrial cancer; Cervical cancer; Sentinel lymph node; Indocyanine green

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PurposeTo establish the surgical, demographic and histopathological factors associated with inaccurate sentinel lymph nodes (SLNs) identification using indocyanine green (ICG) and near-infrared (NIR) fluorescence imaging in uterine and cervical neoplasms during both open and laparoscopic surgery.MethodsWe reviewed patients with atypical endometrial hyperplasia (AEH), clinical stage I and II cervical cancer or uterine malignancies who underwent primary surgery with SLN mapping between September 2015 and January 2018. An analysis of patients' demographics, tumor factors and surgical approach was conducted. Bilateral and overall detection rates were calculated and univariate analysis was performed to estimate factors associated with failed SLN mapping.ResultsA total of 32 patients with uterine and cervical neoplasms were included in the study. The overall detection rate of the SLN was 84% and bilateral detection rate was 75%. There were no statistically relevant differences in overall and bilateral SLN detection rates by BMI, surgical approach or age. Regarding endometrial cancer, there were no differences in SLN detection rates when comparing tumor grade, histology nor myometrial invasion. For SLN detection failure, only the presence of metastatic lymph nodes and lack of surgical experience significantly increased the disability to detect SLNs (p=0.03, p=0.04, respectively).ConclusionsSLN mapping technique using NIR fluorescence imaging with ICG appears to be accurate method in most of the patients with cervical or endometrial carcinoma, regardless of demographic characteristics, tumor-related features and surgical approach. Surgeons' expertise in that field allows obtaining excellent detection rates.

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