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Cited 10 time in webofscience Cited 11 time in scopus
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Learning curve for sentinel lymph node mapping in gynecologic malignancies

Authors
Kim, SeongminRyu, Ki JinMin, Kyung JinLee, Sang hoonJung, Un SukHong, Jin HwaSong, Jae YunLee, Jae KwanLee, Nak Woo
Issue Date
Mar-2020
Publisher
WILEY
Keywords
cervical cancer; endometrial cancer; learning curve; sentinel lymph node
Citation
Journal of Surgical Oncology, v.121, no.4, pp.599 - 604
Indexed
SCIE
SCOPUS
Journal Title
Journal of Surgical Oncology
Volume
121
Number
4
Start Page
599
End Page
604
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/1003
DOI
10.1002/jso.25853
ISSN
0022-4790
Abstract
Background and Objectives Only a few studies have reported the learning curve for sentinel lymph node (SLN) detection in gynecologic malignancies. We investigated the learning curve for SLN detection during robot-assisted laparoscopic surgery for endometrial and cervical carcinomas. Methods This retrospective analysis included patients with stage IA to IIA1 cervical cancer or stage I to III endometrial cancer who underwent SLN mapping using indocyanine green during robot-assisted laparoscopic surgery performed by a single surgeon. Learning curves were analyzed in consecutive cases using SLN detection rates and the cumulative sum (CUSUM) method. Results SLN mapping was achieved in 81.25% (65/80), 77.50% (62/80), and 66.25% (53/80) of the cases involving the right, left, and simultaneous bilateral pelvic areas, respectively. Learning curve analysis based on the cumulative detection rate showed initial fluctuations followed by stabilization; the time required for proficiency was discordant among the LN regions. However, the CUSUM method showed proficient mapping of the right, left, and bilateral SLNs after 27 to 28 cases. Conclusion At least 27 cases were required for SLN mapping proficiency in gynecologic cancer; the learning period could influence the surgical quality. Further studies are warranted to confirm the impact of this learning curve on disease outcomes.
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Song, Jae Yun
Anam Hospital (Department of Obstetrics and Gynecology, Anam Hospital)
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