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. 2020 Mar;121(4):599-604.
doi: 10.1002/jso.25853. Epub 2020 Jan 28.

Learning curve for sentinel lymph node mapping in gynecologic malignancies

Seongmin Kim  1   2 , Ki Jin Ryu  2 , Kyung Jin Min  2 , Sanghoon Lee  2 , Un Suk Jung  3 , Jin Hwa Hong  2 , Jae Yun Song  2 , Jae Kwan Lee  2 , Nak Woo Lee  2
Affiliations

Learning curve for sentinel lymph node mapping in gynecologic malignancies

Seongmin Kim et al. J Surg Oncol. 2020 Mar.

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.

Keywords: cervical cancer; endometrial cancer; learning curve; sentinel lymph node.

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References

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