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How low is low enough? Evaluation of various risk-assessment models for lymph node metastasis in endometrial cancer: a Korean multicenter study

Authors
Kang, SokbomLee, Jong-MinLee, Jae-KwanKim, Jae WeonCho, Chi-HeumKim, Seok-MoPark, Sang-YoonPark, Chan-YongKim, Ki-Tae
Issue Date
10월-2012
Publisher
KOREAN SOC GYNECOLOGY ONCOLOGY & COLPOSCOPY
Keywords
Endometrial cancer; Low-risk group; Lymph node dissection; Lymphadenectomy; Prediction; Sensitivity and specificity
Citation
JOURNAL OF GYNECOLOGIC ONCOLOGY, v.23, no.4, pp.251 - 256
Indexed
SCIE
SCOPUS
KCI
Journal Title
JOURNAL OF GYNECOLOGIC ONCOLOGY
Volume
23
Number
4
Start Page
251
End Page
256
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/107240
DOI
10.3802/jgo.2012.23.4.251
ISSN
2005-0380
Abstract
Objective: The aim of this study was to identify a standard for the evaluation of future models for prediction of lymph node metastasis in endometrial cancer through estimation of performance of well-known surgicopathological models. Methods: Using the medical records of 947 patients with endometrial cancer who underwent surgical management with lymphadenectomy, we retrospectively assessed the predictive performances of nodal metastasis of currently available models. Results: We evaluated three models included: 1) a model modified from the Gynecologic Oncology Group (GOG) pilot study; 2) one from the GOG-33 data; and 3) one from Mayo Clinic data. The three models showed similar negative predictive values ranging from 97.1% to 97.4%. Using Bayes' theorem, this can be translated into 2% of negative post-test probability when 10% of prevalence of lymph node metastasis was assumed. In addition, although the negative predictive value was similar among these models, the proportion that was classified as low-risk was significantly different between the studies (56.4%, 44.8%, and 30.5%, respectively; p<0.001). Conclusion: The current study suggests that a false negativity of 2% or less should be a goal for determining clinical usefulness of preoperative or intraoperative prediction models for low-risk of nodal metastasis.
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