Introduction/Background Endometrial carcinoma (EC) is the most common gynaecologic malignancy in developed countries. Approximately eighty percent of cases are endometrioid type which usually presents in early stage and suitable for surgical staging. However, pelvic lymph node (PLN) evaluation mostly depends on surgeon preference. The objective of this study is to explore the associated factors and create the predictive model for PLN in endometrioid EC patients.
Methodology Medical record of 293 patients with endometrioid EC, who received surgical staging in single University Hospital during 2009–2016, were reviewed. The relationship between PLN metastasis and these data: demographic factors, biochemical markers, preoperative and intraoperative tumor characteristics were analysed by using a logistic regression model. Consequently, a score was created in order to predict the probability of pelvic lymph node metastasis. The internal validation by bootstrapping 400 times was performed.
Results From multivariate analysis, associated factors were grade of tumor, platelet count, deep myometrial invasion (deep MI: more than half of myometrial invasion) and size of tumor. Platelet count and size of tumor were re-calculated. Thrombocytosis (platelet count more than or equal to 380,000) and large tumor (tumor size more than or equal to 6 centimetres) were statistically significant for cut-off point. The generated score was: (2 × grade 2) + (4 × grade3) + (4 × deep MI) + (2 × large tumor) + (3 × thrombocytosis). The area under curve (AUC) for this score was 0.824. There were 88.9% sensitivity, 57.5% specificity, 40.5% positive predictive value and 94.1% negative predictive value, using score 4 as a cut-off point. The internal validation by bootstrapping 400 times revealed AuROC from 0.820 to 0.807 (optimism 0.013).
Conclusion Grade of tumor, platelet count, deep MI and size of tumor, were associated with PLN metastasis in endometrioid EC. The generated score yield a promising result to predict PLN metastasis.
Disclosure Nothing to disclose.
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.