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A predictive model for lymph node metastasis using tumor location in presumed early-stage endometrioid endometrial cancer patients.
- Source :
-
Journal of Gynecologic Oncology . Jul2024, Vol. 35 Issue 4, p1-9. 9p. - Publication Year :
- 2024
-
Abstract
- Objective: The aim of this study was to identify high- and low-risk subgroups of patients with lymph node (LN) metastasis in presumed early-stage endometrioid endometrial cancer (EC) patients. Methods: Clinicopathologic data of presumed early-stage endometrioid EC patients (n=361) treated with lymphadenectomy between March 2000 and July 2022 were analyzed. None of the patient had definite evidence of LN metastasis in a preoperative magnetic resonance imaging (MRI). A received operating characteristic curve analysis was conducted to define the sensitivity and specificity for the combined preoperative risk factors for LN metastasis, which was determined by multivariate analysis. Results: Nineteen patients (5.3%) had LN metastasis. Multivariate analysis identified cervical stromal invasion on MRI (odds ratio [OR]=4.386; 95% confidence interval [CI]=1.020-18.852; p=0.047), cornual location of tumor on MRI (OR=36.208; 95% CI=7.902-165.913; p<0.001), and lower uterine segment/isthmic location of tumor on MRI (OR=8.454; 95% CI=1.567-45.610; p=0.013) as independent prognostic factors associated with LN metastasis. Patients were categorized into low- and high-risk groups according to risk criteria. Significant differences in the rates of LN metastasis were observed between the two groups (0.4% vs. 22.2%, p<0.001). Conclusion: Approximately 95% of presumed early-stage endometrioid EC patients did not have LN metastasis. A model using tumor location was significantly correlated with the risk of LN metastasis. Even in presumed early-stage endometrioid EC patients, therefore, tumor location should be investigated to determine whether to perform LN assessment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20050380
- Volume :
- 35
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Gynecologic Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 178539856
- Full Text :
- https://doi.org/10.3802/jgo.2024.35.e53