Batista TP, Cavalcanti CLC, Tejo AAG, Bezerra ALR. Accuracy of preoperative endometrial sampling diagnosis for predicting the final pathology grading in uterine endometrioid carcinoma.
Eur J Surg Oncol 2016;
42:1367-71. [PMID:
27052799 DOI:
10.1016/j.ejso.2016.03.009]
[Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 02/07/2016] [Accepted: 03/04/2016] [Indexed: 12/20/2022] Open
Abstract
PURPOSE
To explore the accuracy of preoperative endometrial sampling diagnosis for predicting the final pathology grading in endometrial cancer.
METHODS
A cross-sectional study was carried out on patients who underwent surgical treatment for clinically early-stage endometrioid carcinoma of uterus at our Centers from March, 1991 to June, 2012. The agreement levels for the histological grading between the preoperative endometrial sampling diagnosis and the final surgical pathology were analyzed by the Kappa (κ) statistics with 95% confidence intervals (CI). The statistical analyses were also based on frequency data and diagnostic agreement of the procedures.
RESULTS
We retrospectively selected 79 patients that fit the criteria of this analysis. The overall level of agreement between preoperative and postoperative grading was "fair" according to Kappa (κ) statistics (κ = 0.221; 95%CI = 0.389-0.053; p = 0.01). Accordingly, the overall concordance was 48/79 (60.75%)-39/58 (67.24%) for G1, 7/16 (43.75%) for G2, and 2/5 (40%) for G3 tumors. The preoperative grade 1 diagnosis was upgraded to grade 2 (n = 6) or 3 (n = 1) in 15.2% of patients after hysterectomy. Sensitivity, specificity, NPV, PPV, and accuracy of preoperative endometrial sampling diagnosis to predict grade 1 at the final surgical pathology was 67.2%, 66.7%, 42.4%, 84.8% and 67.1%, respectively.
CONCLUSIONS
Preoperative endometrial sampling was found to be only a modest overall predictor of postoperative histological grading. A selective staging policy based on predictive models to avoid lymph node dissections in endometrial cancer should take into account additional parameters.
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