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Title: Accuracy of frozen-section diagnosis of ovarian mucinous tumors
Authors: Tip Pongsuvareeyakul
Surapan Khunamornpong
Jongkolnee Settakorn
Kornkanok Sukpan
Prapaporn Suprasert
Sumalee Siriaunkgul
Keywords: Medicine
Issue Date: 1-Mar-2012
Abstract: Objective: The objective of the study was to evaluate the diagnostic accuracy of intraoperative frozen sections of ovarian mucinous tumors and to identify the features associated with an inaccurate diagnosis. Methods: Cases of ovarian mucinous tumors (benign, low malignant potential [LMP] or borderline, primary malignant, and metastatic) diagnosed by frozen section or final histology were recruited. Frozen-section diagnoses were compared with the final histologic diagnoses. Possible variables associated with diagnostic discrepancy were analyzed. Results: A comparison of the diagnoses was done in 195 cases (102 benign, 61 LMP, 18 primary malignant, and 14 metastatic). Diagnostic agreement was observed in 164 cases (84.1%) and discrepancy in 31 cases (15.9%). The sensitivity of frozen-section diagnosis was low in LMP (67.2%) and malignant tumors (55.6%). The specificity was the lowest in the benign category (78.5%). The positive predictive values of all categories were less than 90% (range, 83.3%-5.7%). Diagnostic discrepancy was associated with tumor size of greater than 13 cm (P = 0.019) and the number of frozen sections of 4 or more (P = 0.035). However, in a multivariate analysis, there was no independent predictor of diagnostic discrepancy. The number of frozen sections 4 or more was strongly associated with tumor size of greater than 13 cm (P = 0.004). Conclusions: The sensitivity of frozen-section diagnosis of LMP and malignant mucinous tumors was low. The inaccuracy of a frozen-section diagnosis of ovarian mucinous tumors may be related to a tumor size of greater than 13 cm. Increasing the number of intraoperative samples over 3 sections per case may not effectively increase the accuracy of frozen-section diagnosis in mucinous tumors. Copyright © 2012 by IGCS and ESGO.
ISSN: 15251438
Appears in Collections:CMUL: Journal Articles

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