OBJECTIVE: To determine the correlation between the diagnosis of borderline ovarian tumors (BOTs) by frozen section and permanent histology analyses. METHODS: Three hundred fifty-four pathology reports with diagnoses of BOTs by frozen section or permanent histology analysis at a single institution between 1995 and 2010 were evaluated with a review of the literature. Frozen section and permanent histology analyses were compared. Multivariate regression analysis was used to assess the influence of clinicopathological parameters on the likelihood of underdiagnosis. RESULTS: The overall accuracy, i.e., agreement between frozen section and permanent histology diagnoses, was observed in 228 of 354 (64.4%) cases, yielding a sensitivity of 72.6%, a positive predictive value of 85.1%, underdiagnosis in 108 cases (30.5%), and overdiagnosis in 18 cases (5.1%). Based on multivariate analysis, mucinous histology (OR, 1.48; P=0.022) was the only significant predictor for underdiagnosis by frozen section. A comprehensive search of the literature identified 46 studies investigating the accuracy of frozen section analysis of BOTs. The data of 7 of 46 studies that met the criteria for inclusion and the data of the current study were pooled. The overall accuracy was 67.1% (741/1104), yielding a sensitivity of 82.1%, a positive predictive value of 78.7%, underdiagnosis in 222 cases (20.1%), and overdiagnosis in 141 cases (12.8%). CONCLUSIONS: Frozen section analysis of BOTs has low accuracy, sensitivity, and positive predictive value, and underdiagnosis and overdiagnosis are frequent. Therefore, surgical decision-making for BOTs based on frozen section diagnosis should be done carefully, especially in tumors with mucinous histology.
OBJECTIVE: To determine the correlation between the diagnosis of borderline ovarian tumors (BOTs) by frozen section and permanent histology analyses. METHODS: Three hundred fifty-four pathology reports with diagnoses of BOTs by frozen section or permanent histology analysis at a single institution between 1995 and 2010 were evaluated with a review of the literature. Frozen section and permanent histology analyses were compared. Multivariate regression analysis was used to assess the influence of clinicopathological parameters on the likelihood of underdiagnosis. RESULTS: The overall accuracy, i.e., agreement between frozen section and permanent histology diagnoses, was observed in 228 of 354 (64.4%) cases, yielding a sensitivity of 72.6%, a positive predictive value of 85.1%, underdiagnosis in 108 cases (30.5%), and overdiagnosis in 18 cases (5.1%). Based on multivariate analysis, mucinous histology (OR, 1.48; P=0.022) was the only significant predictor for underdiagnosis by frozen section. A comprehensive search of the literature identified 46 studies investigating the accuracy of frozen section analysis of BOTs. The data of 7 of 46 studies that met the criteria for inclusion and the data of the current study were pooled. The overall accuracy was 67.1% (741/1104), yielding a sensitivity of 82.1%, a positive predictive value of 78.7%, underdiagnosis in 222 cases (20.1%), and overdiagnosis in 141 cases (12.8%). CONCLUSIONS: Frozen section analysis of BOTs has low accuracy, sensitivity, and positive predictive value, and underdiagnosis and overdiagnosis are frequent. Therefore, surgical decision-making for BOTs based on frozen section diagnosis should be done carefully, especially in tumors with mucinous histology.
Authors: Koji Matsuo; Hiroko Machida; Tsuyoshi Takiuchi; Brendan H Grubbs; Lynda D Roman; Anil K Sood; David M Gershenson Journal: Gynecol Oncol Date: 2017-01-26 Impact factor: 5.482
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Authors: Salvatore Gizzo; Roberto Berretta; Stefania Di Gangi; Maria Guido; Giuliano Carlo Zanni; Ilaria Franceschetti; Michela Quaranta; Mario Plebani; Giovanni Battista Nardelli; Tito Silvio Patrelli Journal: Biomed Res Int Date: 2014-11-05 Impact factor: 3.411
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Authors: Nithya D G Ratnavelu; Andrew P Brown; Susan Mallett; Rob J P M Scholten; Amit Patel; Christina Founta; Khadra Galaal; Paul Cross; Raj Naik Journal: Cochrane Database Syst Rev Date: 2016-03-01