AIM: To assess the diagnostic accuracy of the ultrasonographic discrimination of benign adnexal masses. METHODS: This was a prospective study, evaluating 245 consecutive cases using real-time gray-scale ultrasonography in a tertiary education hospital. RESULTS: Diagnostic accuracy was high for simple cysts and solid masses as κ-values of 0.824 and 0.816 and accuracy was moderate for endometriomas, dermoid cysts and cystadenomas as κ-values of 0.758, 0.689 and 0.627, respectively, and low for hemorrhagic cysts as a κ-value of 0.587. A logistic regression model was developed using ultrasonographic characteristics of the adnexal masses. Irregularity at lining of the inner wall, presence of solid component, papillary projections and echogenicity were shown to have a strong impact on the diagnosis of benign masses. CONCLUSION: Although ultrasonography is a useful technique for the diagnosis of benign adnexal masses, it is limited in diagnostic accuracy. Pattern recognition is the most favored method for ultrasonographic diagnosis. Logistic regression analysis can contribute to diagnostic accuracy.
AIM: To assess the diagnostic accuracy of the ultrasonographic discrimination of benign adnexal masses. METHODS: This was a prospective study, evaluating 245 consecutive cases using real-time gray-scale ultrasonography in a tertiary education hospital. RESULTS: Diagnostic accuracy was high for simple cysts and solid masses as κ-values of 0.824 and 0.816 and accuracy was moderate for endometriomas, dermoid cysts and cystadenomas as κ-values of 0.758, 0.689 and 0.627, respectively, and low for hemorrhagic cysts as a κ-value of 0.587. A logistic regression model was developed using ultrasonographic characteristics of the adnexal masses. Irregularity at lining of the inner wall, presence of solid component, papillary projections and echogenicity were shown to have a strong impact on the diagnosis of benign masses. CONCLUSION: Although ultrasonography is a useful technique for the diagnosis of benign adnexal masses, it is limited in diagnostic accuracy. Pattern recognition is the most favored method for ultrasonographic diagnosis. Logistic regression analysis can contribute to diagnostic accuracy.