Objective: To know the diagnostic accuracy performance of the International Ovarian Tumour Analysis (IOTA) Simple Rules and Assessment of Different NEoplasias in the AdneXa (IOTA-ADNEX) Model for identifying benign or malignant adnexal masses against histopathological diagnosis. Methods: This was a prospective single-center, cross-sectional diagnostic accuracy study including 53 women with an adnexal mass between May 2017 and March 2018. Pelvic ultrasound examination was done and serum levels of tumor marker CA 125 were obtained in all subjects prior to surgery. Adnexal masses were categorized according to the IOTA Simple rules and IOTA ADNEX model. The gold standard was histopathological diagnosis. The sensitivity, specificity, positive and negative predictive values of each scoring system utilized was determined and compared with the histopathologic result. Results: Using the IOTA Simple rules, 35 adnexal masses classified as benign are 94.28 % truly benign by histopathologic diagnosis and 5.72% came out to be malignant; All 12 malignant tumors were truly malignant; there were 6 inconclusive tumors and came out to be malignant. In this study, IOTA Simple rules obtained a sensitivity of 90%, specificity of 100%, positive predictive value of 100%, Negative predictive value of 94% and Accuracy of 96%. Using the baseline risks assessment proposed by IOTA ADNEX Model, overall computation are as follows: (1) Sensitivity of 96.88 %, (2) Specificity of 90.48, (3) Positive Predictive Value of 93.94%, and a (4) Negative Predicitve Value of 95% Conclusion The majority of adnexal masses in our study were classified correctly using the IOTA Simple rules and IOTA ADNEX model. Due to high statistical significant values obtained by IOTA Simple rules, its use is validated and is encouraged to be the standard of use in scoring adnexal masses. In this study, we are able to prove that by subjective expert opinion from an expert sonographer in reclassifying those that are unclassified by IOTA simple rules approximates 100% accuracy.
Ovarian Neoplasms