EP24.06: Diagnostic accuracy of sonographic evaluation of deep endometriosis and adhesions using #Enzian classification

To evaluate the accuracy of preoperative transvaginal sonography (TVS) supplemented by transabdominal sonography (TAS) in deep endometriosis and adhesions using surgical findings as the reference standard. The test and reference standard both use the #Enzian classification as the metric. We performed a retrospective analysis of data from 203 women undergoing TVS/TAS and surgery for deep endometriosis between January 2022 - December 2023 at a tertiary referral centre for endometriosis. The #Enzian classification system was used to describe the localization and severity of endometriotic lesions/adhesions at preoperative ultrasound and during the surgery. The concordance between these findings were analysed. The sensitivity, specificity, positive and negative predictive values and accuracy in the different #Enzian compartments were calculated. Preoperative TVS/TAS and surgical findings showed a concordance rate ranging between 76% and 100% for the presence or absence of endometriotic lesions/adhesions. The concordance rate between TVS and surgery ranged between 65% and 88% for different severity grades, in #Enzian compartments O, T, A, B and C. The sensitivity of TVS/TAS for the detection of endometriotic lesions ranged from 25% (#Enzian compartments FI, FO) to 100% (#Enzian compartment FB), specificity from 90% (#Enzian compartment T left) to 100% (#Enzian compartments FB, FI, FU), positive predictive value from 67% (#Enzian compartments B right, FO) to 100% (#Enzian compartments FB, FI, FU), negative predictive value from 71% (#Enzian compartment B left) to 100% (#Enzian compartment FB) and accuracy from 76% (#Enzian compartment B right) to 100% (#Enzian compartment FB). The localization and extent of endometriosis lesions/adhesions can be accurately diagnosed non-invasively using TVS/TAS based on #Enzian classification. This coding system allows consistent description of the location and extent of endometriosis. This can support both the prediction of the surgical complexity or in monitoring of the disease.