Objectives: The aim of this study was to externally validate the diagnostic performance of the International Ovarian Tumor Analysis logistic regression models (LR1 and LR2, 2005) and other popular prognostic models including the Timmerman logistic regression model (1999), the Alcazar model (2003), the risk of malignancy index (RMI, 1990), and the risk of malignancy algorithm (ROMA, 2009). We compared these models to subjective ultrasonographic assessment performed by an experienced ultrasonography specialist, and with our previously developed scales: the sonomorphologic index and the vascularization index. Furthermore, we evaluated diagnostic tests with regard to the menopausal status of patients. Materials and methods: This study included 268 patients with adnexal masses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the nalyzed models. Materials and methods: This study included 268 patients with adnexal asses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the analyzed models. Results: The subjective ultrasonographic sessment and all of the studied predictive models achieved similar diagnostic performance in the whole study population. However, significant differences were observed when preand postmenopausal patients were analyzed separately. In the subgroup of premenopausal atients, the highest area under the curve (AUC) was achieved by subjective ultrasonographic assessment (0.931), the Alcazar model (0.912), and LR1 (0.909). Alternatively, in the group of postmenopausal patients, the highest AUC was noted for the Timmerman model (0.973), ROMA (0.951), and RMI (0.938). Conclusions: Menopausal status is a key factor that affects the utility of prognostic models for differential diagnosis of ovarian tumors. Diagnostic models of ovarian tumors are reasonable tools for predicting tumor malignancy.