The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions. Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43вЂ“80% sensitivity and 87.5вЂ“89.77% specificity by the KNN classifier.

Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). The featuresвЂ™ ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The USTA was performed on dedicated software. A total of 123 adnexal lesions (benign, 88 malignant, 35) were retrospectively included. This study aimed to further investigate USTAвЂ™s capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations.

Topaz denoise ai 2.4.1