Ultrasonography in the Diagnosis of Adnexal Lesions: The Role of Texture Analysis

Abstract

The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA’s capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features’ 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. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). 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 71.43–80% sensitivity and 87.5–89.77% specificity by the KNN classifier. 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.

Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach

“…The “Contrast” parameter (CH3D6Contrast) is a measure of intensity or gray-level variations between the reference pixel and its neighbor. In the visual perception of the real world, contrast is determined by the difference in the color and brightness of the object and other objects within the same field of view [ 46 , 47 ]. The contrast parameter increases its values when there is a large amount of variation within an ROI [ 45 ].…”

Comparison of Contrast‐Enhanced Spectral Mammography and Contrast‐Enhanced MRI in Screening Multifocal and Multicentric Lesions in Breast Cancer Patients

“…Ultrasonography-based texture analysis (USTA) offers a new perspective. It is unclear whether the parameters can reflect the histopathological characteristics of lesions [ 8 ]. Recently, preoperative contrast-enhanced ultrasonography only showed good predictive value for the sentinel lymph node detection in patients with breast cancer [ 8 ].…”

“…It is unclear whether the parameters can reflect the histopathological characteristics of lesions [ 8 ]. Recently, preoperative contrast-enhanced ultrasonography only showed good predictive value for the sentinel lymph node detection in patients with breast cancer [ 8 ]. CE-MRI is considered to be one of the most accurate imaging methods for the diagnosis of breast cancer [ 9 ].…”

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

“…On the other side, several efforts have been shown to improve the normalization procedures for image quality as a crucial pre-processing step to correct the imaging-related batch effects before extracting quantitative radiomic features [ 15 , 18 , 19 , 20 , 21 ]. The most common image pre-processing for the texture analysis approaches are the limitation of dynamics to µ ± 3σ (where µ is the mean gray-level value and σ is the standard deviation) and gray level compression based on the range between δ and 2δ (where δ is the number of bits per pixel), among others [ 22 , 23 ]. Although image pre-processing normalization is decisive to reduce the technical variability across images, additional feature normalization steps are still needed during post-processing to reduce the within-subject bias and between-subjects bias effects and, in the case of quantitative features coming from a multicenter study, to identify a batch-specific transformation to express all the data in a common space.…”