Differentiation of Endometriomas from Ovarian Hemorrhagic Cysts at Magnetic Resonance: The Role of Texture Analysis

Abstract

Background and Objectives: To assess ovarian cysts with texture analysis (TA) in magnetic resonance (MRI) images for establishing a differentiation criterion for endometriomas and functional hemorrhagic cysts (HCs) that could potentially outperform their classic MRI diagnostic features. Materials and Methods: Forty-three patients with known ovarian cysts who underwent MRI were retrospectively included (endometriomas, n = 29; HCs, n = 14). TA was performed using dedicated software based on T2-weighted images, by incorporating the whole lesions in a three-dimensional region of interest. The most discriminative texture features were highlighted by three selection methods (Fisher, probability of classification error and average correlation coefficients, and mutual information). The absolute values of these parameters were compared through univariate, multivariate, and receiver operating characteristic analyses. The ability of the two classic diagnostic signs ("T2 shading" and "T2 dark spots") to diagnose endometriomas was assessed by quantifying their sensitivity (Se) and specificity (Sp), following their conventional assessment on T1-and T2-weighted images by two radiologists. Results: The diagnostic power of the one texture parameter that was an independent predictor of endometriomas (entropy, 75% Se and 100% Sp) and of the predictive model composed of all parameters that showed statistically significant results at the univariate analysis (100% Se, 100% Sp) outperformed the ones shown by the classic MRI endometrioma features ("T2 shading", 75.86% Se and 35.71% Sp; "T2 dark spots", 55.17% Se and 64.29% Sp). Conclusion: Whole-lesion MRI TA has the potential to offer a superior discrimination criterion between endometriomas and HCs compared to the classic evaluation of the two lesions' MRI signal behaviors.

Keywords: endometrioma; endometriosis; magnetic resonance imaging (MRI); ovarian cysts; texture analysis.

Figure 1

(A) Oblique axial T2-weighted image of a 32-year-old patient with pathologically confirmed endometrioma (green arrow) and (B) the region of interest (red area) used for texture analysis.

Figure 2

Receiver operating characteristic curve of wavelet energy variations (A), entropy variations computed for the 135° direction (B), entropy variations computed for vertical directions (C), angular second moment variations (D), sum entropy and variance (E), and (F) the entropy parameter that represents an independent predictor of endometriomas (CV2S6Entropy) and the prediction model itself. WavEn, wavelet energy; Entropy, entropy; AngScMom, angular second moment; SumEntrp, sum entropy; Variance, histogram’s variance.