• Tumay Bekci, Ismet Mirac Cakir, and Serdar Aslan.
• Giresun University Faculty of Medicine, Department of Radiology - Giresun, Turkey.
• Rev Assoc Med Bras (1992). 2022 May 1; 68 (5): 641-646.

ObjectiveThis study aimed to evaluate the feasibility of texture analysis on T2-weighted axial images in differentiating affected and nonaffected ovaries in ovarian torsion.MethodsWe included 22 torsioned ovaries and 19 healthy ovaries. All patients were surgically proven ovarian torsion cases. On T2-weighted axial images, ovarian borders were delineated by the consensus of two radiologists for magnetic resonance imaging-based texture analysis. Statistical differences between texture features of affected and nonaffected ovaries were assessed.ResultsA total of 44 texture features were extracted from each ovary using LIFEx software. Of these, 17 features were significantly different between affected and nonaffected ovaries in ovarian torsion. NGLDM_Coarseness and NGLDM_Contrast, which are the neighborhood gray-level difference matrix parameters, had the largest area under the curve: 0.923. The best cutoff values for the NGLDM_Contrast and NGLDM_Coarseness were 0.45 and 0.01, respectively. With these cutoff levels, NGLDM_Contrast had the best accuracy (85.37%).ConclusionMagnetic resonance imaging-based texture analysis on axial T2-weighted images may help differentiate affected and nonaffected ovaries in ovarian torsion.

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