JOURNAL ARTICLE

Differentiation of affected and nonaffected ovaries in ovarian torsion with magnetic resonance imaging texture analysis

Tumay Bekci, Ismet Mirac Cakir, Serdar Aslan
Revista da Associação Médica Brasileira 2022, 68 (5): 641-646
35584489

OBJECTIVE: This study aimed to evaluate the feasibility of texture analysis on T2-weighted axial images in differentiating affected and nonaffected ovaries in ovarian torsion.

METHODS: We 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.

RESULTS: A 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%).

CONCLUSION: Magnetic resonance imaging-based texture analysis on axial T2-weighted images may help differentiate affected and nonaffected ovaries in ovarian torsion.

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