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

• Main Author: Bekci,Tumay
• Publication Date: 2022
• Other Authors: Cakir,Ismet Mirac, Aslan,Serdar
• Format: Article
• Language: eng
• Source: Revista da Associação Médica Brasileira (Online)
• Download full: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-42302022000500641
• topic: Artificial intelligence; Diagnostic techniques; Obstetrical and gynecological

SUMMARY 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.