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MRI of pelvic endometriosis: evaluation of the mr#Enzian classification and the importance of adenomyosis subtypes

  • Antonia M. Pausch,
  • Vivien Filleböck,
  • Merve Benli,
  • Isabell Witzel,
  • Andreas M. Hötker

摘要

Purpose

This study aimed to investigate the utility of the #Enzian classification in magnetic resonance imaging (MRI) for endometriosis assessment, focusing on inter-reader agreement, diagnostic accuracy, and the correlation of adenomyosis with deep endometriosis (DE).

Methods

This IRB- approved retrospective single-center study included 412 women who underwent MRI evaluation for endometriosis between February 2017 and June 2022. Two experienced radiologists independently analyzed MRI images using the #Enzian classification and assessed the type of adenomyosis, if any. The surgical #Enzian classification served as the gold standard for evaluating preoperative MRI results of 45 patients. Statistical analysis was performed to assess inter-reader agreement and diagnostic accuracy.

Results

Inter-reader agreement was substantial to excellent (Cohen’s kappa 0.75–0.96) for most compartments except peritoneal involvement (0.39). The preoperative MRI showed mostly substantial to excellent accuracy (0.84–0.98), sensitivity (0.62–1.00), specificity (0.87–1.00), positive (0.58–1.00) and negative predictive values (0.86–1.00) for most compartments, except for peritoneal lesions (0.36, 0.17, 1.00, 1.00, 0.26 respectively). A trend with a higher prevalence of concordant DE in women with MR features of external adenomyosis compared to those with internal adenomyosis was visible (p = 0.067).

Conclusions

The mr#Enzian showed mostly high inter-reader agreement and good diagnostic accuracy for various endometriosis compartments. MRI’s role is particularly significant in the context of the current paradigm shift towards medical endometriosis treatment. The inclusion of information about the type of adenomyosis in the mr#Enzian classification could enhance diagnostic accuracy and inform treatment planning.

Graphical Abstract