Refining Mri Protocols for Endometriosis: A Comparative Study of Abbreviated and Full Mri Sequences Publisher Pubmed



Arian A¹ ; Karimi N¹ ; Ahmadinejad N¹ ; Azadnajafabad S² ; Delazar S¹ Show Affiliations
Source: British Journal of Radiology Published:2025

Abstract

Objectives: Endometriosis is a significant cause of chronic abdominal pain and infertility in females, often overlooked due to its resemblance to other abdominopelvic pathologies. This study aims to compare the diagnostic performance and agreement rate between an abbreviated MRI protocol (aMRI) and a full MRI protocol (fMRI) for detecting pelvic endometriosis. Methods: We retrospectively analysed 446 consecutive MRI exams, including both full (fMRI) and abbreviated (aMRI) protocols, performed for suspected pelvic endometriosis. An expert radiologist assessed the presence of endometriosis at 14 distinct anatomical sites. Each MRI protocol was interpreted in random order, with a minimum two-week interval between sessions to minimize recall bias. Agreement between the protocols was evaluated using kappa statistics. Results: The average age of the patients was 34.13 years. The highest incidences of endometriosis were found in the ovaries (88.8%) and the recto-uterine pouch (65%). The MRI protocols demonstrated perfect agreement (kappa coefficient = 1) for the ovaries, bladder, uterus, and caesarean section scar. High agreement was also observed in the rectum and uterine ligaments (kappa coefficients of 0.98 and 0.97). Detection of malignant transformation in existing ovarian endometriomas showed substantial concordance with a kappa coefficient of 0.66. Conclusions: An abbreviated non-contrast MRI protocol exhibits diagnostic accuracy comparable to that of a comprehensive protocol in detecting pelvic endometriosis, with similar confidence and reproducibility. Advances in knowledge: This study demonstrates that an abbreviated MRI protocol is as effective as a full protocol in diagnosing pelvic endometriosis, potentially allowing for quicker, cost-effective imaging without compromising diagnostic accuracy. © 2024 The Author(s).