Prediction of adenomyosis diagnosis based on MRI

C.O. Rees, Mark A. van de Wiel, Joost Nederend, A. Huppelschoten, Massimo Mischi, H.A.A.M. van Vliet, B.C. Schoot
In: Journal of Endometriosis and Uterine Disorders · 2023 · vol. 2 , pp. 100028 · doi:10.1016/j.jeud.2023.100028 · W4379055904
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This study developed a multivariate prediction model using MRI findings and clinical parameters, achieving an AUC of 0.776 for adenomyosis diagnosis.

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Abstract

Development of a multivariate prediction model based on MRI and clinical parameters for histological adenomyosis diagnosis. This single centre retrospective cohort study took place in the gynaecological department of a referral hospital. In all, 296 women undergoing hysterectomy with preoperative pelvic MRI between 2007–2022 were included. MRI scans were retrospectively assessed for adenomyosis markers (junctional zone [JZ] parameters, high signal intensity [HSI] foci in a blinded fashion. A multivariate regression model for histopathological adenomyosis diagnosis was developed based on MRI and clinical variables from univariate analysis with p 0.05. Adenomyosis patients more often had: undergone a curettage (22.1% vs. 8.9%, p = 0.002), a higher mean JZ thickness (9.40 vs. 8.35 mm, p < .001), maximal JZ thickness (16.00 vs. 13.40 mm, p < .001), mean JZ/myometrium ratio (0.56 vs. 0.49, p = .040), and JZ differential (8.60 vs. 8.15 mm, p = .003). Presence of HSI foci was the strongest predictor for adenomyosis (39.7% vs. 8.9%, p 40, and presence of HSI foci, a predictive model was created with a good area under the curve (AUC) of .776. This is the first study to create a diagnostic tool based on MRI and clinical parameters for adenomyosis diagnosis. After sufficient external validation, this model could function as a useful clinical decision-making tool in women with suspected adenomyosis.

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adenomyosisdysmenorrhea

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