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Prediction of Adenomyosis Diagnosis based on MRI
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  • Connie Rees,
  • Marloes van de Wiel,
  • Joost Nederend,
  • Aleida Huppelschoten,
  • Massimo Mischi,
  • Huib van Vliet,
  • Benedictus Schoot
Connie Rees
Catharina Hospital
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Marloes van de Wiel
Catharina Hospital
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Joost Nederend
Catharina Hospital
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Aleida Huppelschoten
Catharina Hospital
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Massimo Mischi
Eindhoven University of Technology
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Huib van Vliet
Catharina Hospital
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Benedictus Schoot
Catharina Hospital
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Abstract

Study Objective: Development of a prediction tool for histopathological adenomyosis diagnosis after hysterectomy based on MRI and clinical parameters. Design: Single-centre retrospective cohort study Setting: Gynaecological department of a referral hospital from 2007-2022. Population: 296 women undergoing hysterectomy with preoperative pelvic MRI Methods: MRI’s were retrospectively assessed for adenomyosis markers (junctional zone (JZ) parameters, high signal intensity foci (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>0.10 and factors deemed clinically relevant. Results: 131/296 women (44.3%) had histopathological adenomyosis. Patients were of comparable age at hysterectomy, BMI and clinical symptoms, 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. 8.35mm, p <.001), maximal JZ thickness (16.00 vs. 13.40mm, p<.001), mean JZ/myometrium ratio (0.56 vs. 0.49, p=.040), and JZ differential (8.60 vs. 8.15mm, p=.003). Presence of HSI foci was a strong predictor for adenomyosis (39.7% vs. 8.9%, p<.001). Based on the parameters age and BMI, history of curettage, dysmenorrhoea, abnormal uterine bleeding (AUB), mean JZ, JZ Differential  5mm, JZ/myometrium ratio >.40, and presence of HSI Foci, a predictive model was created with a good Area Under the Curve (AUC) of .776. Conclusions: 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.