Prediction of adenomyosis diagnosis based on MRI - 09/06/23
, M. van de Wiel a, J. Nederend d, A. Huppelschoten a, M. Mischi b, H.A.A.M. van Vliet a, c, B.C. Schoot a, b, c
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Highlights |
• | We present the first clinical diagnostic tool combining both MRI and clinical parameters to predict histopathology adenomyosis diagnosis on an individual level with good accuracy. |
• | Our model would be especially useful in cases of mild or atypical adenomyosis where MRI is indicated. |
• | External validation is needed in other (prospective) settings, where clinicians can apply our results to aid in patient counselling. |
Abstract |
Objective |
Development of a multivariate prediction model based on MRI and clinical parameters for histological adenomyosis diagnosis.
Materials and methods |
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<0.1 and factors deemed clinically relevant.
Results |
131/296 women (44.3%) had histopathological adenomyosis. Patients had 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 the strongest 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.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Adenomyosis, MRI, Hysterectomy, Pathology, Diagnosis
Abbreviations : MRI, TVUS, JZ, BMI, OR, AUB
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Vol 2
Articolo 100028- Giugno 2023 Ritorno al numero
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