Development of a clinical prediction model for diagnosing adenomyosis

We developed a clinical prediction model for diagnosing adenomyosis that shows a good test quality and a high specificity, but still requires validation.

Volume 110, Issue 5, Pages 957–964.e3


Tina Tellum, M.D., Staale Nygaard, Ph.D., Else K. Skovholt, M.D., Erik Qvigstad, M.D., Ph.D., Marit Lieng, M.D., Ph.D.



To develop a multivariate prediction model for diagnosing adenomyosis using predictors available through transvaginal ultrasonography and clinical examinations.


Prospective observational single-center study.


Teaching university hospital.


One hundred consecutively enrolled premenopausal women aged 30–50 years, undergoing hysterectomy due to a benign condition and not using hormonal treatment.


Preoperative 2-D and 3-D transvaginal ultrasonography investigations were performed, and the results were documented in a standardized form. Clinical information was collected using a questionnaire. Histopathology confirmed the outcome.

Main Outcome Measure(s)

Diagnostic performance (sensitivity, specificity, area under the curve (AUC)) of a multivariate prediction model for adenomyosis. Independent diagnostic performance of single predictors and their quantitative effect (β) in the final model.


The final model showed a good test quality (area under the curve [AUC] = 0.86, [95% confidence interval = 0.79–0.94], optimal cutoff 0.56, sensitivity of 85%, specificity 78%). The following nine predictors were included ([sensitivity, specificity, β] or [AUC, β]): presence of myometrial cysts (51%, 86%, β = 0.86), fan-shaped echo (36%, 92%, β = 0.54), hyperechoic islets (51%, 78%, β = 0.62), globular uterus (61%, 83%, β = 0.2), normal uterine shape (83%, 61%, β = –0.75), thickest/thinnest ratio for uterine wall (0.61, β = 0.26), maximum width of the junctional zone in sagittal plane (0.71, β = 0.1), regular appearance of junctional zone (31%, 92%, β = –1.0), and grade of dysmenorrhea measured on a verbal numerical rating scale (0.61, β = 0.08).


We have presented a multivariate model for diagnosing adenomyosis that weights predictors based on their diagnostic significance. The reported findings could aid clinicians who are interpreting the heterogeneous appearance of adenomyosis in ultrasonography.

Clinical trial registration number: NCT02201719.

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