Development of a clinical prediction model for diagnosing adenomyosis

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.

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.

NCT02201719.