75 Data Management: To store patient data, protected software, Research Manager (Research Manager, Deventer, the Netherlands), was used. Data pertaining to patients were given a pseudonymised study ID and could therefore not be traced back to the individual patient. Data Analysis and Model Development The study was conducted conform both the STROBE (189) and the TRIPOD statements (190) (see appendices 10C and 10D for the appropriate checklists). All statistical analyses were conducted with IBM SPSS Statistics, version 28.0 (IBM Corp., Armonk, NY, USA). Flowcharts were created using Miro (Miro, Amsterdam, the Netherlands). Except for univariate logistic regression analysis, a p-value of <.05 was considered statistically significant for all variables. Between-group differences were compared between patients with and without a histopathological adenomyosis diagnosis after hysterectomy. For clinical characteristics and primary MRI parameters, counts and frequencies were reported. For normally distributed continuous variables, means and standard deviations were calculated. For continuous variables that were not normally distributed, medians and inter-quartile ranges were given. To assess betweengroup differences for continuous variables, Student’s t-Test and Mann-Whitney U test were used. For categorical variables, the Chi Squared test was used. For all possible predictive factors, sensitivity, specificity, PPV, NPV, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and accuracy were calculated. Potential threshold values of continuous variables were investigated using Receiver Operator Characteristics (ROC) curves and Area Under the Curve (AUC) to identify appropriate cut-off values, and to test the prognostic diagnostic potential for histopathological adenomyosis diagnosis. For the development of the prediction model, the methodology as described by Grant et al. (191) and the TRIPOD guidelines were followed (190). For all individual potential predictors for a histopathological adenomyosis diagnosis, a univariate logistic regression analysis was first performed. The odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were reported. Missing values were dealt with by multiple imputation. Furthermore, interaction terms were used to test possible interaction between individual predictive
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