Prediction of histopathological adenomyosis
Table 4 presents the results of both univariate and multivariate logistic regression analysis. Univariate logistic regression analysis showed p-values <.10 for: age at MRI, history of curettage, mean JZ thickness, JZ Max, JZ Diff, JZ/MYO, mean uterine volume, JZ Max ≥12 mm, JZ Diff ≥5 mm, and the presence of HSI foci. The potential predictors showed no two-way interaction; however, mean JZ thickness, JZ Max, and JZ Diff did show multicollinearity. These variables were not included in the multivariate regression model to avoid overoptimism. Nevertheless, high diagnostic performance was found for dysmenorrhoea and AUB (sensitivity/specificity >70%). Additionally, due to clinical relevance, BMI was manually forced into the multivariate model. The final model included age at MRI, BMI, history of curettage, dysmenorrhoea, AUB, mean JZ thickness, JZ Diff ≥5 mm, JZ/MYO >.40, and the presence of HSI foci. In this model, mean JZ thickness, JZ/MYO >.40 and the presence of HSI foci reached statistical significance. Preference was given to variables with the most statistical significance in univariate analysis, and the number of included variables in the model was kept to a minimum. To further correct the model for overfitting, a shrinkage factor of .747 was applied. Since LOESS already showed a good model fit for the continuous variables of interest, no modifications were necessary. The formula for the final prediction model therefore is as follows: