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: