Fig. 6.

Principal component analysis (PCA) from superior placental region of interest using all radiomic features from image filters. Panel a shows how the feature space used by each model from whole dataset. The plots show each model is using information for making predictions from different areas of the feature space. Panel b shows PCA for each model. Panel c shows the radiomic features within each PCA cluster that were important for each prediction model. This suggests some radiomic features could be used either interchangeably or in combination for placenta accreta spectrum prediction. **kNN yielded variable importance and PCA outputs identical to those obtained from support vector machine (SVM) as seen in panel b (due to the discretisation method used in evaluating variable importance for these two models) and therefore only the output for SVM are shown in c