*2.4. Statistical Analysis*

The statistical analysis was performed with RStudio software [47].

To assess variability among radiomic feature values, the intra-class correlation coefficient (ICC) was calculated. A non-parametric Wilcoxon–Mann–Whitney test and receiver operating characteristic (ROC) analysis were performed and the Youden index was calculated to obtain the optimal cut off value for each feature; then, in order to assess analysis results, the area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were computed.

Linear classifier (linear discriminant analysis—LDA), decision tree (TREE), k-nearest neighbors (KNN), artificial neural network (NNET) and support vector machine (SVM) using all extracted metrics of textural parameters were used [14]. Configuration settings for each classifier are provided in our previous study [41,43]. The 10-fold cross validation (10-fold CV) and the leave-one-out cross validation (LOOCV) approaches and median values of AUC, accuracy, sensitivity, specificity, PPV and NPV were obtained.

Feature selection with the least absolute shrinkage and selection operator (LASSO) method [48] was performed considering both the λ value with the minimum mean squared error (minMSE) and the largest λ value within one standard error of it (1SE) [49].

In addition, the self-adaptive synthetic over-sampling (SASYNO) approach and the adaptive synthetic sampling (ADASYN) approach, to help balance the classes (malignant and benign), were used [50–55].

The best model was chosen considering the highest area under the ROC curve and highest accuracy.

A *p*-value < 0.05 was considered as significant.
