## 2 Materials and Methods
### Pipeline Overview  
The MVI prediction can be considered to be a classification problem in which the two classes are represented by MVI+ and MVI\(-\). Each radiomic feature was scaled according to the median and the interquartile range of its distribution of values. Ridge regression was applied by selecting the 15 most-relevant features according to the absolute values of their coefficient; the minority class (MVI+) was then oversampled using the synthetic minority oversampling technique (SMOTE) [32], with the aim of balancing the dataset classes. Finally, a classification was established using support vector classification (SVC) with a linear kernel on the reduced and balanced dataset. The choice of the SVC classifier model was determined by analyses carried out during the preliminary phase of this study. For the sake of brevity, a comparison with other classifier models was excluded from this study with the aim of focusing the attention on the comparison of the pipelines developed. Due to the limited number of samples, the prediction and evaluation were carried out using the leave-one-out (LOO) cross-validation technique, and the prediction results were used to evaluate the model performance. Three versions of this pipeline were introduced. In the first one (Pipeline 1), the pre-processing was applied outside the cross-validation, i.e., on the entire dataset. In the second one (Pipeline 2), the pre-processing step order was changed. The dataset balancing was applied to the entire dataset, namely outside the cross-validation, followed by the feature selection and classification applied after each splitting, i.e., only on the training data. In the third one (Pipeline 3), the pre-processing part was applied inside the cross-validation procedure, i.e., only on the training data. The schemes of the three proposed pipelines are reported in Figure 1.  
The three pipelines were tested on a synthetic dataset created using a toy model generator provided by the scikit-learn package [33]. This was carried out to verify that the authors' assumption regarding the effect of data cross-contamination in radiomic analyses also applied to more general contexts and was not an incidental effect induced by the limited number of samples in the present dataset. The synthetic data model generator creates normally distributed clusters of points on a hypercube of arbitrary dimension and assigns an equal number of clusters to each class (2 in the present case). It introduces interdependence between features and adds various types of noise to the data. The model allows setting the number of classes and informative, redundant, and repeated features. Two-thousand features for 100 samples were randomly generated using the synthetic data model generator. A number of informative features were used, realistically small as compared to the noise, i.e., 20 features out of 2000. Eighty samples out of one-hundred from Class 0 and twenty from Class 1 were generated, introducing an unbalancing between the classes. In this way, a dataset was created having characteristics similar to a radiomic dataset, i.e., low number of informative features, strongly correlated features, redundant features, and a limited number of samples. Figure 1: Scheme of the three pipelines proposed. Starting from the CECT, the manual VOI, and derived ZOT, the entire set of 4520 features was extracted. (**a**) Pipeline 1, feature selection and data oversampling were carried out on the entire radiomic dataset. It was then classified using the leave-one-out cross-validation, i.e., splitting the dataset into training and test sets, where the test set consisted of only one sample. (**b**) Pipeline 2, the second version of the pipeline in which oversampling was carried out on the entire dataset and feature selection and classification were carried out using leave-one-out cross-validation, i.e., feature selection classification was carried out only on the training dataset.