###### Abstract
In fact, despite its clinical relevance, the detection of MVI represents one of the most-difficult tasks to achieve in conventional imaging analysis since it can be obtained only indirectly, in relation to capsule disruption, irregular tumor margins, peritumoral enhancement, etc. [13]. A possible explanation for failing to evolve towards the pre-treatment radiological diagnosis of MVI is that it is merely based on direct observation by radiologists who rely on contrast differences between adjacent tissues; however, these fade in the peritumor, where the MVI is supposed to most frequently occur [17]. In addition, basic knowledge, diagnostic experience, and work status are other factors that might potentially further limit the accuracy of diagnosing MVI. However, radiological images contain more information than what is visible to the radiologist's eye. Texture and, more in general, radiomic analysis of contrast-enhanced CT (CECT) scans have already been applied by several authors in many medical image pipelines [18; 19; 20; 21; 22], proving their efficiency as a valuable tool in aiding the clinical diagnosis [6] and in predicting patient prognosis [5; 24]. Radiomics is a form of imaging analysis that uses a series of data mining and machine learning (ML) algorithms to extract high-throughput information for the quantification of the predictive and prognostic features associated with clinical outcomes [18; 25]. Therefore, the prediction of MVI on CT scans using radiomic analysis is considered to be an important research topic, potentially being able to overcome the current radiologically based imaging diagnosis in favor of the pre-treatment diagnosis of MVI in HCC. In particular, various papers [18; 26] have focused their attention on the peripheral area of HCC called the zone of transition (ZOT), in which the CECT attenuation is lower than the attenuation of the solid center of the tumor, but is still higher than the attenuation of the surrounding non-tumoral tissue. The use of the ZOT in HCC radiomic analysis is motivated by the potential presence of MVI in the penumbra zone [18; 26]. The present study proposed radiomic feature extraction and ML pipelines to predict MVI in HCC tumor volumes of interest (VOIs). A set of radiomic features was extracted from the tumor VOI and the ZOT; three different pipelines were used to investigate the problem of data contamination. In the first pipeline, data pre-processing was carried out, namely data balancing and feature selection before the training-test split, i.e., on the entire dataset at the beginning of the analysis. In the second pipeline, the feature selection step was moved inside the cross-validation procedure and the dataset balancing outside, i.e., performing part of the pre-processing on the entire dataset and the other part on the training set only. In the last pipeline, the pre-processing step was moved inside the cross-validation procedure, i.e., after the data splitting into the training and test sets [27]. The three pipelines were applied to the radiomic features extracted from the CECT data, monitoring the difference in model performance. The same pipelines were also applied to a synthetic radiomic dataset in order to additionally validate the authors' hypothesis. In this way, it was verified that the performance observed depended only on the design of the pipeline and not on specific dataset characteristics. In the radiomic literature, it is possible to find several examples in which the analysis has been carried in a way that introduces the cross-contamination between the training and test datasets. The authors felt that this was a critical point in radiomic research, which should be addressed in order to produce more reliable and reproducible results. For this reason, in the present analysis, the importance of considering the data cross-contamination was stressed.