## 1 Introduction  
With the advancement of imaging technologies and computing powers within recent decades, an opportunity for mining medical images for additional information using quantitative image analysis has arisen. Radiomics--\"the high throughput extraction of quantitative features from medical images that are further correlated with biologic information\"--was introduced as a result [1]. To date, research into the potential applications of quantitative medical image analysis using radiomic features (RFs). The basis in radiomics analyses is that using quantitative imaging features or RFs could result in extracting additional information to that perceived by human experts. The extracted RFs can then be used for statistical analyses and machine learning modeling [2; 3; 4]. A significant number of studies reported on the predictive ability of RFs to decrypt information on computed tomography (CT) [5; 6; 7], magnetic resonance (MR) [8; 9; 10; 11], and positron emission tomography (PET) scans [12; 13; 14]. Clinical implementation of radiomics tools has the benefits of being a minimally invasive, rapid, and highly reproducible tool that can characterize the entirety of the lesion in comparison to the current clinical standard of care, such as tissue biopsy. However, radiomics analyses must be methodologically sound to ensure the generalizability of the developed radiomic signatures. Radiomics analyses generally aim to devise the RFs as clinical biomarkers, which requires that the RFs are both predictive and reproducible [15]. With the advancement of the field, the limitations of RFs have been increasingly researched. Several studies have studied the effects of variations in some imaging parameters on phantom and human data [16; 17; 18; 19; 20; 21; 22; 23]. These studies reported that significant percentages of RFs are sensitive to the variations in imaging hardware and parameters used for scanning. Other studies reported on the effects of inter-observer variability on the reproducibility of RFs and reported variable results depending on the type of lesions investigated and the number and experience of observers participating in the study [24; 25; 26; 27]. Most importantly, the lists of reproducible RFs differed based on the site and the scanning settings used for acquiring the data for the analysis. This indicates the need for more thorough analyses of the reproducibility of RFs before performing radiomics modeling. The incidence of Renal cell carcinoma (RCC), which currently constitutes 2% of the global cancer burden, is expected to rise, especially in the developed world [28]. Around 90% of RCCs are found incidentally on abdominal scans obtained for other diagnostic reasons, and only 10% of the cases present with symptoms [29; 30]. Clinically, A number of approaches are used for the diagnosis and treatment of RCC patients that are tailored based on the staging of the disease. Nonetheless, advanced non-invasive diagnostic and prognostic clinical tools remain unmet clinical needs. Numerous studies investigated the possibility of developing clinical decision support tools using radiomic signatures in renal carcinomas, e.g., the differentiation between benign and malignant renal lesions [7; 31; 32], and the prediction of overall survival [33; 34; 35]. However, the limitations of radiomics, as mentioned earlier, hinder incorporating these models in clinical practice. Understanding the impacts of different imaging parameters on RFs is currently the most crucial objective in the field since the majority of imaging datasets are collected retrospectively and most frequently contain scans that were acquired differently. ComBat harmonization is a data-driven harmonization method that was established as a tool to remove different batch effects on gene expression arrays [36]. Because of its successful application in genomics, ComBat has been explored in some studies to harmonize RFs extracted from scans acquired differently [12; 37; 38; 39; 40].