## 1 Introduction  
With radiomics, quantitative medical image analysis can be performed by extracting quantitative features hypothesized to decode biologic information not even detectable by the human eye [1, 2]. The field has rapidly expanded, with large numbers of published studies regarding the potential of radiomics for clinical applications. Determining lesions' malignant potential [3, 4, 5], assessing response to therapy [6, 7, 8], and predicting overall survival [9, 10] are some of the clinical endpoints investigated. Despite the many possibilities with radiomics, there are a number of limitations that have hindered the translation of radiomic signatures (models) into clinical practice [11, 12]. As is the case of almost all other biomarkers [13], radiomic features (RFs) must be repeatable and reproducible to be used in clinical settings [14, 15]. One of the limitations of real-world data is that CT scanners from different vendors, different models of CT scanners from the same vendor, and the imaging center implementation of these CT scanners result in many variations of image acquisition parameters. Previous studies have reported that the reproducibility of RFs can be significantly affected by the variations in image acquisition parameters including scan acquisition and reconstruction settings [15, 16, 17, 18, 19]. Therefore, it is ideal to evaluate and potentially \"correct\" these settings in order to provide the most accurate radiomics analyses. Two potential solutions for these problems include (1) the selection and exclusive use of robust RFs by studying the results of reproducibility analyses and (2) the reduction of the variations in RF values not attributed to biologic differences [14]by either pre-processing, for example image resampling, and/or post-processing, for example with ComBat harmonization techniques [20]. A number of studies have investigated the effects of different preprocessing methods, such as image resampling [16; 21; 22], kernel normalization [23; 24; 25], and image discretization [26; 27; 28]. These studies reported inconsistent performance depending on the data analyzed. ComBat harmonization, a method originally developed for removing batch effects from gene expression arrays [29], has been investigated as a potential harmonization method in radiomics analyses. ComBat is a function that is based on empirical Bayes and attempts to remove the effects attributed to machinery differences while preserving biologic information. This is done by pooling the gene expressions and providing ComBat formula with the biologic covariates that are known to affect the expression of genes being harmonized [29]. However, there are several limitations to fulfilling the assumptions of ComBat in radiomics analyses. First, radiomic features are differently affected by the variations in imaging parameters as previously reported. Therefore, pooling all RFs in a single ComBat harmonization run might affect the performance of ComBat. Second, the aim of radiomic analyses is to investigate the correlations with biologic information. As such, it is not possible to provide the correct biologic covariates to the ComBat formula, risking the loss of biologic information in the process. Lastly, ComBat is a data-driven harmonization method, i.e., the estimates of batch effects can vary significantly with the variations in the data being harmonized. In this manner, every time new data point are added, the harmonization and modeling process have to be repeated [30], which makes the application of ComBat not suitable for clinical practice without specific adjustments of ComBat for RFs. Hence, we are performing this experiment to evaluate these hypotheses. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal adenocarcinoma, the most common type of renal malignancies which account for around 90% of kidney cancers [31]. ccRCC has variable outcomes for individual patients, and consequently, the predictability of overall survival in ccRCC patients remains challenging clinically [32].