## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were performed using R language [45] on RStudio (V 3.6.3) [46]. To assess the impacts of the variation in the contrast enhancement phase of the CECT scans on the values of renal lesions' RFs, the concordance correlation coefficient (CCC) was used [47]. The CCC is a measurement of agreement in the value and rank of paired readings, with a CCC \(>\) 0.90 considered a good agreement. Pairwise CCC values were calculated across the three phases, resulting in 3 different scenarios. Reproducible RFs were defined as the RFs with CCC \(>\) 0.9 in a given scenario.  
To assess the ability of ComBat to remove batch effects attributed to the variation in the CECT phase, shape and RFs with (near) zero variance were removed. The batch defined for ComBat harmonization formula was the phase of the CECT scans. ComBat has also been performed in a pairwise manner. RFs with CCC values higher than 0.90 were considered reproducible following ComBat harmonization. The statistical significance of the difference in the number of reproducible RFs before and after ComBat harmonization was assessed using [PERSON]'s test [48]. \(p\)-values below 0.05 were considered significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vendor** & **Number of Scans** & **Convolution Kernels** & **Slice Thickness (mm)** & **Pixel Spacing (mm\({}^{2}\))** \\ \hline GE & 12 & Standard & 1.25–5.0 & 0.69 \(\times\) 0.69–0.86 \(\times\) 0.86 \\ Philips & 2 & B & 3, 4 & 0.68 \(\times\) 0.68–0.97 \(\times\) 0.97 \\ Siemens & 54 & B20f, B30f, B30s, B31f, B31s, & 1–7 & 0.55 \(\times\) 0.55–0.98 \(\times\) 0.98 \\ Toshiba & 1 & FC18 & 2 & 0.74 \(\times\) 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Imaging hardware and parameters of the data included.  
To assess the difference in the ability of RFs to predict overall survival in RCC patients based on the CECT phase used to extract the RFs, patients who were not followed up were removed. The remaining patients were split randomly into 80% training and 20% validation sets. RFs with high correlations, as defined per Spearman's correlation (R > 0.85), were removed from the training RFs set [49]. Univariate feature selection was applied by modeling Cox regression models using each of the remaining uncorrelated RFs [50]. Cox regression is used to establish whether a set of variables, or in our case RFs, are predictive of a particular time event. RFs with a \(p\)-value < 0.05 were considered essential and were further input as the predictors in the final Cox regression model. If no RF was found to have a \(p\)-value < 0.05, the 3 RFs with the least \(p\)-values were selected to construct the final cox regression model. The final Cox regression model was then tested on the validation dataset. The concordance index (C-index) was used to assess the predictive performance of the final model on the validation dataset [51]. The C-index estimates how well a model differentiates between two survival distributions ranging from 0 to 1, with higher values indicating better predictivity. The process was repeated 1000 times to avoid obtaining a significant result by chance, and the performance per imaging phase was averaged over all the repeats. Kruskal-Wallis test was used to assess the difference in the distribution of the C-index values obtained on the 1000 repeats for each imaging phase [52].