## 4 Discussion  
In this study, we aimed to investigate (i) the potential of RFs to predict the overall survival in ccRCC, without adjusting for the differences in image acquisition and reconstruction parameters, using thorough statistical analyses; and (ii) the impact of ComBat harmonization on the performance of RFs. We found that on average, radiomic signatures had an above chance level (C-index \(>\) 0.5) for the prediction of overall survival in ccRCC patients. Since in machine learning different data splits could result in significant variations in the performance of the model developed, we ran the analyses with thousand different splits and assessed the performance of radiomic signatures. It was observed that the chances of obtaining a C-index of 0.70 or higher across the data and its subsets was less than 5%, which falls within chance level. Moreover, different RFs were selected with different splits of the data. However, three intensity features were selected in at least 70% of the times  
Figure 3: Boxplot comparison of the C-indices before and after ComBat harmonization. (Supplementary Table S1). These RFs could be considered correlated with overall survival in ccRCC patients. Nonetheless, definitive conclusions can only be reached following the collection of more datapoints, as well as the availability of reproducibility analysis to assess the robustness of these RFs to the variations in imaging parameters observed across the data being analyzed [14]. Furthermore, we compared the performance of radiomic signatures to that of the percentage of tumor tissue necrosis, since the latter is a known clinical predictor [39; 47]. Our results showed that on average, predictions based on the percentage of tumor tissue necrosis alone significantly outperformed those based on the different radiomic signatures. Nonetheless, the most frequently selected RFs (e.g., \"intensity minimum\", \"intensity skewness\", as shown in the supplement Table S1) were CT attenuation-related features, which more or less reflected some information about tumor tissue necrosis. We noticed that none of the predefined RFs was highly correlated to the percentage of tumor tissue necrosis. The findings that the clinical model only--on average--performed better, suggests that the RFs likely resulted in added noise to the survival model. Nevertheless, the comparison is not representative of the full potential of RFs, since in some instances the performance of the combination was higher, and the limitations of reproducibility have to be addressed before a final conclusion could be reached. The data analyzed in this study included scans that were acquired with different hardware and different imaging parameters, such as convolution kernel, slice thickness, and pixel spacing. Variations in these parameters have been previously reported to affect the reproducibility of RFs. A number of studies investigated the effects of variations in convolution kernels on the reproducibility of RFs. All of these studies concluded that the variations in convolution kernel significantly affects the reproducibility of RFs [17; 23; 24; 25]. Similarly, the effects of variations in slice thickness [23; 48; 49; 50], pixel spacing [16; 50], and effective mAs [51] were reported to have significant effects on the reproducibility of RFs. Since the data analyzed in this study included data acquired with different imaging protocols, it is expected that the reproducibility of the majority of RFs is affected significantly, which could explain the wide range of C-indices obtained across the thousand repeats in each approach. It could also explain the selection of intensity based RFs across all the investigated approaches, since these RFs are expected to be the least affected by the variations in our data. In addition, more sensitive RFs that decode texture were not frequently selected, which is in line with the expectations based on the literature.