## 2 Materials and Methods  
The data used in this study were taken from the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset [21]. The collective consists of 495 patients (501 MRI datasets) with a histopathologically confirmed diagnosis of grade II-IV diffuse gliomas. The patients underwent preoperative MRI at a single medical center between 2015 and 2021. Imaging was performed using a 3T scanner (Discovery 750, GE Healthcare, Waukesha, WI, USA) using a dedicated 8-channel head coil (Invivo, Gainesville, FL, USA). The data used in this study are freely available at the Cancer Imaging Archive (cancerimagingarchive.net, accessed on 14 August 2023). Next to the imaging data, clinical data of the patients were also available. This includes sex, age, tumor grade, pathological diagnosis (WHO classification), MGMT status and index, genetic makeup, IDH mutation, dead or alive status, overall survival, extent of resection, and finally if a biopsy was performed. In this study, the parameters found in Table 1 have been selected and used for further analysis, i.e., WHO classification, mutation status, MGMT, and tumor grade. No discrimination between sex and/or age was performed. The survival status was also not included in the analysis. Details on the distribution of patients with regard to the different classifications used in this study can be found in Table 1. The entire dataset consists of structural images, i.e., T1(with and without contrast agent), T2, and FLAIR as well as physiological imaging data, such as susceptibility-weighted images, diffusion-weighted images (and derivatives), and ASL perfusion. In this study, only the ASL data were used for the purpose of analyzing the tumor itself alongside the surrounding edema and the contralateral side in order to evaluate differences between the affected and contralateral hemispheres. ASL was performed as 3D sequence and registered and resampled to the 3D space defined by the FLAIR image that had a 1 mm isotropic resolution using automated non-linear registration (Advanced Normalization Tools). Thus, the ASL had the same final resolution as the FLAIR scan (and the other sequences from the dataset). Additionally, the available tumor segmentation was used to delineate the ASL images. For this, the available segmentations of the enhancing tumor from the gadolinium-enhanced T1 images and the surrounding edema from the FLAIR images were used. This multicompartment tumor segmentation was part of the 2021 BraTS challenge [22]. In this, the image data first underwent automated segmentation using an ensemble model consisting of prior BraTS challenge-winning segmentation algorithms and were then manually corrected by trained radiologists and approved by 2 expert reviewers. Finally, with both the enhancing tumor and edema segmentation available within the same 3D space, these segmentations were directly used. Precisely, from the individual segmentations, binary masks were created and applied to the ASL perfusion images. This was performed for the highly perfused tumor area and the edema, while the latter was also mirrored to allow for a comparison to the contralateral hemisphere. The remaining image data after the logical AND operation were the used regions of interest for the subsequent statistical analysis, i.e., the region of interest (ROI). An example is shown in Figure 1.  
Within these ROIs, the values for entropy, minimum, maximum, mean value, standard deviation, skewness, and kurtosis of the resulting histograms were calculated using in-house developed scripts using MATLAB R2018b (The MathWorks, Natick, MA, USA). These scripts calculated each parameter on a voxel-wise base within the corresponding masks, and finally, the average value per parameter and patient was taken into consideration for further processing and statistical analysis. Having obtained a single value for each parameter, the subsequent statistical analysis was performed using GraphPad 10.0.0 for Windows (GraphPad Software, Boston, MA, USA).