## 4 Discussion  
In this study, the ASL datasets of 495 patients (with a total of 501 MRI scans) with a confirmed diagnosis of glioma (grades II-IV) were analyzed by means of histogram analysis of the segmented tumor area as well as the surrounding edema and its contralateral side. Using ASL, the differentiation between low-grade and high-grade gliomas has been presented earlier in smaller cohorts and meta-analyses [13; 23; 24]. The final pathological diagnosis according to the WHO 2021 criteria has not yet been performed. Furthermore, from the dataset, the MGMT status and type of mutation were analyzed. Finally, a differentiation between the tumor mass and edema was performed alongside the side-comparison of the edema and the unaffected hemisphere, which has not yet been done before as to our knowledge. The metrics of entropy, mean, standard deviation, skewness, kurtosis, minimum, and maximum are commonly used in the analysis of medical images, and their use has already been presented for gliomas on ADC data for diffusion-weighted imaging [25]. These parameters can be easily calculated using state-of-the-art software, and since the output is a single number only, the results can be directly used for interpretation and comparison. Furthermore, the described parameters depict a wide range of possible influencing factors in medical imaging; e.g., they describe the inhomogeneity of the areas (or the homogeneity) alongside the distribution of gray values and whether they tend to the lower or higher end. Thus, using these parameters, a holistic view of tumor image properties can be drawn in reasonable processing times and with clear and easily understandable results. Additionally,as these metrics are measured and not derived by algorithms, they are less likely to be skewed by the type of calculation that is being performed. The first aim of this study was to investigate whether ASL is an appropriate tool for determining the final pathological diagnosis of gliomas according to the WHO guidelines [2]. In the used dataset, wildtype and IDH-mutated astrocytomas as well as wildtype gliomas and oligodendrogliomas were clinically and pathologically confirmed. Most analyzed metrics (except skewness and kurtosis) showed statistically significant differences between these tumor types. This is also reflected in the box plots (Figure 2) with the larger range of values evident in the IDH-mutated astrocytomas and the wildtype gliomas. These results can be explained with the higher cell differentiation in mutated cells which causes a higher variation in multiple metrics, including their perfusion pattern. Such distinctions are important, as these differences can influence the type of therapy. However, given the overlap of values between the different types of diagnosis, one should be careful to not overestimate the effect of the values, especially in individual cases. As a second result, the differentiation between LGGs and HGGs has been performed, and each of these metrics showed a \(p\)-value below the threshold of 0.05. Thus, it seems appropriate to use ASL as a tool for differentiation between glioma tumor grades. This has already been shown previously [13; 23; 24]. The large dataset available that has been used in this study thus adds to the body of proof to use ASL in such a setting. A differentiation between LGGs and HGGs is straightforward, and the differences in perfusion are evident. Commonly, higher-grade tumors have a higher rate of cell proliferation and thus increased perfusion. In this study, grade II was compared to grades III and IV. The results of this study might be skewed, as the difference in the number of patients is high (99 LGG and 402 HGG). However, as the results show clear differences, tumor perfusion can be seen as a viable option for differential diagnosis in gliomas as part of a larger MRI protocol including other sequences and contrasts. Third, the MGMT status was analyzed. Here, either the positive or negative status was used, while the indeterminate and empty datapoints were discarded.