## 4 Discussion
Furthermore, the parameters that were calculated in this study are easily obtainable with simple image-processing methods. The large dataset is particularly interesting as it is a quality-controlled and freely available set of MRI data, which can be used for research and all parameters are clinically confirmed, therefore a control group was not necessary as the methods used can be considered as gold standard. Furthermore, such a large dataset compensates for outliers that can likely occur in such complex entities like gliomas. Another major advantage is the available tumor segmentation. This quality-controlled segmentation that was performed as part of an international challenge and evaluated by independent experts eliminates mostly any kind of bias compared to manual (and local) tumor segmentation efforts, let alone the potential loss of concentration when delineating more than 500 datasets with multiple slices. Therefore, any bias of segmentation in the used dataset can be mostly discarded. Using the ASL data alone, as in this study, allows for an isolated view of this method whether it could bring future benefits in clinical routine or not. This was shown for some parameters (WHO grade, LGG vs. HGG, and tumor vs. edema analysis), while others could not be detected using this method. Thus, the knowledge gained from this study can be translated to clinical routine by being aware of which parameters are being able to be analyzed. Additionally, ASL can not only show the extent of the tumor via visual analysis, but its main use is perfusion analysis. Thus, such a physiological parameter can become interesting when it comes to tumor metabolism (i.e., cell proliferation) and the subsequent effect it has. Using this method for perfusion imaging could then be an aid for deciding whether chemotherapy can be utilized in highly perfused tumors or if there might not be a benefit due to central necrosis or low blood flow which could mean that the active substances cannot reach the tumor cells. There are limitations to this study, mainly the potential loss (or skewing) of information due to the image processing, i.e., reshaping the ASL data to the FLAIR resolution, which potentially alters the obtained information. Furthermore, the data are not equally distributed. This includes, for example, a higher number of wildtype glioblastomas (374 out of 501) or 402 HGGs vs. 99 LGGs. Imaging of tumors is generally not a single-sequence, single-parameter method. In clinical routine, multiple sequences and their derivatives are being used for a holistic image of the tumor to aid in the diagnostic decision. Commonly clinically used are structural imaging including T1, T2, FLAIR, and T1 with contrast agents alongside DWI and potentially SWI. ASL is currently underutilized in clinical routine;therefore, our study aims to show more applications of this MRI method. It is clear that the results from this study cannot claim ASL to be a singular method to diagnose and classify gliomas, but they show the potential of this method in order to bring ASL closer to being part of a routine protocol, as it can differentiate between several tumor entities. In fact, ASL as a means of a clinical imaging method in brain tumors has already been suggested earlier [12; 13; 14; 15; 16; 17; 18; 19; 20]. Another issue that this study raises is that only the image features have been used rather than using CBF as the main predictor of disease; thus, a direct comparison of the findings with the literature cannot be performed. A comparative analysis potentially can reveal a complementary diagnostic benefit when combining CBF and image features. Future studies could include the other acquired (and pre-processed) contrasts to create a more comparative image of the tumor. This could include the histogram analysis of the DWI (as presented earlier [25]) and the SWI alongside ASL or including all the available contrasts.