MATERIAL and METHODS: This retrospective study included 131 patients who underwent ASL MRI and anatomic MRI. Cerebral blood flow (CBF) maps were calculated, from which 10 histogram features describing the CBF distribution were extracted within the tumor region. Correlation analysis was performed to determine the correlations between histogram features as well as tumor grades and IDH genotypes. The independent t-test and Fisher?s exact test were used to determine differences in the extracted histogram features, age at diagnosis, and sex in different glioma subtypes. Multivariate binary logistic regression analysis was performed, and diagnostic performances were evaluated with the receiver operating characteristic curves.

RESULTS: CBF histogram features were significantly correlated with tumor grades and IDH genotypes. These features can effectively differentiate LGGs from glioblastomas, and IDH-mut from IDH-wt gliomas. The area under the receiving operating characteristic curve of the model calculated using combined CBF 30th percentile and age at diagnosis in differentiating LGGs from glioblastomas was 0.73. Integrating age at diagnosis and CBF 10th percentile could be more effective in differentiating IDH-mut from IDH-wt gliomas. Furthermore, the combined model had a better area under the receiving operating characteristic curve at 0.856 (sensitivity: 84.4%, specificity: 82.9%).

CONCLUSION: The histogram features on ASL were significantly correlated with tumor grade and IDH genotypes. Moreover, the use of these features could effectively differentiate glioma subtypes. The combined application of age at diagnosis and perfusion histogram features resulted in a more comprehensive identification of tumor subtypes. Therefore, ASL can be a noninvasive tool for the pre-surgical evaluation of gliomas. Keywords : Gliomas, Isocitrate Dehydrogenase, Arterial spin labeling, perfusion MRI, Histogram, Radiology, Diagnosis