Recent studies have demonstrated a high potential of PASL and CASL in differentiating high- and low-grade gliomas before surgery [8–14, 31]. Although ASL-perfusion is a relatively new method, it has already proved to be effective in diagnosis of cerebral gliomas. There is a number of studies establishing a high correlation between tumor blood flow derived from ASL-perfusion and DSC-perfusion which is known as the “gold standard” in perfusion studies [33, 34].
Several recent studies were devoted to ASL-perfusion in differentiating cerebral gliomas. They showed contradictory results regarding sensitivity and specificity of this method in distinguishing LGG and HGG and TBF threshold values. An our opinion, these differences are the result of different approaches used for selecting ROI/VOI in TBF assessing as well as different methods of TBF normalization.
To measure TBF several study groups used large ROI covering almost all the tumor volume. Brendle et al. (2017) measured TBF in 63 patients with high- and low-grade gliomas using pASL and DCE, thus segmenting the whole tumor excluding large vessels and areas of necrosis. Also mean TBF was measured in particular volume. The authors did not find any significant difference in ASL-TBF parameter for HGG and LGG. ASL-TBF normalization was not the aim of their study.
Zeng et al. (2017) selected a slice with the highest TBF used color TBF maps. Using post-contrast T2-FLAIR the authors then chose ROI incorporating the whole tumor volume on the selected slice. Next ROI was transcribed on perfusion maps and afterwards, mean TBF was measured. ROI also included cystic components of the tumor, necrosis, hemorrhages characterized by low perfusion values and as such they could decrease the TBF . The authors used pCASL and normalization was performed to contralateral grey matter.
Wang et al. (2019) used a dual approach for TBF measuring. Firstly, they chose a slice with the highest TBF according to color maps. Then on the slice they delineated the whole tumor volume in this slice in T2-FLAIR and measured mean TBF. Area with the highest TBF was selected on the slice and the ROI of 95–105 pixels was placed on it to get max TBF. The authors used pCASL and normalization was performed to contralateral grey matter. Maximal TBF was found to be associated with the highest sensitivity and specificity in distinguishing LGG and HGG.
A different approach was adopted for measuring TBF in other studies, including small-sized ROI/VOI. Lin et al. (2015) included solid component of the tumor picked on the slice with the highest TBF on the color maps . ROI size was not specified in the paper. The authors did not use TBF normalization for the differential diagnosis in this paper.
Ma et al. (2017) used color maps to select the highest TBF and ROI was set to 50–60 mm2 . In contrast, we used smaller ROI size of 20 ± 10 mm2 and performed normalization to the ROI within tumor in the mirror-like area of the contralateral hemisphere.
Hashido et al. (2020) used small VOI 162.8 mm3 in size and normalization was performed to the contralateral white matter.
Hales et al. (2019) used 50 mm2 ROI and normalization was performed to the contralateral grey matter.
The approach used by Xiao et al. (2015) was mostly close to the one we used: the researchers placed several 28–32 mm2 ROI scattered within the whole tumor volume. Then ROIs with maximal TBF were picked for analysis . Normalization was performed to CBF in the cerebellar white matter.
All the above mentioned papers were based upon pCASL technique.
Studies using small-size ROI and VOI demonstrated higher sensitivity and specificity in distinguishing LGG and HGG. According to meta-analysis performed by Alsaedi et al. (2019) maxTBF was proved to be more informative rather than meanTBF in differentiating cerebral glioma grades.
Our results coincide with the aforementioned studies, but demonstrate higher sensitivity and specificity in distinguishing LGG and HGG and much higher AUC. maxTBF for low-grade gliomas in our study group was much lower compared to other studies, and much higher for high-grade gliomas. The observed difference could be explained by different ROI selection for TBF measuring.
Our study is also different by nTBF: the difference was mostly defined by the site of normalization – we used center semiovale of the contralateral hemisphere.
TBF value comparison computed using pCASL in grade III and grade IV glioma groups was evaluated in a small number of studies due to small patient sample size. Zeng et al. (2017)  revealed a significant difference in TBF and nTBF values for these groups of patients, but they did not present ROC-analysis results and thus to evaluate ASL-perfusion effectiveness in differentiating cerebral gliomas grade III and grade IV. Wang et al. (2019) did not find any significant difference for TBF and nTBF in these two groups of patients.
In our study we used both absolute (TBF) and normalized (nTBF) maximal tumor blood flow and demonstrated higher sensitivity and specificity in distinguishing LGG and HGG. We found a significant difference in TBF for grade III and grade IV gliomas, although low sensitivity and specificity did not let us using ASL-perfusion for differentiating gliomas of grades III and IV. Importantly, excluding anaplastic oligodendrogliomas affected neither sensitivity nor specificity. We also failed to detect any statistically significant difference in TBF for diffuse astrocytomas and oligodendrogliomas as well as anaplastic astrocytomas and anaplastic oligodendrogliomas. On the contrary, in the study conducted by Zeng et al. (2017) exclusion of oligodendrogliomas and anaplastic oligodendrogliomas led to TBF lowering in glioma grade II and glioma grade III groups and statistically significant difference for this parameter in grade III and grade IV glioma groups. This can be explained by ROI selection method: we adopted the protocol for selecting small ROI in the highest TBF areas, whereas Zeng et al. (2017) delineated the whole tumor volume on the slice with maximal TBF.
It is well-known, that oligodendrogliomas are characterized by even higher microvascular density within the whole tumor volume . Inclusion of the whole tumor volume on the slice in the measurement area results in the increased TBF on perfusion map in oligodendrogliomas. Our results suggest that application of small ROI showed enable measuring maxTBF which is the same for astrocytomas and oligodendrogliomas.