Comparison of the normalized cerebral blood volume (CBV) between different models and evaluation of the efficacy of gadolinium leakage in evaluating preoperative adult-type gliomas

Introduction

Brain glioma is the most common malignant tumor of the nervous system (1). According to the World Health Organization (WHO) 2021, gliomas are classified into 4 grades: grades 1–2 are low-grade gliomas (LGGs), and grades 3–4 are high-grade gliomas (HGGs) (2,3). The grade of glioma is closely related to the recurrence rate and prognosis of patients after treatment. Gliomas with different genotypes also exhibit different biological behaviors and imaging findings (4). Therefore, the accurate classification of glioma before surgery is very important for neurosurgeons to make suitable treatment plans. Currently, magnetic resonance imaging (MRI) is usually employed to diagnose and follow-up brain tumors (5-7). Dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) has been demonstrated to characterize and differentiate gliomas (3,8,9). DSC-PWI can provide information on the vascular and microvascular environments of brain tumors (3,4). In the clinic, the injection of gadolinium (Gd) contrast agent is necessary for DSC-PWI (10,11), and the time-signal intensity curve (TIC) and the arterial input function (AIF) of brain tissue can be used to generate perfusion metrics, namely, the cerebral blood volume (CBV), cerebral blood flow, mean transit time, and time to peak (12,13).

Theoretically, the precondition of DSC-PWI used to evaluate brain gliomas is an intact blood-brain barrier (BBB) (10). Nevertheless, owing to the biological characteristics and genetic status of brain gliomas, the invaded BBB leads to Gd leakage and failure to obtain accurate perfusion information (3). Gd leakage can be visualized by the raw TIC of glioma, regardless of post-processing methods (3), which is also used to guarantee the perfusion efficiency in characterizing glioma. However, it is necessary to correct the raw TIC after negative enhancement (14-16). Reducing the effect of Gd leakage mainly focuses on the application of the preloading of Gd, lower flip angle (FA), and post-processing methods (12). In general, post-processing methods of DSC-PWI [i.e., AIF and gamma-variate fitting (GVF)] can both generate perfusion parameters and correct the raw TIC. The weight of different types of Gd leakage can also be quantitatively evaluated by the percentage of signal recovery (PSR) of raw TIC (3). Additionally, the PSR has been demonstrated to differentiate intracranial tumors (3,17-19). AIF is based on the dilution theory of indicators. It requires the combination of the TIC of the input arteries and brain tissues to evaluate the CBV through deconvolution. The AIF can also generate two indicators of T₂* and T₁ leakage, which characterize both leakage effects. Theoretically, the T₂* indicator mainly reflects the effect of local magnetic field inhomogeneity, whereas the T₁ indicator reflects the change in longitudinal relaxation. Meanwhile, GVF does not require any additional assumptions. It fits the actual curve based on the assumed shape of TIC, thereby eliminating signal interference. Unlike the AIF, GVF can correct the raw TIC to reduce the influence of curve instability caused by the recirculation and leakage of contrast agent (20,21). However, the post-processing methods (AIF, GVF) used to characterize intracranial tumors have been inconsistent in previous works. In addition, the consistency of mirror normalized relative CBV (nrCBV) obtained from different methods of DSC-PWI for brain gliomas with different grades and genetic statuses [isocitrate dehydrogenase (IDH) status] is still ambiguous. In theory, the bias of perfusion parameters could be affected by Gd leakage and other potential reasons (e.g., cellularity, vascular architecture, and microvascular architecture) (3,22).

This retrospective study aimed to investigate the consistency of the nrCBV between the AIF and GVF in preoperative adult-type gliomas with different grades and IDH statuses and to verify and compare the efficiency of PSR and T₂* and T₁ leakage effects in evaluating the grade and IDH status of preoperative adult-type gliomas. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1439/rc).

Methods

This retrospective study was approved by the Institutional Review Board (IRB) of Cangzhou Central Hospital [No. 2023-223-01(z)], and due to the retrospective nature of the study, the IRB of Cangzhou Central Hospital waived the need of obtaining informed consent. The study was in accordance with the Declaration of Helsinki and its subsequent amendments.

Patients

We retrospectively included 64 patients with pathologically confirmed glioma from 1 January 2020 to 1 January 2024. The inclusion criteria were as follows: (I) age >18 years; (II) contrast-enhanced MRI and DSC-PWI were performed before any treatment of brain gliomas (the details of all MRI sequences are provided in Table 1); (III) patients without any other neurological or psychiatric disorders; and (IV) the grade and IDH status of the gliomas were confirmed according to the WHO 2021 criteria (4). The exclusion criteria were as follows: (I) perfusion imaging with obvious artifacts; (II) contralateral cerebral hemispheres with gliomas or other lesions, which hinder the drawing of mirror regions of interest (ROIs); and (III) extravasation of Gd. Finally, 60 patients were enrolled (Figure 1). Among the 60 patients, 19 gliomas were confirmed as grade 2, 18 gliomas were confirmed as grade 3, and 23 gliomas were confirmed as grade 4 (Table 2), respectively.

Figure 1 Flowchart of patients enrolled in the study. MRI, magnetic resonance imaging.

Perfusion imaging and data processing

3.0 T MRI (GE Discovery 750 W, GE Healthcare, Chicago, IL, USA) was employed to perform all MRI sequences. A 20-channel head phased-array coil was used in this investigation. MRI sequences contained non-contrast MRI, contrast-enhanced T1-weighted imaging, and DSC-PWI. Gradient-recalled echo-echo planar imaging (GRE-EPI) with 90° FA was used for DSC-PWI, which revealed 50 phases without preloading with Gd. Gd (Gadoterate meglumine, Hengrui, Lianyungang, China) (0.1 mmol/kg) was injected at a rate of 3.5 mL/s, followed by 15 mL saline (3.5 mL/s). The DSC-PWI was processed on a GE AW 4.7 workstation (Advantage for Windows; GE Healthcare). The DSC-PWI data was processed by two radiologists with 6 and 11 years of clinical experience in neuroimaging. Both radiologists were blinded to patients’ clinical information.

Motion correction is mandatory for the AIF and GVF. The motion correction methods of AIF and GVF were mainly achieved through automatic, rigid (or affine) registration algorithms based on image grayscale information, aligning the entire dynamic sequence to a reference image without contrast agent. AIF and GVF were subsequently used to obtain the relative CBV (rCBV). The auto-mode of AIF can abandon the section of input and outflow vessels. The semiautomatic mode was required when the input artery could not be labeled. In addition, the types of raw TICs of all gliomas (1 balanced, 2 descending, 3 ascending) (Figure 2) were recorded. The region of interest (ROI) was outlined the maximum section of the enhanced gliomas, which was described in a previous study (23), avoiding necrosis and hemorrhage. The drawing ROIs was performed by the two radiologists. The mirror ROI was located on the contralateral cerebral hemisphere to normalize the rCBV. The ROIs of the lesion (ROI_lesion) and mirror ROI were cloned to all phases of the current series to analyze the T₂* and T₁ leakage effects. The formula for calculating the nrCBV was as follows:

AIF-nrCBV=AIF-rCBVlesionmirrorROI[1]

GVF-nrCBV=GVF-rCBVlesionmirrorROI[2]

Figure 2 Three representative patients (A-C) with preoperative adult-type gliomas. The upper row represents the T₂* and T₁ leakage effect indicators (A-C), and the lower row represents the raw TICs, respectively (D-F). (A) The T₂* and T₁ leakage effect indicators of patient A with astrocytoma (grade 2 and IDH^M) in the right cerebral hemisphere and the T₂* and T₁ leakage effects were graded as 1 point and 0 points, respectively. (B) The T₂* and T1 leakage effect indicators of patient B with glioblastoma (grade 4 and IDH^W) in the left cerebral hemisphere, and the T₂* and T₁ leakage effects were graded as 3 points and 0 points, respectively. (C) Both leakage effect indicators of patient C with glioblastoma (grade 4 and IDH^W) in the left cerebral hemisphere and the T₂* and T₁ leakage effects were graded as 0 points and 3 points, respectively. (D) The raw TIC (the yellow dotted line) of patient A is of the balance type. (E) The raw TIC (the yellow dotted line) of patient B is of the descending type. (F) The raw TIC (the yellow dotted line) of patient C is of the ascending type. EPI, echo planar imaging; GR, gradient; IDH^M, isocitrate dehydrogenase-mutant; IDH^W, isocitrate dehydrogenase wild-type; L, level; m, minimum; M, maximum; TIC, time-signal intensity curve; W, width.

Finally, the nrCBV was the average of the two radiologists.

The ROI can be cloned to all imaging sequences. Then, the interpretation of T₂* and T₁ leakage effects and calculation of the nrCBV can be performed on the same ROI. Two radiologists used a Likert scale (0, none; 1, mild; 2, moderate; 3, severe) to interpret the weighted point of T₂ and T₁ leakage effects on the imaging, which takes the mirror ROI as the reference (Figure 2) to avoid interference from background noise. A third radiologist with 13 years of experience in neuroimaging arbitrated any disagreements between the above two radiologists. All radiologists should be blinded to the raw TIC to ensure the accuracy of assessing different leakage effects. The difference in points between the T₂* and T₁ leakage effects was subsequently calculated to compare the weights of the leakage effects. The formula for calculating the difference in weight between two leakage effects was as follows:

Thedifferenceinpoints=(pointoftheT2*leakageeffect)−(pointoftheT1leakageeffect)[3]

The quantitative metric PSR derived from the raw TIC of AIF was subsequently calculated according to previous reports (3), and the final PSR was the average of the other two radiologists with 5 years and 7 years of experience in neuroimaging. The PSR as an effective quantitative method for assessing gliomas, which has been demonstrated in previous investigations (3), was employed to verify the hypothesis that T₂* & T₁ leakage effects are meaningful for evaluating adult-type glioma grade and IDH status.

Statistical analysis

The software SPSS 22.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism (version 9.5.1, GraphPad Software, La Jolla, CA, USA) were used to analyze all the data. The data with a normal distribution were expressed as the means ± standard deviations. The consistency and difference of the nrCBV between the AIF and GVF was tested by Pearson’s correlation coefficient (r) (≤0.25: low correlation, >0.25; ≤0.5: moderate correlation, >0.5; ≤0.75: strong correlation, >0.75: excellent correlation) and Bland-Altman plots in adult-type gliomas with different grades and IDH statuses. The correlation between the PSR and the difference in points between T₂* and T₁ leakage effects was tested via Spearman correlation analysis. The PSR was compared between different adult-type glioma grades using one-way analysis of variance and Tukey post hoc analysis, and the difference in points of T₂* and T₁ leakage effects were tested between different IDH status [IDH-mutant (IDH^M) vs. IDH wild-type (IDH^W)] via Fisher’s exact test. Interobserver consistency of the nrCBV, PSR, and points of T₂* and T₁ leakage effects was tested by the intraclass correlation coefficient (ICC) (<0.5: poor, ≥0.5, <0.75: moderate, ≥0.75, <0.9: good, ≥0.9: excellent) and the kappa test. Two-sided tests were used for statistical analysis, and a significant difference was indicated when P<0.05.

Results

The numbers of different types of TICs were 19, 9, and 32, respectively (Table 3). Most adult-type gliomas with type 2 and type 3 TIC were grade 4. Most adult-type gliomas with type 1 TIC were IDH^M.

In all subgroups, the AIF-nrCBV was correlated with the GVF-nrCBV (r=0.56–0.90, all P<0.01) (Table 4), whereas an excellent correlation was detected in grade 2 and IDH^W (both r>0.8). Bland-Altman plots demonstrated that the AIF-nrCBV was slightly lower than the GVF-nrCBV in grade 2 and IDH^M; conversely, the AIF-nrCBV was slightly greater than the GVF-nrCBV in grade 3, grade 4, and IDH^W (Table 5).

Spearman correlation analysis revealed that the difference in points between the T₂* and T₁ leakage effects was negatively correlated with the PSR (r=−0.64, P<0.001). The differences in the PSR between different grades and IDH status (IDH^W vs. IDH^M) were significant (F=4.38, P=0.02; t=2.91, P=0.005) (Figure 3A,3B).

Figure 3 Comparative analysis of the PSR. (A) The PSR in adult-type gliomas with grade 4 is greater than that in adult-type gliomas with grade 2, and (B) the PSR in adult-type gliomas with IDH^W is greater than that in adult-type gliomas with IDH^M. *, P<0.05; **, P<0.01. IDH^M, isocitrate dehydrogenase-mutant; IDH^W, isocitrate dehydrogenase wild-type; ns, no significance; PSR, percentage of signal recovery.

Comparative analysis of different leakage effects derived from AIF demonstrated that the adult-type gliomas with high grade were more likely to display T₂* and T₁ leakage effects than were the adult-type gliomas with low grade (all P<0.001) (Figure 4A,4B). Compared with adult-type gliomas with IDH^M, the T₂* leakage effect isn’t more likely to occur in IDH^W (P=0.09) (Figure 4C). However, there were significant differences in the T₁ leakage effect between different IDH statuses (P=0.03) (Figure 4D). Moreover, the weight differences of T₂* and T₁ leakage effects were not significant in adult-type gliomas with different grades (P=0.06) and different IDH statuses (P=0.17) (Figure 5).

Figure 4 Comparative analysis of T₂* and T₁ leakage effects in adult-type gliomas with different grades and IDH statuses. (A) Compared with gliomas with grade 2, the gliomas with grade 3 and 4 prefer T₂* leakage. (B) Compared with gliomas with grade 2, the gliomas with grade 3 and 4 prefer T₁ leakage. (C) Compared with IDH^M, IDH^W prefers T₂* leakage. (D) Compared with IDH^M, IDH^W prefers T₁ leakage. IDH, isocitrate dehydrogenase; IDH^M, isocitrate dehydrogenase-mutant; IDH^W, isocitrate dehydrogenase wild-type.

Figure 5 Comparative analysis of the weighted differences between T₂* leakage and T₁ leakage effects in gliomas of different grades and IDH statuses. (A) The difference in the point of T₂* and T1 leakage effects show no significant difference among adult-type gliomas with different grades (P<0.05). (B) The difference in the point of T₂* and T₁ leakage effects show no significant difference among adult-type gliomas with different IDH status (P<0.05). ≥0 represents the point where the T₂* leakage effect is greater than the T₁ leakage effect; <0 represents the point where the T₂* leakage effect is smaller than the T₁ leakage effect. IDH, isocitrate dehydrogenase; IDH^M, isocitrate dehydrogenase-mutant; IDH^W, isocitrate dehydrogenase wild-type.

ICC =0.939 [95% confidence interval (CI): 0.932–0.945] and the kappa-value (0.915) both demonstrated the intra-observer consistency of the nrCBV and the judgment of T₂* and T₁ leakage effect indicators were good. The ICC of 0.927 (95% CI: 0.913–0.938) also showed that the intra-observer consistency of the PSR was good.

Discussion

In theory, the TIC is the basis of DSC-PWI for obtaining the CBV. The area under the negative enhancement curve is correlated with perfusion information, which can characterize glioma vascularization. Most adult-type gliomas with high grade and IDH^W exhibited ascending curves in the present study (Table 3). The results of the quantitative analysis demonstrated that the AIF-nrCBV was correlated with the GVF-nrCBV in adult-type gliomas with different grades and IDH statuses. However, there was a slightly paired difference between the AIF-nrCBV and GVF-nrCBV in adult-type gliomas with different grades and IDH statuses. In addition, combining the analysis of PSR and both leakage effects revealed that the adult-type glioma with higher grade and IDH^W exhibited higher PSR compared with lower grade and IDH^M. Meanwhile, PSR could be the more effective method for evaluating adult-type gliomas compared with leakage effects. The role of the leakage effects in the assessment of adult-type gliomas still requires further investigation.

Gd leakage, which is caused by destruction of the BBB (Figure 6), results in instability of perfusion parameters (15,21,24,25). Previous reports have demonstrated that using preload leakage correction, spin echo, different echo times, and lower FA can improve the accuracy of perfusion parameters (21,26-28). However, those methods include reducing the T₁ effect as a primary aim. Additionally, the different models can compensate for the distortion of the raw TIC. The GVF uses gamma fits to decrease the side-effect of recirculation and Gd leakage after negative enhancement (21). The correlative analysis of nrCBV emphasized the consistency between AIF and GVF. Nevertheless, the different paired differences in nrCBVs could demonstrate that the discrepancy in nrCBVs derived from different postprocessing methods could be affected by adult-type glioma grade and IDH status. The AIF-nrCBV is less than GVF-nrCBV in adult-type glioma with grade 2, but the AIF-nrCBVs are larger than GVF-nrCBVs in adult-type gliomas with grade 3 and 4 (Table 5). The AIF-nrCBV is less than GVF-nrCBV in adult-type gliomas with IDH^M, however, the AIF-nrCBV is larger than GVF-nrCBV in adult-type gliomas with IDH^W (Table 5). The Gd leakage occurs at a relatively later stage of the TIC, which mainly affects the CBV (29). The type 3 raw TIC presented the T₁ leakage effect could underestimate CBV (26); moreover, the T₂* leakage effect could lead to an overestimation of CBV. In the present study, the AIF-nrCBV was greater than the GVF-nrCBV in adult-type gliomas with type 2 TIC, and the AIF-nrCBV was lower than the GVF-nrCBV in adult-type gliomas with type 1 and 3 TIC (Table 3). Additionally, the paired difference of nrCBV between AIF and GVF was larger in adult-type gliomas with type 2 TIC (0.27±0.31) than the others (Table 3). The quantitative difference of nrCBV between different curve types can also be verified by the technical discrepancy between AIF and GVF. Figure 2D-2F concisely illustrates the discrepancy in correcting different types of raw TICs between AIF and GVF (i.e., the areas under the corrected curve between AIF and GVF are inconsistent). The inference based on the technical analysis can also be supported by the small bias of the nrCBV between the AIF and GVF in the type 1 TIC (Table 3, Figure 2D). The more aggressive biological behavior of adult-type gliomas with higher grades and IDH^W may contribute to the greater weight of the T₁ leakage effect, which increases the probability of presenting the type 3 TIC. The comparison of T₁ leakage in adult-type gliomas with different grades and IDH statuses also confirmed this (Figure 4B,4D). The paired difference in the nrCBV between the AIF and GVF is inconsistent across different types of TICs, and the AIF overestimates the nrCBV compared with the GVF in adult-type gliomas with grades 3, 4 and IDH^W, which mostly present type 3 TICs. These inconsistent differences may be explained by the greater paired difference between AIF-nrCBV and GVF-nrCBV in adult-type gliomas with type 2 TIC (Table 3). These findings also demonstrate that the greater paired difference of the nrCBV between the AIF and GVF could occur in adult-type gliomas with greater T₂* leakage effects. The correlation between PSR and the point difference of different leakage effects demonstrated that both leakage effects could also characterize the equilibrium of the T₂* and T₁ leakage effects. This finding indicates the consistency of the interpretation of different leakage effects between the subjective point and the objective PSR. The transverse relaxivity at tracer equilibrium reflected the balance of T₂* and T₁ leakage effects, is also correlated with the PSR (r=−0.87) (3). The previous result echoes the result of the correlation between the point of leakage effects and PSR. However, the correlation coefficient was lower than that in previous studies, which could illustrate the limitation of subjective interpretation compared with quantitative transverse relaxivity at tracer equilibrium. Quantitative comparison demonstrated that PSR can be employed to differentiate adult-type glioma grade and IDH status, which is also consistent with the findings of previous studies (3,18,19,30). Compared with adult-type gliomas with grade 2 and IDH^M, the adult-type gliomas with grade 4 and IDH^W have higher PSRs (Figure 3). The greater weight of the T₁ leakage effect contributes to the ascending curve with a high PSR. These inferences can also be verified by a comparison of the point differences of both leakage effects. Compared with the adult-type gliomas with low grade and IDH^M, the weight of the T₁ leakage effect was greater than that of the T₂* leakage effect in adult-type gliomas with high grade and IDH^W. However, the difference in the PSR according to IDH status was inconsistent with a previous report (31), which may be due to the difference in the adult-type glioma grade selection. The main leakage effects for type 2 TICs and type 3 TICs are the T₂* and T₁ effect, respectively. The reason for the type 1 TIC is intelligible, namely, the approximate weights of the T₂* and T₁ leakage effects. Almost 63% of adult-type gliomas with type 1 TICs presented zero difference between two leakage effects, and the remaining adult-type gliomas had a small difference between the T₂* and T₁ leakage effects. Compared with the other types, the number of adult-type gliomas with type 2 curves is the lowest. Owing to the complexity of the T₂* effect, the variations in magnetic susceptibility between different chambers could be a potential reason (3,22,32,33). Additionally, since there is minimal or no leakage in the adult-type gliomas with low grade, the T₂* effects likely originate from the intravascular compartment rather than the extravascular space (3). The microscopic factor of the T₂* effect is the accumulation of contrast media in the extravascular extracellular compartment (21,22). Compared with the previous report, the likelihood of the T₂* effect was significantly lower at the dose of 0.1 mmol/kg Gd used in the present investigation (21), which can explain why the number of adult-type gliomas with type 2 TICs was the lowest. In this study, the proportion of adult-type gliomas with predominantly T₂* leakage was nearly 15% (9/60), which was lower than that reported in previous investigations (24). This discrepancy could also be explained by the differences in the concentration of Gd contrast media (0.1 vs. 0.2 mmol/kg) and the difference of the enrolled adult-type gliomas (3). Compared with the dual echo sequence, the GRE-EPI sequence used in this study may only be able to achieve the T₂* leakage effect at the macroscopic level, which reduces the sensitivity of the susceptibility gradients between different chambers. The difference in sequences could be another reason for the small number of adult-type gliomas with T₂* leakage effects and type 2 TICs. On the basis of the above analysis, the adult-type with low grade and IDH^W could be more susceptible to the T₁ leakage effect than to the T₂* leakage effect at a dose of Gd (0.1 mmol/kg). However, one previous study indicated that the T₂* leakage effect could be affected by the cell volume fraction, which can increase the susceptibility gradients to enhance the T₂* leakage effect (22). The relatively greater number of adult-type gliomas with high grade and IDH^W with higher tumor cell density (31) in type 2 TICs also verified this hypothesis. The larger AIF-nrCBV (IDH^Wvs. IDH^M: 1.90 vs. 1.66) also echoes the high cell fraction in adult-type gliomas with type 2 TICs, which is also consistent with the findings of Sanvito et al. (22). In theory, the non-usage of small FAs and the preloading of Gd could increase the T₁ effect, which cannot be completely eliminated (29,34). These differences in protocols could also lead to paired differences in the nrCBV between AIF and GVF across different adult-type glioma grades and IDH statuses in this study.

Figure 6 The contrast-enhancement T1WI of the three representative patients (A-C) with preoperative adult-type gliomas. (A) The T1WI of patient A without obvious enhancement indicated that there was no significant leakage of gadolinium contrast media. The adult-type glioma exhibited type 1 TIC (AIF-nrCBV vs. GVF-nrCBV: 0.92 vs. 0.77; point of T₂* leakage effect vs. point of T1 leakage effect: 1 vs. 0). (B,C) The T1WI of patient B (type 2 TIC; AIF-nrCBV vs. GVF-nrCBV: 1.36 vs. 1.31; point of T₂* leakage effect vs. point of T₁ leakage effect: 3 vs. 1) and patient C (type 3 TIC; AIF-nrCBV vs. GVF-nrCBV: 1.57 vs. 1.98; point of T₂* leakage effect vs. point of T₁ leakage effect: 1 vs. 3) with glioblastoma both exhibited high signal intensity, which indicates the large dose of gadolinium agent leakage. AIF-nrCBV, the normalized relative cerebral blood volume derived from arterial input function; GVF-nrCBV, the normalized relative cerebral blood volume derived from gamma-variate fitting; T1WI, T1-weighted; TIC, time-signal intensity curve.

Compared with white-gray matter with stable perfusion status (17,35), the mirror ROI could increase the perfusion difference compared with previous studies, which use white-gray matter to normalize the rCBV. This difference in normalizing the rCBV could be the other potential reason for the paired difference in the nrCBV between the AIF and GVF in the investigation. However, in terms of the research objective of the study, the mirror ROI can guarantee the judgment of T₂* and T₁ leakage effects, which take the contralateral cerebral hemisphere as the reference. A mirror ROI could improve the generalization of visual interpretation of both leakage effects (ICC =0.93). Another reason that is worth noting and differs from previous studies is the heterogeneity of adult-type gliomas, which may increase the possibility of differences in nrCBV. Compared with three-dimensional (3D) voxels, two-dimensional (2D) ROI of gliomas could be another reason for the increased probability of overlooking glioma heterogeneity in studies (3,17). DSC-PWI was based on the gradient recalled echo sequence, which can be affect by B0 field inhomogeneities, slice-timing misalignment and physiological noise (36). Those technical issues could be the other reason for the difference of nrCBV between AIF and GVF.

The differences in the T₂* and T₁ leakage effects in adult-type gliomas with different grades and IDH statuses (Figure 4) support the paired differences in nrCBV between AIF and GVF. However, the difference in the point of different leakage effects is not significantly different (all P>0.05) among adult-type gliomas with different grades and IDH statuses (Figure 5) also indicates that the subjective scoring of leakage effects for evaluating adult-type gliomas may require further verification. The remarkable difference of the objective metrics (PSR) and the subjective metrics (both leakage effects) demonstrates that the PSR could has a more accurate performance in evaluating the gliomas grade and in identifying IDH status. The differences in the subjective and objective parameters are particularly evident among different IDH status (Figures 3,5). Additionally, when it comes to simply evaluating adult gliomas, non-injection of Gd contrast agents is more favorable for patients with renal insufficiency (7).

This work has some limitations. First, the small sample size hinders further validation of different leakage effects in adult-type gliomas of different types. Second, different MRI manufacturers and scanning protocols were not investigated in this study. Third, the subjective assessment of different leakage effects cannot be utilized to identify other brain solid tumors.

Conclusions

The AIF-nrCBV is correlated with the GVF-nrCBV in gliomas, regardless of adult-type glioma grade and IDH status. However, the paired difference in the nrCBV between the AIF and GVF could be affected by the grade and IDH status. Compared with PSR, the two leakage effects have lower efficacy in differentiating the grades of gliomas and the IDH status.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1439/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1439/dss

Funding: This study was supported by the Science and Technology Planning Project of Cangzhou City (No. 222106148).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1439/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Cangzhou Central hospital [No. 2023-223-01(z)], and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Ostrom QT, Gittleman H, Liao P, Vecchione-Koval T, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol 2017;19:v1-v88. [Crossref] [PubMed]
2. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 2021;23:1231-51. [Crossref] [PubMed]
3. Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Martinez-Zalacain I, Castell J, Camins A, Vidal N, Bruna J, Cos M, Perez-Lopez R, Oleaga L, Warnert E, Smits M, Majos C. Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. Eur Radiol 2024;34:5320-30. [Crossref] [PubMed]
4. Pons-Escoda A, Majos C, Smits M, Oleaga L. Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units. Radiologia (Engl Ed) 2024;66:260-77. [Crossref] [PubMed]
5. Li Y, Liu Y, Liang Y, Wei R, Zhang W, Yao W, Luo S, Pang X, Wang Y, Jiang X, Lai S, Yang R. Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol 2022;32:8039-51. [Crossref] [PubMed]
6. Stockham AL, Tievsky AL, Koyfman SA, Reddy CA, Suh JH, Vogelbaum MA, Barnett GH, Chao ST. Conventional MRI does not reliably distinguish radiation necrosis from tumor recurrence after stereotactic radiosurgery. J Neurooncol 2012;109:149-58. [Crossref] [PubMed]
7. Zheng WY, Shi YT, He YY, Lin RL, Xiao BH, Jiang RF, Wáng YXJ. A novel MRI diffusion metric 'slow diffusion coefficient' (SDC) for diagnosing isocitrate dehydrogenase (IDH) genotype in diffuse gliomas: initial promising results. BMC Med Imaging 2025;25:427. [Crossref] [PubMed]
8. Nakajima S, Okada T, Yamamoto A, Kanagaki M, Fushimi Y, Okada T, Arakawa Y, Takagi Y, Miyamoto S, Togashi K. Differentiation between primary central nervous system lymphoma and glioblastoma: a comparative study of parameters derived from dynamic susceptibility contrast-enhanced perfusion-weighted MRI. Clin Radiol 2015;70:1393-9. [Crossref] [PubMed]
9. Purohit B, Kamli AA, Kollias SS. Imaging of adult brainstem gliomas. Eur J Radiol 2015;84:709-20. [Crossref] [PubMed]
10. Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990;14:249-65. [Crossref] [PubMed]
11. Belliveau JW, Kennedy DN Jr, McKinstry RC, Buchbinder BR, Weisskoff RM, Cohen MS, Vevea JM, Brady TJ, Rosen BR. Functional mapping of the human visual cortex by magnetic resonance imaging. Science 1991;254:716-9. [Crossref] [PubMed]
12. Boxerman JL, Quarles CC, Hu LS, Erickson BJ, Gerstner ER, Smits M, et al. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 2020;22:1262-75. [Crossref] [PubMed]
13. Pons-Escoda A, Garcia-Ruiz A, Garcia-Hidalgo C, Gil-Solsona R, Naval-Baudin P, Martin-Noguerol T, Fernandez-Coello A, Flores-Casaperalta S, Fernandez-Viñas M, Gago-Ferrero P, Oleaga L, Perez-Lopez R, Majos C. MR dynamic-susceptibility-contrast perfusion metrics in the presurgical discrimination of adult solitary intra-axial cerebellar tumors. Eur Radiol 2023;33:9120-9. [Crossref] [PubMed]
14. Quarles CC, Ward BD, Schmainda KM. Improving the reliability of obtaining tumor hemodynamic parameters in the presence of contrast agent extravasation. Magn Reson Med 2005;53:1307-16. [Crossref] [PubMed]
15. Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27:859-67.
16. Hu LS, Kelm Z, Korfiatis P, Dueck AC, Elrod C, Ellingson BM, Kaufmann TJ, Eschbacher JM, Karis JP, Smith K, Nakaji P, Brinkman D, Pafundi D, Baxter LC, Erickson BJ. Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma. AJNR Am J Neuroradiol 2015;36:2242-9. [Crossref] [PubMed]
17. Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Cos M, Vidal N, Plans G, Bruna J, Perez-Lopez R, Majos C. Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI. AJNR Am J Neuroradiol 2020;41:1816-24. [Crossref] [PubMed]
18. Pons-Escoda A, García-Ruíz A, Naval-Baudin P, Grussu F, Viveros M, Vidal N, Bruna J, Plans G, Cos M, Perez-Lopez R, Majós C. Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics. AJNR Am J Neuroradiol 2022;43:1567-74. [Crossref] [PubMed]
19. Garcia-Ruiz A, Pons-Escoda A, Grussu F, Naval-Baudin P, Monreal-Aguero C, Hermann G, Karunamuni R, Ligero M, Lopez-Rueda A, Oleaga L, Berbís MÁ, Cabrera-Zubizarreta A, Martin-Noguerol T, Luna A, Seibert TM, Majos C, Perez-Lopez R. An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI. Cell Rep Med 2024;5:101464. [Crossref] [PubMed]
20. Benner T, Heiland S, Erb G, Forsting M, Sartor K. Accuracy of gamma-variate fits to concentration-time curves from dynamic susceptibility-contrast enhanced MRI: influence of time resolution, maximal signal drop and signal-to-noise. Magn Reson Imaging 1997;15:307-17. [Crossref] [PubMed]
21. Paulson ES, Schmainda KM. Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 2008;249:601-13. [Crossref] [PubMed]
22. Sanvito F, Raymond C, Cho NS, Yao J, Hagiwara A, Orpilla J, Liau LM, Everson RG, Nghiemphu PL, Lai A, Prins R, Salamon N, Cloughesy TF, Ellingson BM. Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI. Eur Radiol 2024;34:3087-101. [Crossref] [PubMed]
23. Ma H, Wang Z, Xu K, Shao Z, Yang C, Xu P, Liu X, Hu C, Lu X, Rong Y. Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery. Exp Ther Med 2017;13:2691-8. [Crossref] [PubMed]
24. Bjornerud A, Sorensen AG, Mouridsen K, Emblem KE. T1- and T2*-dominant extravasation correction in DSC-MRI: part I--theoretical considerations and implications for assessment of tumor hemodynamic properties. J Cereb Blood Flow Metab 2011;31:2041-53. [Crossref] [PubMed]
25. Emblem KE, Bjornerud A, Mouridsen K, Borra RJ, Batchelor TT, Jain RK, Sorensen AG T. (1)- and T(2)(*)-dominant extravasation correction in DSC-MRI: part II-predicting patient outcome after a single dose of cediranib in recurrent glioblastoma patients. J Cereb Blood Flow Metab 2011;31:2054-64. [Crossref] [PubMed]
26. Hu LS, Baxter LC, Pinnaduwage DS, Paine TL, Karis JP, Feuerstein BG, Schmainda KM, Dueck AC, Debbins J, Smith KA, Nakaji P, Eschbacher JM, Coons SW, Heiserman JE. Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas. AJNR Am J Neuroradiol 2010;31:40-8. [Crossref] [PubMed]
27. Kelm ZS, Korfiatis PD, Lingineni RK, Daniels JR, Buckner JC, Lachance DH, Parney IF, Carter RE, Erickson BJ. Variability and accuracy of different software packages for dynamic susceptibility contrast magnetic resonance imaging for distinguishing glioblastoma progression from pseudoprogression. J Med Imaging (Bellingham) 2015;2:026001. [Crossref] [PubMed]
28. Schmainda KM, Prah MA, Hu LS, Quarles CC, Semmineh N, Rand SD, Connelly JM, Anderies B, Zhou Y, Liu Y, Logan B, Stokes A, Baird G, Boxerman JL. Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low-Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors. AJNR Am J Neuroradiol 2019;40:626-33. [Crossref] [PubMed]
29. Bammer R, Amukotuwa SA. Dynamic Susceptibility Contrast Perfusion, Part 2: Deployment With and Without Contrast Leakage Present. Magn Reson Imaging Clin N Am 2024;32:25-45. [Crossref] [PubMed]
30. Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Grussu F, Fernandez JJS, Simo AC, Sarro NV, Fernandez-Coello A, Bruna J, Cos M, Perez-Lopez R, Majos C. Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis. Eur Radiol 2022;32:3705-15. [Crossref] [PubMed]
31. Cindil E, Sendur HN, Cerit MN, Erdogan N, Celebi F, Dag N, Celtikci E, Inan A, Oner Y, Tali T. Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI. Acad Radiol 2022;29:S52-62. [Crossref] [PubMed]
32. Quarles CC, Gochberg DF, Gore JC, Yankeelov TE. A theoretical framework to model DSC-MRI data acquired in the presence of contrast agent extravasation. Phys Med Biol 2009;54:5749-66. [Crossref] [PubMed]
33. Semmineh NB, Xu J, Skinner JT, Xie J, Li H, Ayers G, Quarles CC. Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE). Magn Reson Med 2015;74:772-84. [Crossref] [PubMed]
34. Schmiedeskamp H, Andre JB, Straka M, Christen T, Nagpal S, Recht L, Thomas RP, Zaharchuk G, Bammer R. Simultaneous perfusion and permeability measurements using combined spin- and gradient-echo MRI. J Cereb Blood Flow Metab 2013;33:732-43. [Crossref] [PubMed]
35. Cho NS, Hagiwara A, Sanvito F, Ellingson BM. A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors. Neuroradiology 2023;65:559-68. [Crossref] [PubMed]
36. van der Weijden CWJ, Gutmann IW, Somsen JF, Luurtsema G, van der Goot T, Arzanforoosh F, Kramer MCA, Buunk AM, de Vries EFJ, Rauscher A, van der Hoorn A. DSC Perfusion MRI Artefact Reduction Strategies: A Short Overview for Clinicians and Scientific Applications. J Clin Med 2025;14:4776. [Crossref] [PubMed]