Comparison of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging and Arterial Spin Labeling MR Imaging in Gliomas

Abstract

Gliomas grading is important for treatment plan; we aimed to investigate the application of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) in gliomas grading, by comparing with the three-dimensional pseudocontinuous arterial spin labeling (3D pCASL). 24 patients (13 high grade gliomas and 11 low grade gliomas) underwent IVIM DWI and 3D pCASL imaging before operation; maps of fast diffusion coefficient (D ∗), slow diffusion coefficient (D), fractional perfusion-related volume (f), and apparent diffusion coefficient (ADC) as well as cerebral blood flow (CBF) were calculated and then coregistered to generate the corresponding parameter values. We found CBF and D ∗ were higher in the high grade gliomas, whereas ADC, D, and f were lower (all P < 0.05). In differentiating the high from low grade gliomas, the maximum areas under the curves (AUC) of D ∗, CBF, and ADC were 0.857, 0.85, and 0.902, respectively. CBF was negatively correlated with f in tumor (r = −0.619, P = 0.001). ADC was positively correlated with D in both tumor and white matter (r = 0.887, P = 0.000 and r = 0.824, P = 0.000, resp.). There was no correlation between CBF and D ∗ in both tumor and white matter (P > 0.05). IVIM DWI showed more efficiency than 3D pCASL but less validity than conventional DWI in differentiating the high from low grade gliomas.

Radiomics strategy for glioma grading using texture features from multiparametric MRI

“…31 In addition, the grading performance using histogram features and the mean values derived from all the 10 modality or parametric maps were also acceptable, and better than the results of previous studies. 14,32 This may be explained by the combined force of the features from multiparametric MRI (rather than parameters from singlemodality MRI) with that of the SVM classifier (rather than a cutoff value of univariate analysis). Furthermore, the usability of histogram analysis is much easier to adopt and understand for clinical use than texture feature analysis.…”

“…Previous studies indicated that some histogram parameters, including the mean, minimum, maximum intensity value of a VOI or ROI, were effective in differentiating glioma grades. [12][13][14][15]32 Therefore, as a comparison group, nine histogram parameters were also extracted for each VOI (Supplementary Table 1).…”

“…8,9 Recently, advanced MRI techniques, including diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) imaging, also showed great potential in glioma grading by noninvasively assessing the cell proliferation and angiogenesis in a heterogeneous microenvironment. [10][11][12][13][14][15] However, most previous studies using these techniques were largely focused on single modality or comparisons among single modality. Simple parameters such as the mean, minimal, or maximal values were commonly used in these studies and could not reflect further phenotype information.…”

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

“…Concerning the used acquisition parameters, the amount and distribution of b values varied greatly. For example, IVIM studies in the glioma grading have used different b-value distributions and obtained different trend values for IVIM parameters [22,[37][38][39][40]. Specifically, using 13 b values (0-1000 s/mm 2 ), Togao and his colleagues found significantly lower D and higher f in high grade glioma (HGG) than in low grade glioma (LGG), while no statistically difference of D* in two groups [37].…”

Bench to bedside molecular functional imaging in translational cancer medicine: to image or to imagine?

“…118,119 By calculating various diffusion-and perfusion-related parameters (diffusion heterogeneity index, intravoxel distribution of diffusion coefficient, tissue diffusivity, pseudo-diffusivity, and perfusion fraction), these techniques provide information regarding tissue diffusivity and microcapillary perfusion. 120,121 Promising results have been seen regarding cancer detection and staging, monitoring of treatment response, and detecting nodal metastases. The various clinical settings tested include head and neck cancers, gliomas, and characterising hepatic and prostate lesions.…”