Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading

Abstract

Purpose An accurate differentiation of brain glioma grade constitutes an important clinical issue. Powerful non-invasive approach based on diffusion MRI has already demonstrated its feasibility in glioma grade stratification. However, the conventional diffusion tensor (DTI) and kurtosis imaging (DKI) demonstrated moderate sensitivity and performance in glioma grading. In the present work, we apply generalised DKI (gDKI) approach in order to assess its diagnostic accuracy and potential application in glioma grading. Methods Diffusion scalar metrics were obtained from 50 patients with different glioma grades confirmed by histological tests following biopsy or surgery. All patients were divided into two groups with low- and high-grade gliomas as grade II versus grades III and IV, respectively. For a comparison, trained radiologists segmented the brain tissue into three regions with solid tumour, oedema, and normal appearing white matter. For each region, we estimated the conventional and gDKI metrics including DTI maps. Results We found high correlations between DKI and gDKI metrics in high-grade glioma. Further, gDKI metrics enabled introduction of a complementary measure for glioma differentiation based on correlations between the conventional and generalised approaches. Both conventional and generalised DKI metrics showed quantitative maps of tumour heterogeneity and oedema behaviour. gDKI approach demonstrated largely similar sensitivity and specificity in low-high glioma differentiation as in the case of conventional DKI method. Conclusion The generalised diffusion kurtosis imaging enables differentiation of low- and high-grade gliomas at the same level as the conventional DKI. Additionally, gDKI exhibited higher sensitivity to tumour heterogeneity and tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour differentiation.

Differentiation of microinfiltration and simple-edema areas in VX2 bone tumors by diffusion kurtosis imaging in animal experiments: a preliminary study

“…New MRI techniques, such as DKI, are increasingly being used in the clinic. Many studies have been conducted using DKI in the brain (18), liver (19), and prostate (20), which have confirmed its clinical application value and research prospects. DWI is a conventional single-index model, whose theoretical basis is that the diffusion of water molecules in the human body follows a Gaussian distribution, and the ADC value obtained is affected by the b-value.…”

Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion‐weighted MRI models

“…22,33 Diffusion-weighted imaging has been demonstrated to be beneficial in grading gliomas, which was consistent with a number of earlier investigations. 34,35 However, most studies only utilized one or two advanced diffusion models and categorized gliomas into high and low grades. Moreover, the results were less consistent in studies that performed multiple comparisons of grades II-IV.…”

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

“…58 Some studies have reported better diagnostic performance of DKI than DTI in classifying different grades of gliomas. 59,60 Similarly, Wu et al 33 have shown higher accuracy for DKI-derived metrics than DTI metrics in differentiating TP (n = 24) from PsP (n = 16) in high-grade gliomas (Table 1). Another valuable technique known as intravoxel incoherent motion (IVIM) has also shown encouraging findings in the identification of PsP in GBM patients.…”

“…This in turn leads to higher non‐Gaussianity and increased MK 58 . Some studies have reported better diagnostic performance of DKI than DTI in classifying different grades of gliomas 59,60 . Similarly, Wu et al 33 have shown higher accuracy for DKI‐derived metrics than DTI metrics in differentiating TP (n = 24) from PsP (n = 16) in high‐grade gliomas (Table 1).…”