Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas

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 II grade versus III and IV grades, 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 tissue contrast between tumour and healthy tissue and, thus, may contribute as a complementary source of information on tumour heterogeneity.

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Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models

BMC Medical Imaging ◽

10.1186/s12880-020-00524-w ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Jiaji Mao ◽

Weike Zeng ◽

Qinyuan Zhang ◽

Zehong Yang ◽

Xu Yan ◽

...

Keyword(s):

Brain Metastases ◽

Fractional Anisotropy ◽

Volume Fraction ◽

Mean Diffusivity ◽

Diffusion Kurtosis Imaging ◽

High Grade ◽

Diffusion Weighted Mri ◽

Diffusion Weighted ◽

High Grade Gliomas ◽

Solitary Brain Metastases

Abstract Background To compare the diagnostic performance of neurite orientation dispersion and density imaging (NODDI), mean apparent propagator magnetic resonance imaging (MAP-MRI), diffusion kurtosis imaging (DKI), diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs). Methods Patients with previously untreated, histopathologically confirmed HGGs (n = 20) or SBMs (n = 21) appearing as a solitary and contrast-enhancing lesion on structural MRI were prospectively recruited to undergo diffusion-weighted MRI. DWI data were obtained using a q-space Cartesian grid sampling procedure and were processed to generate parametric maps by fitting the NODDI, MAP-MRI, DKI, DTI and DWI models. The diffusion metrics of the contrast-enhancing tumor and peritumoral edema were measured. Differences in the diffusion metrics were compared between HGGs and SBMs, followed by receiver operating characteristic (ROC) analysis and the Hanley and McNeill test to determine their diagnostic performances. Results NODDI-based isotropic volume fraction (Viso) and orientation dispersion index (ODI); MAP-MRI-based mean-squared displacement (MSD) and q-space inverse variance (QIV); DKI-generated radial, mean diffusivity and fractional anisotropy (RDk, MDk and FAk); and DTI-generated radial, mean diffusivity and fractional anisotropy (RD, MD and FA) of the contrast-enhancing tumor were significantly different between HGGs and SBMs (p < 0.05). The best single discriminative parameters of each model were Viso, MSD, RDk and RD for NODDI, MAP-MRI, DKI and DTI, respectively. The AUC of Viso (0.871) was significantly higher than that of MSD (0.736), RDk (0.760) and RD (0.733) (p < 0.05). Conclusion NODDI outperforms MAP-MRI, DKI, DTI and DWI in differentiating between HGGs and SBMs. NODDI-based Viso has the highest performance.

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Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

Acta Radiologica ◽

10.1177/0284185119898656 ◽

2020 ◽

Vol 61 (9) ◽

pp. 1228-1239

Author(s):

Xiaodan Chen ◽

Lin Lin ◽

Jie Wu ◽

Guang Yang ◽

Tianjin Zhong ◽

...

Keyword(s):

Pearson Correlation ◽

Mean Diffusivity ◽

Labeling Index ◽

Histogram Analysis ◽

Diffusion Kurtosis Imaging ◽

Low Grade ◽

High Grade ◽

Ki 67 ◽

Diffusion Kurtosis ◽

Median Maximum

Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

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Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging

European Journal of Radiology ◽

10.1016/j.ejrad.2020.109445 ◽

2021 ◽

Vol 135 ◽

pp. 109445

Author(s):

Xiao-feng Wu ◽

Xiao Liang ◽

Xiao-chun Wang ◽

Jiang-bo Qin ◽

Lei Zhang ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

Diffusion Tensor ◽

High Grade Glioma ◽

Diffusion Kurtosis Imaging ◽

High Grade ◽

Glioma Recurrence ◽

Diffusion Kurtosis ◽

And Diffusion

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Diffusion Kurtosis Imaging—A Superior Approach to Assess Tumor–Stroma Ratio in Pancreatic Ductal Adenocarcinoma

Cancers ◽

10.3390/cancers12061656 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1656

Author(s):

Philipp Mayer ◽

Yixin Jiang ◽

Tristan A. Kuder ◽

Frank Bergmann ◽

Ekaterina Khristenko ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Diffusion Constant ◽

Tumor Stroma ◽

Random Motion ◽

Imaging Biomarker ◽

Ductal Adenocarcinoma ◽

Diffusion Kurtosis Imaging ◽

Non Invasive ◽

Diffusion Weighted ◽

Diffusion Kurtosis

Extensive desmoplastic stroma is a hallmark of pancreatic ductal adenocarcinoma (PDAC) and contributes to tumor progression and to the relative resistance of tumor cells towards (radio) chemotherapy. Thus, therapies that target the stroma are under intense investigation. To allow the stratification of patients who would profit from such therapies, non-invasive methods assessing the stroma content in relation to tumor mass are required. In the current prospective study, we investigated the usefulness of diffusion-weighted magnetic resonance imaging (DW-MRI), a radiologic method that measures the random motion of water molecules in tissue, in the assessment of PDAC lesions, and more specifically in the desmoplastic tumor stroma. We made use of a sophisticated DW-MRI approach, the so-called diffusion kurtosis imaging (DKI), which possesses potential advantages over conventional and widely used monoexponential diffusion-weighted imaging analysis (cDWI). We found that the diffusion constant D from DKI is highly negatively correlated with the percentage of tumor stroma, the latter determined by histology. D performed significantly better than the widely used apparent diffusion coefficient (ADC) from cDWI in distinguishing stroma-rich (>50% stroma percentage) from stroma-poor tumors (≤50% stroma percentage). Moreover, we could prove the potential of the diffusion constant D as a clinically useful imaging parameter for the differentiation of PDAC-lesions from non-neoplastic pancreatic parenchyma. Therefore, the diffusion constant D from DKI could represent a valuable non-invasive imaging biomarker for assessment of stroma content in PDAC, which is applicable for the clinical diagnostic of PDAC.

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