Diffusion kurtosis imaging differs between primary central nervous system lymphoma and high-grade glioma and is correlated with the diverse nuclear-to-cytoplasmic ratio: a histopathologic, biopsy-based study

Diffusion kurtosis imaging-based habitat analysis identifies high-risk molecular subtypes and heterogeneity matching in diffuse gliomas

OBJECTIVE

High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS

We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS

The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION

The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

Diffusion kurtosis imaging with multiple quantitative parameters for predicting microsatellite instability status of endometrial carcinoma

PURPOSE

To explore the value of Diffusion kurtosis imaging (DKI) with multiple quantitative parameters in predicting microsatellite instability (MSI) status in endometrial carcinoma (EC).

METHODS

Data of 38 patients with EC were retrospectively analyzed, including 12 MSI and 26 microsatellite stability (MSS). All patients underwent preoperative 1.5T MR examination. The quantitative values of the DKI sequence in the tumor parenchyma of the two groups, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), fractional anisotropy of kurtosis (FAk), mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr) were measured by two observers, respectively.

RESULTS

The MK, Ka, Kr, FA, FAk, MD, Da, and Dr values of the MSI group were 1.074 ± 0.162, 1.253 ± 0.229, 0.886 ± 0.205, 0.207 ± 0.041, 0.397 ± 0.129, 0.890 ± 0.158 μm2/ms, 1.083 ± 0.218 μm2/ms, and 0.793 ± 0.133 μm2/ms, and 0.956 (0.889,1.002), 1.048 ± 0.211, 0.831 ± 0.099, 0.188 ± 0.061, 0.334 (0.241,0.410), 1.043 ± 0.217 μm2/ms, 1.235 ± 0.229 μm2/ms, and 0.946 ± 0.215 μm2/ms in the MSS group. The MK and Ka values of the MSI group were higher than those of the MSS group (P<0.05), while the MD and Dr values were lower than those of the MSS group (P<0.05). The AUC of MK, Ka, MD, and Dr values in predicting MSI status of EC was 0.763, 0.729, 0.731, 0.748, respectively. The sensitivity was 58.3%, 50.0%, 65.4%, 61.5%, and the specificity was 96.2%, 92.3%, 75.0%, 83.3%, respectively.

CONCLUSION

DKI can provide multiple quantitative parameters for predicting the MSI status of EC, and assist gynecologist to optimize the treatment plan for the patients.

DKI can distinguish high-grade gliomas from IDH1-mutant low-grade gliomas and correlate with their different nuclear-to-cytoplasm ratio: a localized biopsy-based study

OBJECTIVES

To explore whether differences in diffusional kurtosis imaging (DKI) between therapy-naïve high-grade gliomas (HGGs) and low-grade gliomas (LGGs) are related to the cellularity and/or the nuclear-to-cytoplasmic (N/C) ratio.

METHODS

We analyzed 44 and 40 diffuse glioma samples that were pathologically confirmed as HGGs and IDH1-mutant LGGs, respectively. The DKI parameters included kurtosis metrics (mean kurtosis [MK], axial kurtosis [K//], and radial kurtosis [K⊥]), and the diffusional metrics (fractional anisotropy [FA], mean diffusion [MD], axial diffusion [λ//], and radial diffusion [λ⊥]). The cellularity and the N/C ratio were compared within LGGs and HGGs using the Mann-Whitney U test (significant level, p < 0.007 [0.05/7]); Bonferroni correction). Spearman's correlation analysis was used to calculate the correlation coefficients among DKI metrics, cellularity, and the N/C ratio at a significant level of p = 0.05.

RESULTS

Excluding FA, all DKI metrics showed significant differences between HGGs and LGGs (all p ≤ 0.001). The N/C ratio of HGGs was significantly higher than that of LGGs; however, differences in cellularity were not significant between the two glioma groups (p = 0.525). Similarly, excluding FA, all DKI metrics were significantly correlated with the N/C ratio in LGGs, with correlation coefficients of - 0.365 (MD), - 0.313 (λ//), - 0.376 (λ⊥), 0.859 (MK), 0.772 (K//), and 0.842 (K//). There was a non-significant correlation between any DKI parameters and the cellularity in LGGs. Additionally, the cellularity and N/C ratios in HGGs did not correlate with any DKI metrics.

CONCLUSIONS

DKI differentiate LGGs from HGGs associated with their different N/C ratios.

CLINICAL RELEVANCE STATEMENT

This study shows that DKI differentiates LGGs from HGGs may correlated with their different N/C ratios, this could provide a possible histopathological mechanism about why DKI can DKI differentiate LGGs from HGGs.

KEY POINTS

• Excluding FA, all DKI metrics showed a significant difference between high-grade gliomas and IDH1-mutant low-grade gliomas. • The nuclear-to-cytoplasm ratios in high-grade gliomas were significantly more extensive than that in IDH1-mutant low-grade gliomas, but not the cellularity. • Significant associations were seen between DKI measures and the N/C ratio; a non-significant correlation was noted between any DKI metric and cellularity in glioma specimens.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study

OBJECTIVES

To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas.

METHODS

Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000 s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades.

RESULTS

Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r =  - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006).

CONCLUSIONS

IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas.

KEY POINTS

• Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study

Background

To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas.

Methods

Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC₁₅₀₀, α₁₅₀₀ fitted by 15 b-values (0-1,500 sec/mm²)and DDC₅₀₀₀ and α₅₀₀₀ fitted by 22 b-values (0-5,000 sec/mm²) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters.

Results

MDWI-derived α₁₅₀₀ negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC₁₅₀₀ and DDC₅₀₀₀ negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α₁₅₀₀(r=-0.485; P=0.005) and α₅₀₀₀(r=-0.395; P=0.025).

Conclusions

SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

High-Resolution Diffusion-Weighted Imaging of C6 Glioma on a 7T BioSpec MRI Scanner: Correlation of Tumor Cellularity and Nuclear-to-Cytoplasmic Ratio with Apparent Diffusion Coefficient

RATIONALE AND OBJECTIVES

To determine the association of the apparent diffusion coefficient (ADC) with quantitative cellularity and the nuclear-to-cytoplasmic ratio in C6 glioma.

MATERIALS AND METHODS

Animal models bearing C6 gliomas underwent MR scans with T1 rapid acquisition with relaxation enhancement (RARE), T2 RARE, and high-resolution diffusion-weighted imaging sequences. For each model, three consecutive sections were used to draw regions of interest (ROIs) and measure ADC values; the middle section was localized in the plane with the maximal solid tumor area. The minimal, mean, and maximal ADC values were recorded for each ROI. GFAP-immunostained sections coregistered with ADC measurements were used to calculate tumor cellularity and the nuclear-to-cytoplasmic (N/C) ratio. Spearman's correlation was used to assess the relationship between ADC values and quantitative tumor cellularity as well as N/C ratios with a significance level of p < 0.05.

RESULTS

Thirty-three sections from 11 glioma-bearing rats were analyzed. The median values of the minimal, mean, and maximal ADC were 0.443 × 10^-3, 0.744 × 10^-3, and 1.140 × 10^-3 mm²/s, respectively. The median cellularity and N/C ratio were 2151.234 per 0.025 mm² and 0.857, respectively. The minimal, mean, and maximal ADCs were all significantly associated with cellularity, with correlation coefficients of -0.712 (p < 0.001), -0.631 (p < 0.001), and -0.460 (p = 0.007), respectively. The minimal and mean ADC had significant negative relationships with the N/C ratio, with correlation coefficients of -0.565 (p =  0.001) and -0.426 (p = 0.013), respectively.

CONCLUSION

The minimal ADC correlated well with cellularity and N/C ratios in C6 glioma and may be used as a biomarker of these two pathological features.

Diffusion kurtosis imaging assessment of the response to radiotherapy in a VX2 bone tumor model: an animal study

BACKGROUND

Neoadjuvant radiotherapy plays a vital role in the treatment of malignant bone tumors, and non-invasive imaging methods are needed to evaluate the response to treatment.

PURPOSE

To assess the value of diffusion kurtosis imaging (DKI) for monitoring early response to radiotherapy in malignant bone tumors.

MATERIAL AND METHODS

Treatment response was evaluated in a rabbit VX2 bone tumor model (n = 35) using magnetic resonance imaging (MRI), DKI, and histopathologic examinations. Subjects were divided into three groups: pre-treatment, post-treatment, and control groups. The post-treatment group was subclassified into good response and poor response groups according to the results of histopathologic examination. Apparent diffusion coefficient (ADC) and DKI parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were recorded. The relationship between ADC, DKI parameters, and histopathologic changes after radiotherapy was determined using Pearson's correlation coefficient. The diagnostic performance of these parameters was assessed using receiver operating characteristic analysis.

RESULTS

MD in the good response group was higher after treatment than before treatment (P < 0.001) and higher than that in the poor response group (P = 0.009). MD was highly correlated with tumor cell density and apoptosis rate (r = -0.771, P < 0.001 and r = 0.625, P < 0.001, respectively). MD was superior to other parameters for determining the curative effect of radiotherapy, with a sensitivity of 75.0%, specificity of 100.0%, and area under the curve of 0.917 (P < 0.001).

CONCLUSION

The correlations between MD, tumor cell density, and apoptosis suggest that MD could be useful for assessing the early response to radiotherapy in rabbit VX2 malignant bone tumors.

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D^∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D^∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

Comparative study of evaluating the microcirculatory function status of primary small HCC between the CE (DCE-MRI) and Non-CE (IVIM-DWI) MR Perfusion Imaging

PURPOSE

To compare the difference of evaluating the microcirculatory function status of primary small HCC between DCE-MRI with two-compartmental pharmacokinetic model and IVIM-DWI.

METHODS

27 patients (22 men, 5 women; mean age, 49 years; range 36-65 years) with primary single sHCC who underwent IVIM-DWI and DCE-MRI before the operation were included in this retrospective study. The MR perfusion parameters are Ktrans, Ve, Kep, D, D* and f. Pathological results include pathological grade (low grade ≤ II, high grade > II) and MVD. The perfusion parameters and pathological results of sHCC were analyzed and compared in their relevance, sensitivity and specificity. Statistical methods included Spearman and ROC curve analysis.

RESULTS

The perfusion parameters (Ktrans, Kep, D*, f) were significantly positive correlated (r = 0.892, 0.808, 0.589 and 0.543, P = 0.000, 0.000, 0.001 and 0.003 with MVD of sHCC. The parameter Ve and D values were negatively correlated (r = - 0.454 and - 0.399, P = 0.017 and 0.039, respectively) with the pathological grade. Regarding the evaluation MVD of sHCC, the evaluation of the sensitivity and specificity performance was present in descending order: Ktrans > Kep > PF > D*. In the evaluation pathological grade of sHCC, the sensitivity and specificity were better by parameters D than Ve.

CONCLUSION

DCE-MRI is better than IVIM-DWI for evaluation microcirculation functional status of sHCC. But for evaluating the pathological grade, IVIM-DWI is better than DCE-MRI. Combination of the two imaging techniques may provide more comprehensive evaluation in microcirculation functional status of the sHCC.

Differentiating Glioblastoma from Primary Central Nervous System Lymphoma: The Value of Shaping and Nonenhancing Peritumoral Hyperintense Gyral Lesion on FLAIR Imaging

BACKGROUND

This study describes a distinct magnetic resonance imaging (MRI) feature, placing emphasis on fluid-attenuation inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1C) images for the preoperative differentiation of glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL).

METHODS

The preoperative MRI findings of 116 pathologically confirmed glioblastoma (n = 72) and PCNSL (n = 44) were retrospectively reviewed. Two neuroimaging specialists analyzed the MRIs, and image analysis was focused on the presence or absence of a shaping and nonenhancing peritumoral hyperintense gyral lesion on FLAIR imaging (SNEPGF, i.e., hyperintense lesion in a shaping and nonenhancing peritumoral gyral area on FLAIR imaging). The gyral area adjacent to and within 3 cm of the enhanced tumor was defined as the peritumoral gyrus region. The FLAIR hyperintensity lesion were termed as the signal intensity ratio ≥30% compared with contralateral normal gray matter. Then, the differential diagnostic efficacy of SNEFPG sign for GBM and PCNSL was analyzed.

RESULTS

The SNEPGF sign was found in 33 GBM cases (33 of 72, 45.8%), and the FLAIR signal intensity and apparent diffusion coefficient value of these area were lower than the peritumoral edema area (P < 0.0001). In 44 PCNSL cases, no SNEPGF sign was found. A slightly higher FLAIR signal intensity was seen in 9 PCNSLs, but uniform and marked enhancement was seen in these areas. The sensitivity, specificity, positive predictive value, and negative predictive value of the differential diagnosis of GBM and PCNSL with SNEPGF sign were 45.8%, 100%, 100%, and 53.0%, respectively.

CONCLUSIONS

The SNEPGF sign is effective in identifying GBM from PCNSL, especially with high specificity.

Microstructural and Cerebral Blood Flow Abnormalities in Subjective Cognitive Decline Plus: Diffusional Kurtosis Imaging and Three-Dimensional Arterial Spin Labeling Study

Objective: To explore microstructural and cerebral blood flow (CBF) abnormalities in individuals with subjective cognitive decline plus (SCD plus) using diffusional kurtosis imaging (DKI) and three-dimensional (3D) arterial spin labeling (ASL). Methods: Twenty-seven patients with SCD plus, 31 patients with amnestic mild cognitive impairment (aMCI), and 33 elderly controls (ECs) were recruited and underwent DKI and 3D ASL using a GE 3.0-T MRI. Mean kurtosis (MK), fractional anisotropy (FA), mean diffusivity (MD), and CBF values were acquired from 24 regions of interest (ROIs) in the brain, including the bilateral hippocampal (Hip) subregions (head, body, and tail), posterior cingulate cortex (PCC), precuneus, dorsal thalamus subregions (anterior nucleus, ventrolateral nucleus, and medial nucleus), lenticular nucleus, caput nuclei caudati, white matter (WM) of the frontal lobe, and WM of the occipital lobe. Pearson's correlation analysis was performed to assess the relationships among the DKI-derived parameters, CBF values, and key neuropsychological tests for SCD plus. Results: Compared with ECs, participants with SCD plus showed a significant decline in MK and CBF values, mainly in the Hip head and PCC, and participants with aMCI exhibited more significant abnormalities in the MK and CBF values than individuals with ECs and SCD plus in multiple regions. Combined MK values showed better discrimination between patients with SCD plus and ECs than that obtained using CBF levels, with areas under the receiver operating characteristic (ROC) curve (AUC) of 0.874 and 0.837, respectively. Similarly, the AUC in discriminating SCD plus from aMCI patients obtained using combined MK values was 0.823, which was also higher than the combined AUC of 0.779 obtained using CBF values. Moreover, MK levels in the left Hip (h) and left PCC positively correlated with the auditory verbal learning test-delayed recall (AVLT-DR) score in participants with SCD plus. By contrast, only the CBF value in the left Hip head positively correlated with the AVLT-DR score. Conclusions: Our results provide new evidence of microstructural and CBF changes in patients with SCD plus. MK may be used as an early potential neuroimaging biomarker and may be a more sensitive DKI parameter than CBF at the very early stage of Alzheimer's disease (AD).