Comparative analysis of the diffusion kurtosis imaging and diffusion tensor imaging in grading gliomas, predicting tumour cell proliferation and IDH-1 gene mutation status

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics

BACKGROUND

Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status.

PURPOSE

To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG.

STUDY TYPE

Prospective.

POPULATION

Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients.

FIELD STRENGTH/SEQUENCE

3 T, multi-shell diffusion imaging.

ASSESSMENT

Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients.

STATISTICAL TESTS

Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant.

RESULTS

82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75).

DATA CONCLUSION

Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Diffusion MRI is valuable in brainstem glioma genotyping with quantitative measurements of white matter tracts

OBJECTIVES

To investigate the value of diffusion MRI (dMRI) in H3K27M genotyping of brainstem glioma (BSG).

METHODS

A primary cohort of BSG patients with dMRI data (b = 0, 1000 and 2000 s/mm2) and H3K27M mutation information were included. A total of 13 diffusion tensor and kurtosis imaging (DTI; DKI) metrics were calculated, then 17 whole-tumor histogram features and 29 along-tract white matter (WM) microstructural measurements were extracted from each metric and assessed within genotypes. After feature selection through univariate analysis and the least absolute shrinkage and selection operator method, multivariate logistic regression was used to build dMRI-derived genotyping models based on retained tumor and WM features separately and jointly. Model performances were tested using ROC curves and compared by the DeLong approach. A nomogram incorporating the best-performing dMRI model and clinical variables was generated by multivariate logistic regression and validated in an independent cohort of 27 BSG patients.

RESULTS

At total of 117 patients (80 H3K27M-mutant) were included in the primary cohort. In total, 29 tumor histogram features and 41 WM tract measurements were selected for subsequent genotyping model construction. Incorporating WM tract measurements significantly improved diagnostic performances (p < 0.05). The model incorporating tumor and WM features from both DKI and DTI metrics showed the best performance (AUC = 0.9311). The nomogram combining this dMRI model and clinical variables achieved AUCs of 0.9321 and 0.8951 in the primary and validation cohort respectively.

CONCLUSIONS

dMRI is valuable in BSG genotyping. Tumor diffusion histogram features are useful in genotyping, and WM tract measurements are more valuable in improving genotyping performance.

CLINICAL RELEVANCE STATEMENT

This study found that diffusion MRI is valuable in predicting H3K27M mutation in brainstem gliomas, which is helpful to realize the noninvasive detection of brainstem glioma genotypes and improve the diagnosis of brainstem glioma.

KEY POINTS

• Diffusion MRI has significant value in brainstem glioma H3K27M genotyping, and models with satisfactory performances were built. • Whole-tumor diffusion histogram features are useful in H3K27M genotyping, and quantitative measurements of white matter tracts are valuable as they have the potential to improve model performance. • The model combining the most discriminative diffusion MRI model and clinical variables can help make clinical decision.

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI)

BACKGROUND

Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential.

PURPOSE

To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI).

STUDY TYPE

Retrospective study.

SUBJECTS

70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)).

FIELD STRENGTH/SEQUENCE

3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values.

ASSESSMENT

Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed.

STATISTICAL TESTS

Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction.

RESULTS

Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%).

DATA CONCLUSION

Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prediction of overall survival in patients with high-grade glioma

OBJECTIVE

To evaluation of diffusion heterogeneity and vascular perfusion in tumor and peritumoral areas for prognostic prediction in high-grade glioma (HGG, WHO III/IV grade).

METHODS

Forty patients with HGG underwent diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and arterial spin labeling (ASL) MRI before operation. After normalization, the parameters were divided into diffusion heterogeneity parameters (rD, rMD, rMK, rKr, rKa) and vascular perfusion parameters (rD*, rF, rCBF). Univariate and multivariate Cox regression analyses were used to evaluate associations between overall survival (OS) and the above parameters, clinical factors, and IDH1 status. The Mann-Whitney test was used to evaluate differences in the parameters between different IDH1 states.

RESULTS

In the univariate Cox regression analysis, OS was significantly associated with tumor resection range, IDH1 status, tumor heterogeneity parameters (rD, rMD, rMK, rKr, rKa), and rCBF in tumor area(all p < 0.05). In addition, rD and rCBF measured in the peritumoral region were also predictors of poor OS (both p < 0.01). Multivariate Cox regression analysis indicated that rMK in the tumor area and rCBF in the peritumoral area (hazard ratio = 7.900 and 5.466, respectively, for each 0.1 increase in the normalized value) were independent predictors of OS.

CONCLUSION

The rMK of tumor area and rCBF of peritumoral area had independent predictive value for OS in patients with HGG.

ADVANCES IN KNOWLEDGE

This study explored useful imaging biomarkers from the diffusion heterogeneity and vascular perfusion of tumor and peritumoral areas in HGG, which is useful to help clinician to make precise therapeutic plans, and predict the prognostic for glioma patients.

An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients

Imaging is central to the clinical surveillance of brain tumors yet it provides limited insight into a tumor's underlying biology. Machine learning and other mathematical modeling approaches can leverage paired magnetic resonance images and image-localized tissue samples to predict almost any characteristic of a tumor. Image-based modeling takes advantage of the spatial resolution of routine clinical scans and can be applied to measure biological differences within a tumor, changes over time, as well as the variance between patients. This approach is non-invasive and circumvents the intrinsic challenges of inter- and intratumoral heterogeneity that have historically hindered the complete assessment of tumor biology and treatment responsiveness. It can also reveal tumor characteristics that may guide both surgical and medical decision-making in real-time. Here we describe a general framework for the acquisition of image-localized biopsies and the construction of spatiotemporal radiomics models, as well as case examples of how this approach may be used to address clinically relevant questions.

Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas

BACKGROUND

Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging.

PURPOSE

To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas.

MATERIAL AND METHODS

This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas.

RESULTS

Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05).

CONCLUSION

Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Peripheral Neuropathy in Diabetes: What Can MRI Do?

Diabetes peripheral neuropathy (DPN) is commonly asymptomatic in the early stage. However, once symptoms and obvious defects appear, recovery is not possible. Diagnosis of neuropathy is based on physical examinations, questionnaires, nerve conduction studies, skin biopsies, and so on. However, the diagnosis of DPN is still challenging, and early diagnosis and immediate intervention are very important for prevention of the development and progression of diabetic neuropathy. The advantages of MRI in the diagnosis of DPN are obvious: the peripheral nerve imaging is clear, the lesions can be found intuitively, and the quantitative evaluation of the lesions is the basis for the diagnosis, classification, and follow-up of DPN. With the development of magnetic resonance technology, more and more studies have been conducted on detection of DPN. This article reviews the research field of MRI in DPN.

Advances in molecular and imaging biomarkers in lower-grade gliomas

INTRODUCTION

Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up.

AREAS COVERED

This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up.

EXPERT OPINION

The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.

Structural alterations of spinocerebellar ataxias type 3: from pre-symptomatic to symptomatic stage

OBJECTIVES

To investigate and characterize the structural alterations of the brain in SCA3, and their correlations with the scale for the assessment and rating of ataxia (SARA) and normal brain ATXN3 expression.

METHODS

We performed multimodal analyses in 52 SCA3 (15 pre-symptomatic) and healthy controls (HCs) (n = 35) to assess the abnormalities of gray and white matter (WM) of the cerebrum, brainstem, and cerebellum via FreeSurfer, SUIT, and TBSS, and their associations with disease severity. Twenty SCA3 patients (5 pre- and 15 symptomatic) were followed for at least a year. Besides, we uncovered the normal pattern of brain ATXN3 spatial distribution.

RESULTS

Pre-symptomatic patients showed only WM damage, mainly in the cerebellar peduncles, compared to HCs. In the advanced stage, the WM damage followed a caudal-rostral pattern. Meanwhile, continuous nonlinear structure damage was characterized by brainstem volumetric reduction and relatively symmetric cerebellar and basal ganglia atrophy but spared the cerebral cortex, partially explained by the ATXN3 overexpression. The bilateral pallidum, brainstem, and cerebellar peduncles demonstrated a very large effect size. Besides, all these alterations were significantly correlated with SARA; the pons (r = -0.65) and superior cerebellar peduncle (r = -0.68) volume demonstrated a higher correlation than the cerebellum with SARA. The longitudinal study further uncovered progressive atrophy of pons in symptomatic SCA3.

CONCLUSIONS

Significant WM damage starts before the ataxia onset. The bilateral pallidum, brainstem, and cerebellar peduncles are the most vulnerable targets. The volume of pons appears to be the most promising imaging biomarker for a longitudinal study.

TRIAL REGISTRATION

ClinicalTrial ID: ChiCTR2100045857 ( http://www.chictr.org.cn/edit.aspx?pid=55652&htm=4 ) KEY POINTS: • Pre- SCA3 showed WM damage mainly in cerebellar peduncles. Continuous brain damage was characterized by brainstem, widespread, and relatively symmetric cerebellar and basal ganglia atrophy. • Volumetric abnormalities were most evident in the bilateral pallidum, brainstem, and cerebellar peduncles in SCA3. • The volume of pons might identify the disease progression longitudinally.

Evaluating the renal mild tubulointerstitial damage and renal function in IgAN patients: a comparative study based on diffusion kurtosis imaging and diffusion tensor imaging

OBJECTIVE

To compare the performance of 3.0 T magnetic resonance diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in evaluation of the degree of tubulointerstitial damage and renal function in Immunoglobulin A Nephropathy (IgAN) patients.

METHODS

Both DKI and DTI were performed in 40 IgAN patients and 17 healthy volunteers. IgAN patients were divided into two groups according to tubulointerstitial lesion score: Mild injury group, n = 24; Moderate-severe injury group, n = 16. DKI characteristic parameters [mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr)] and DTI parameters [fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr)] of renal cortex and medulla were measured and compared among different groups. Correlations between DKI, DTI parameters and clinicopathological characteristics were assessed. Diagnostic performance of DKI and DTI to evaluate tubulointerstitial damage of IgAN was compared.

RESULTS

Cortical MK, Kr, Da and parenchymal Ka significantly differed among three groups (P < 0.05). Cortical MK, Kr, Ka were negatively correlated with estimated glomerular filtration rate (eGFR) (MK: r = - 0.613; Kr: r = - 0.539; Ka: r = - 0.664) and positively correlated with tubulointerstitial lesion score (MK: r = 0.655; Kr: r = 0.577; Ka: r = 0.661) (all P < 0.001). Lower correlation coefficient was found among cortical FA, MD, Dr and eGFR, tubulointerstitial lesion score (all|r|< 0.350). The AUCs of DKI and DTI parameters for differentiating Mild injury group from control group were (cortical MK 0.822, cortical Ka 0.816; cortical FA 0.515, cortical MD 0.714) and for differentiating Mild injury group from Moderate-severe injury group were (cortical MK 0.813, cortical Ka 0.831; medulla FA 0.784, medulla MD 0.586).

CONCLUSION

Compared with DTI, DKI was more sensitive and accurate to probe the renal function and the tubulointerstitial damage of IgAN, especially the mild tubulointerstitial damage.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI

PURPOSE

To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity.

METHODS

Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters.

RESULTS

MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001).

CONCLUSION

MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Efficacy of MR diffusion kurtosis imaging for differentiating low-grade from high-grade glioma before surgery: A systematic review and meta-analysis

BACKGROUND

Accurate discrimination and diagnosis of low-grade glioma (LGG) and high-grade glioma (HGG) before surgery is clinically important because it affects the patient's outcome and guides the clinicians to select appropriate management. The aim of this study was to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) for differentiating LGG from HGG.

METHODS

A literature search of the PubMed, Web of Science, Cochrane Library and EMBASE databases was conducted up to December 15, 2020. Studies that evaluated the diagnostic performance of DKI for differentiating LGG from HGG were selected. Retrieved hits were evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary sensitivity and specificity were determined, and the data analysis was performed using Stata 14.0 and Review Manager 5.3.

RESULTS

Thirteen studies with 705 patients were included. The individual sensitivity and specificity of the 13 studies varied from 71% to 100% for sensitivity and 73-100% for specificity. The pooled sensitivity of DKI was 88% (95% confidence interval [CI], 83-91%), and the pooled specificity was 91% (95% CI, 86-95%). The area under the summary receiver operating characteristic curve was 0.93 (95% CI, 0.90-0.95). The pooled diagnostic odds ratio of DKI was 64.85 (95% CI 38.52-109.19). The levels of heterogeneity for sensitivity and specificity across the included studies were high (I² =66%) and mild (I² =47.04%), respectively. The multiple subgroup analyses were driven by DKI technique and study region.

CONCLUSIONS

DKI demonstrated a high diagnostic performance for differentiation of LGG from HGG.

A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders

INTRODUCTION

Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water.

AIM

To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment.

METHOD

A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons".

RESULTS

Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue.

CONCLUSION

Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.

Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status

OBJECTIVES

To evaluate the glioma grade, Ki-67 expression, and IDH-1 mutation status using mean apparent propagator (MAP) MRI.

METHODS

Forty enrolled glioma patients underwent structural and diffusion MRI. The diffusion metric values including fractional anisotropy (FA), mean diffusivity (MD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP) in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The TP/NAWM ratios of diffusion metric values were correlated with tumor grades, Ki-67, and IDH-1 mutation statuses, and the diagnostic performance was assessed.

RESULTS

QIV were significantly higher, whereas RTAP and RTOP were significantly lower in low-grade gliomas (LGGs) than those in high-grade gliomas (HGGs); QIV and MD were significantly higher, whereas RTAP and RTOP were significantly lower in lower-grade gliomas (grade II and III) than those in grade IV gliomas (p < 0.05 for all). RTAP performed best in grading gliomas. MSD, QIV, and MD were significantly higher, whereas RTAP, RTOP, RTPP, and FA were significantly lower in the IDH-1 mutant gliomas than those in the IDH-1 wild-type ones both for all gliomas and lower-grade gliomas (p < 0.05 for all). RTAP performed best in all gliomas, while QIV performed best in lower-grade gliomas. Additionally, RTAP, RTOP, and FA correlated positively, whereas MSD, QIV, and MD correlated negatively with Ki-67 (p < 0.05 for all).

CONCLUSIONS

MAP-MRI is a potent approach in evaluating the microstructural changes in gliomas with different grades, cellular proliferation, and IDH-1 mutation statuses.

KEY POINTS

• MAP-MRI, a newly developed diffusion technique, accurately reveals microstructure-related features in the complex white matter by recovering important microstructural tissue parameters. • MAP-MRI is a potent approach in evaluating the glioma grade, IDH-1 mutation status, and Ki-67 expression. • Compared with DTI, MAP-MRI seems to demonstrate higher diagnostic performance.

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.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm²) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R² value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article.

Machine Learning-Based Radiomics in Neuro-Oncology

In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Non-invasive assessments of quantitative radiological biomarkers mined from complex imaging characteristics across various applications are used to predict survival, discriminate between primary and secondary tumors, as well as between progression and pseudo-progression. In particular, the application of molecular phenotyping, envisioned in the field of radiogenomics, has gained popularity for both primary and secondary brain tumors. Although promising results have been obtained thus far, the lack of workflow standardization and availability of multicenter data remains challenging. The objective of this review is to provide an overview of novel applications of machine learning- and deep learning-based radiomics in primary and secondary brain tumors and their implications for future research in the field.

CHANGES IN THE MICROSTRUCTURE AND FUNCTION OF BRAIN TISSUE IN PD BY DIFFUSION KURTOSIS IMAGING

This study proposed to detect changes in brain microstructure in patients with Parkinson’s disease (PD) using diffusion kurtosis imaging (DKI) to quantitatively diagnose early-stage PD. Conventional magnetic resonance imaging and DKI scanning were performed in 24 patients with PD and in 12 age- and sex-matched healthy participants. Hoehn and Yahr (H–Y) stage and Unified Parkinson’s Disease Rating Scale-III (UPDRS-III) scores were obtained from both groups. The mean kurtosis (MK), axial kurtosis, and radial kurtosis of the bilateral substantia nigra on DKI were measured and compared between the two groups. The correlations between MK, H–Y stage, and UPDRS-III scores were determined. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of MK for PD in the substantia nigra. The MK value in the PD group was 0.971. The area under the ROC curve of the substantia nigra was 0.905; the sensitivity and specificity were 0.917 and 0.875, respectively, and the cutoff value was 1.046. The MK of the substantia nigra in the PD group had no significant correlation with the H–Y stages but was negatively correlated with the UPDRS-III scores ([Formula: see text]; [Formula: see text]). Our research identified DKI as a novel tool for the qualitative diagnosis of PD. The optimal MK value for PD diagnosis could be determined with ROC analysis.

Magnetic resonance quantification of non-Gaussian water diffusion in hepatic fibrosis staging: a pilot study of diffusion kurtosis imaging to identify reversible hepatic fibrosis

Background

This study aimed to evaluate the diagnostic accuracy of diffusion kurtosis imaging (DKI) in differentiating early hepatic fibrosis (HF) from normal liver and advanced HF in rabbits.

Methods

A total of 35 healthy New Zealand white rabbits were included in the study. A model of HF was established in 30 rabbits through subcutaneous injections of 50% carbon tetrachloride (CCl₄)/olive oil, while 5 rabbits received saline injections. The gradually increased doses of CCl₄ were 0.1, 0.2, and 0.3 mL/kg in weeks 1 to 3, weeks 4 to 6, and weeks 7 to 10, respectively. Two injections were given each week. Two rabbits in the experimental group died. All rabbits underwent DKI with three b values (0, 500, and 1,000 s/mm²) at week 5 (n=8), week 6 (n=9), week 7 (n=8), and week 10 (n=8). Approximately 2 liver lobes per rabbit were selected for histopathology. Mean diffusivity (MD) and mean kurtosis (MK) were calculated. Discrimination capacities of DKI parameters were analyzed and compared by receiver operating characteristic (ROC) analysis.

Results

The meta-analysis of histological data in viral hepatitis (METAVIR) scoring system was used to classify liver lobes into the control group (F0, n=0), early HF group (F1-F2, n=28), and advanced HF group (F3-F4, n=28). MD and MK values were significantly different among the three groups (all P<0.05). MD value was negatively correlated with increased fibrosis level, while MK value was positively correlated with increased fibrosis level (ρ=-0.540, 0.614; P<0.05). The area under ROC curves (AUCs) for MD and MK were 0.886 and 0.875, respectively, for characterization of F0 and F1-F2, and 0.975 and 0.957 for F0 and F3-F4. AUC for MK was 0.751 for characterization of F1-F2 and F3-F4. MD performed better than MK for characterization of F0 and F1-F2 as well as F0 and F3-F4. MK showed good differentiation performance between F1-F2 and F3-F4.

Conclusions

Our results showed that DKI contributed to discriminating reversible early HF from normal liver and advanced HF and as a result, showed promise for use in HF diagnosis.

Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma

OBJECTIVE

When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas.

METHODS

In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery. DKI scans were performed to generate DKI parameter maps of the solid tumor and peritumoral edema areas. In the solid tumor area, the kurtosis parameters showed the highest area under the curve (AUC), sensitivity, and specificity for distinguishing high- and low-grade gliomas (all P < 0.01).

RESULTS

In the peritumoral edema area, significant differences were found between groups with grade III and IV gliomas (P < 0.05). DKI parameters were found to correlate with clinical Ki-67 scores within the solid tumor area (MK: R2 = 0.288, P < 0.001; Kr: R2 = 0.270, P < 0.001; Ka: R2 = 0.274, P < 0.001; MD: R2 = 0.223, P < 0.001; FA: R2 = 0.098, P < 0.01). No significant correlations were found between Ki-67 and kurtosis parameters of peritumoral edema.

CONCLUSIONS

In this study, DKI showed potential utility for studying solid tumor and peritumoral edema of high grade gliomas.

Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression

OBJECTIVES

To compare the added value of diffusion kurtosis imaging (DKI) with the combination of dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression.

METHODS

Thirty-four patients with high-grade gliomas developing new and/or increasing enhanced lesions within six months after surgery and chemoradiotherapy were retrospectively analyzed. All patients were pathologically confirmed to have recurrent glioma (n = 22) or pseudoprogression (n = 12). The DKI and DSC MRI parameters were calculated based on the enhanced lesions on contrast-enhanced T1WI. ROC analysis was performed on significant variables to determine their diagnostic performance. Multivariate logistic regression was used to determine the best prediction model for discrimination.

RESULTS

The relative mean kurtosis (rMK), relative axial kurtosis (rKa), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) of glioma recurrence were higher than those of pseudoprogression (all, P < 0.05). The AUCs and diagnostic accuracy were 0.879 and 82.35% for rMK, 0.723 and 70.59% for rKa, 0.890 and 82.35% for rCBV, 0.765 and 73.53% for rMTT, respectively. A multivariate logistic regression model showed a significant contribution of rMK (P = 0.006) and rCBV (P = 0.009) as independent imaging classifiers for discrimination. The combined use of rMK and rCBV improved the AUC to 0.924 (P < 0.001) and the diagnostic accuracy to 88.24%.

CONCLUSION

DKI may be a valuable non-invasive tool in differentiating glioma recurrence from pseudoprogression, and its use in combination with DSC MRI can improve diagnostic performance in assessing treatment response compared with either technique alone.

Study of Diffusion Weighted Imaging Derived Diffusion Parameters as Biomarkers for the Microenvironment in Gliomas

Objectives

To explore the efficacy of diffusion weighted imaging (DWI)-derived metrics under different models as surrogate indicators for molecular biomarkers and tumor microenvironment in gliomas.

Methods

A retrospective study was performed for 41 patients with gliomas. The standard apparent diffusion coefficient (ADC_st) and ADC under ultra-high b values (ADC_uh) (b values: 2500 to 5000 s/mm²) were calculated based on monoexponential model. The fraction of fast diffusion (f), pseudo ADC (ADC_fast) and true ADC (ADC_slow) were calculated by bi-exponential model (b values: 0 to 2000 s/mm²). The apparent diffusional kurtosis (K_app) was derived from the simplified diffusion kurtosis imaging (DKI) model (b values: 200 to 3000 s/mm²). Potential correlations between DWI parameters and immunohistological indices (i.e. Aquaporin (AQP)1, AQP4, AQP9 and Ki-67) were investigated and DWI parameters were compared between high- and low-grade gliomas, and between tumor center and peritumor. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were calculated to determine the performance of independent or combined DWI parameters in grading gliomas.

Results

The ADC_slow and ADC_uh at tumor center showed a stronger correlation with Ki-67 than other DWI metrics. The ADC_st, ADC_slow and ADC_uh at tumor center presented correlations with AQP1 and AQP4 while AQP9 did not correlate with any DWI metric. K_app showed a correlation with Ki-67 while no significant correlation with AQPs. ADC_st (p < 0.001) and ADC_slow (p = 0.001) were significantly lower while the ADC_uh (p = 0.006) and K_app (p = 0.005) were significantly higher in the high-grade than in the low-grade gliomas. ADC_st, f, ADC_fast, ADC_slow, ADC_uh, K_app at the tumor center had significant differences with those in peritumor when the gliomas grade became high (p < 0.05). Involving ADC_uh and K_app simultaneously into an independent ADC_st model (AUC = 0.833) could further improve the grading performance (ADC_st+ADC_uh+K_app: AUC = 0.923).

Conclusion

Different DWI metrics fitted within different b-value ranges (low to ultra-high b values) have different efficacies as a surrogate indicator for molecular expression or microstructural complexity in gliomas. Further studies are needed to better explain the biological meanings of these DWI parameters in gliomas.

Brain Tumor Segmentation From Multi-Modal MR Images via Ensembling UNets

Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery planning and progression evaluation. Based on different biological properties, the glioma can be divided into three partially-overlapping regions of interest, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET). Recently, UNet has identified its effectiveness in automatically segmenting brain tumor from multi-modal magnetic resonance (MR) images. In this work, instead of network architecture, we focus on making use of prior knowledge (brain parcellation), training and testing strategy (joint 3D+2D), ensemble and post-processing to improve the brain tumor segmentation performance. We explore the accuracy of three UNets with different inputs, and then ensemble the corresponding three outputs, followed by post-processing to achieve the final segmentation. Similar to most existing works, the first UNet uses 3D patches of multi-modal MR images as the input. The second UNet uses brain parcellation as an additional input. And the third UNet is inputted by 2D slices of multi-modal MR images, brain parcellation, and probability maps of WT, TC, and ET obtained from the second UNet. Then, we sequentially unify the WT segmentation from the third UNet and the fused TC and ET segmentation from the first and the second UNets as the complete tumor segmentation. Finally, we adopt a post-processing strategy by labeling small ET as non-enhancing tumor to correct some false-positive ET segmentation. On one publicly-available challenge validation dataset (BraTS2018), the proposed segmentation pipeline yielded average Dice scores of 91.03/86.44/80.58% and average 95% Hausdorff distances of 3.76/6.73/2.51 mm for WT/TC/ET, exhibiting superior segmentation performance over other state-of-the-art methods. We then evaluated the proposed method on the BraTS2020 training data through five-fold cross validation, with similar performance having also been observed. The proposed method was finally evaluated on 10 in-house data, the effectiveness of which has been established qualitatively by professional radiologists.

The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations

PURPOSE

Diffusion-weighted imaging (DWI) may aid accurate tumor grading. Decreased diffusivity and increased diffusion heterogeneity measures have been observed in high-grade gliomas using the non-monoexponential models for DWI. However, DWI measures concerning tissue characteristics in terms of pathophysiological and structural changes are yet to be established. Thus, this study aims to investigate the relationship between the diffusion measurements and microstructural changes in the presence of high-grade gliomas using a three-dimensional Monte Carlo simulation with systematic changes of microstructural parameters.

METHODS

Water diffusion was simulated in a microenvironment along with changes associated with the presence of high-grade gliomas, including increases in cell density, nuclear volume, extracellular volume (VF_ex), and extracellular tortuosity (λ_ex), and changes in membrane permeability (P_mem). DWI signals were simulated using a pulsed gradient spin-echo sequence. The sequence parameters, including the maximum gradient strength and diffusion time, were set to be comparable to those of clinical scanners and advanced human MRI systems. The DWI signals were fitted using the gamma distribution and diffusional kurtosis models with b-values up to 6000 and 2500 s/mm², respectively.

RESULTS

The diffusivity measures (apparent diffusion coefficients (ADC), D_gamma of the gamma distribution model and D_app of the diffusional kurtosis model) decreased with increases in cell density and λ_ex, and a decrease in P_mem. These diffusivity measures increased with increases in nuclear volume and VF_ex. The diffusion heterogeneity measures (σ_gamma of the gamma distribution model and K_app of the diffusional kurtosis model) increased with increases in cell density or nuclear volume at the low P_mem, and a decrease in P_mem. Increased σ_gamma was also associated with an increase in VF_ex.

CONCLUSION

Among simulated microstructural changes, only increases in cell density at low P_mem or decreases in P_mem corresponded to both the decreased diffusivity and increased diffusion heterogeneity measures. The results suggest that increases in cell density at low P_mem or decreases in P_mem may be associated with the diffusion changes observed in high-grade gliomas.

Grading of Pediatric Intracranial Tumors: Are Intravoxel Incoherent Motion and Diffusional Kurtosis Imaging Superior to Conventional DWI?

BACKGROUND AND PURPOSE

An accurate evaluation of the World Health Organization grade is critical in pediatric intracranial tumors. Our aim was to explore the correlations between parameters derived from conventional DWI, intravoxel incoherent motion, and diffusional kurtosis imaging with histopathologic features to evaluate the accuracy of diffusion parameters for grading of pediatric intracranial tumors.

MATERIALS AND METHODS

Fifty-four pediatric patients with histologically proved intracranial tumors who underwent conventional DWI, intravoxel incoherent motion, and diffusional kurtosis imaging were recruited. The conventional DWI (ADC), intravoxel incoherent motion (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], perfusion fraction [f], diffusional kurtosis imaging [K], and diffusion coefficient [Dk]) parameters in the solid component of tumors were measured. The cellularity, Ki-67, and microvessel density were measured. These parameters were compared between the low- and high-grade pediatric intracranial tumors using the Mann-Whitney U test. Spearman correlations and receiver operating characteristic analysis were performed.

RESULTS

The ADC, D, and Dk values were lower, whereas the K value was higher in high-grade pediatric intracranial tumors than in low-grade tumors (all, P < .001). The K value showed positive correlations (r = 0.674-0.802; all, P < .05), while ADC, D, and Dk showed negative correlations with cellularity and Ki-67 (r = -0.548 to -0.740; all, P < .05). The areas under the curve of ADC_VOI, D_VOI, Dk_VOI, and K_VOI were 0.901, 0.894, 0.863, and 0.885, respectively, for differentiating high- from low-grade pediatric intracranial tumors. The area under the curve difference in grading pediatric intracranial tumors was not significant (all, P > .05).

CONCLUSIONS

Intravoxel incoherent motion- and diffusional kurtosis imaging-derived parameters have similar performance compared with conventional DWI in predicting pediatric intracranial tumor grade. The diffusion metrics may potentially reflect tumor cellularity and Ki-67 in pediatric intracranial tumors.

Magnetic Resonance Image of Neonatal Acute Bilirubin Encephalopathy: A Diffusion Kurtosis Imaging Study

Background and Objective: The abnormal T1-weighted imaging of MRI can be used to characterize neonatal acute bilirubin encephalopathy (ABE) in newborns, but has limited use in evaluating the severity and prognosis of ABE. This study aims to assess the value of diffusion kurtosis imaging (DKI) in detecting ABE and understanding its pathogenesis.

Method: Seventy-six newborns with hyperbilirubinemia were grouped into three groups (mild group, moderate group, and severe group) based on serum bilirubin levels. All the patients underwent conventional MRI and DKI serial, as well as 40 healthy full-term infants (control group). The regions of interest (ROIs) were the bilateral globus pallidus, dorsal thalamus, frontal lobe, auditory radiation, superior temporal gyrus, substantia nigra, hippocampus, putamen, and inferior olivary nucleus. The values of mean diffusivity (MD), axial kurtosis (AK), radial kurtosis (RK), and mean kurtosis (MK), and fractional anisotropy (FA), radial diffusivity (RD), and axis diffusivity (AD) of the ROIs were evaluated. All newborns were followed up and evaluated using the Denver Development Screening Test (DDST). According to the follow-up results, the patients were divided into the normal group, the suspicious abnormal group, and the abnormal group.

Result: Compared with the control group, significant differences were observed with the increased MK of dorsal thalamus, AD of globus pallidus in the moderate group, and increased RD, MK, AK, and RK value of globus pallidus, dorsal thalamus, auditory radiation, superior temporal gyrus, and hippocampus in the severe group. The peak value of total serum bilirubin was moderately correlated with the MK of globus pallidus, dorsal thalamus, and auditory radiation and was positively correlated with the other kurtosis value. Out of 76 patients, 40 finished the DDST, and only 9 patients showed an abnormality. Compared with the normal group, the AK value of inferior olivary nucleus showed significant differences (p < 0.05) in the suspicious abnormal group, and the MK of globus pallidus, temporal gyrus, and auditory radiation; RK of globus pallidus, dorsal thalamus, and auditory radiation; and MD of globus pallidus showed significant differences (p < 0.05) in the abnormal group.

Conclusion: DKI can reflect the subtle structural changes of neonatal ABE, and MK is a sensitive indicator to indicate the severity of brain damage.

Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas

PURPOSE

EphA2 is a key factor underlying invasive propensity of gliomas, and is associated with poor prognosis of tumors. We aimed to develop a radiomics-based imaging index for predicting EphA2 expression in diffuse gliomas, and further estimating its value for grading of tumors.

METHODS

A total of 182 patients with diffuse gliomas were included. All subjects underwent pre-operative MRI and post-operative pathological diagnosis. EphA2 expression of tumors was scored on pathological sections with immunohistochemical staining using monoclonal EphA2 antibody. MRI radiomics features were extracted from three-dimensional contrast-enhanced T1-weighted imaging and diffusion kurtosis imaging. Predictive models were constructed using machine learning-based radiomics features selection and three classifiers for predicting EphA2 expression and tumor grade. Features of best EphA2 expression model were subsequently used to construct another model of tumor grading. For each model, 146 cases (80%) were randomly picked as training and the rest 36 (20%) were testing cohorts. EphA2 expression was further correlated to the radiomics features in both grade models using Spearman's correlation.

RESULTS

Logistic regression model presented highest performance for predicting EphA2 expression (AUC: 0.836/0.724 in training/validation set). Tumor gradings model guided by features from EphA2 expression model demonstrated comparable performance (AUC: 0.930/0.983) to that constructed directly using imaging radiomics features (AUC: 0.960/0.977). Two radiomics features which included in both LR-grade models showed strong correlation (P < 0.05) with EphA2 expression.

CONCLUSION

The expression of EphA2 in gliomas could be predicted by radiomics features extracted from diffusion kurtosis MRI, which could also be used to assist tumor grading.

Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging

Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.

Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

PURPOSE

We aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients.

METHODS

This retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve.

RESULTS

The radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively.

CONCLUSION

Both the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved.

Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging

OBJECTIVE

To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas.

MATERIALS AND METHODS

Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MK_A), isotropic mean kurtosis (MK_I), total mean kurtosis (MK_T), MK_A/MK_T, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations.

RESULTS

FA, MD, MK_A, MK_I, MK_T, μFA, and MK_A/MK_T differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MK_I: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MK_T and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MK_I had the highest correlation coefficient (r_s = 0.618).

CONCLUSION

DIVIDE is a promising technique for glioma characterization and diagnosis.

KEY POINTS

• DIVIDE metrics MK_I is related to cell density heterogeneity while MK_A and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MK_I was larger than MK_A which indicates predominant cell density heterogeneity in gliomas. • MK_A and MK_I increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MK_A in relation to the cell density heterogeneity metric MK_I.

State of Radiomics in Glioblastoma

Radiomics is an emerging discipline that aims to make intelligent predictions and derive medical insights based on quantitative features extracted from medical images as a means to improve clinical diagnosis or outcome. Pertaining to glioblastoma, radiomics has provided powerful, noninvasive tools for gaining insights into pathogenesis and therapeutic responses. Radiomic studies have yielded meaningful biological understandings of imaging features that are often taken for granted in clinical medicine, including contrast enhancement on glioblastoma magnetic resonance imaging, the distance of a tumor from the subventricular zone, and the extent of mass effect. They have also laid the groundwork for noninvasive detection of mutations and epigenetic events that influence clinical outcomes such as isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT). In this article, we review advances in the field of glioblastoma radiomics as they pertain to prediction of IDH mutation status and MGMT promoter methylation status, as well as the development of novel, higher order radiomic parameters.

Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

Objectives

To explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).

Methods

40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.

Results

The mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.

Conclusions

Overall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading

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.

Diagnostic performance between MR amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction at 3 T

PURPOSE

Accurate glioma grading and IDH mutation status prediction are critically essential for individualized preoperative treatment decisions. This study aims to compare the diagnostic performance of magnetic resonance (MR) amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction.

METHOD

Fifty-one glioma patients without treatment were retrospectively included. APT-weighted (APTw) effect and DKI indices, including mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), and kurtosis FA (KFA) were obtained from APT and diffusion-weighted images, respectively. DKI indices in tumors were normalized to that in contralateral normal appearing white matter (CNAWM) and the APTw difference (ΔAPTw) between the two regions was calculated. Student's t-test, one-way ANOVA and ROC analyses were conducted.

RESULTS

Among the enrolled 51 patients, 13 had glioma-II, 17 had glioma-III and 21 had glioma-IV. 25 patients were diagnosed as IDH-mutant, and 26 as IDH-wild type. MD and MK differed significantly between glioma-IV and glioma II/III (P < 0.05), but not between glioma-II and glioma-III. FA and KFA showed no significant difference among the three groups (P > 0.05). IDH-mutant group exhibited significantly higher MD and lower FA, MK and ΔAPTw than IDH-wild type (P < 0.05), whereas the two groups showed comparable KFA values. In contrast, ΔAPTw differed significantly across tumor grades and IDH mutation status (P < 0.05), with consistently better discriminatory performance than DKI indices in glioma grading and IDH mutation status prediction.

CONCLUSIONS

APT imaging was superior to DKI in glioma grading and IDH mutation status prediction, benefiting accurate diagnoses and treatment decisions.

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

PURPOSE

To compare the diagnostic value of DKI and DTI in differentiation of high-grade glioma recurrence and pseudoprogression (PsP).

METHOD

Forty patients with high-grade gliomas who exhibited new enhancing lesions (24 high-grade glioma recurrence and 16 PsP) within 6 months after surgery followed by completion of chemoradiation therapy. All patients underwent repeat surgery or biopsy after routine MRI and DKI (including DTI). They were histologically classified into high-grade glioma recurrence and PsP groups. DKI (mean kurtosis [MK], axial kurtosis [Ka], and radial kurtosis [Kr]) and DTI (mean diffusivity [MD] and fractional anisotropy [FA]) parameters in the enhancing lesions and in the perilesional edema were measured. Inter-group differences between high-grade glioma recurrence and PsP were compared using the Mann-Whitney U test The receiver operating characteristic (ROC) curve was used to assess differential diagnostic efficacy of each parameter, and Z-scores were used to compare the value between DKI and DTI.

RESULTS

Relative MK (rMK) was significantly higher and relative MD (rMD) was significantly lower in the enhancing lesions of high-grade glioma recurrence compared to PsP (P <  0.001, P = 0.006, respectively). The AUC was 0.914 for rMK and 0.760 for rMD, and this difference was significant (P = 0.030). In the perilesional edema, rMK values were significantly higher and rMD values were significantly lower in high-grade glioma recurrence compared to PsP (P <  0.001, P =  0.005).

CONCLUSIONS

DKI had superior performance in differentiating high-grade glioma recurrence from PsP, and rMK appeared to be the best independent predictor.

Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease

OBJECTIVE

To test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change, and reproducibility of diffusion metrics.

METHODS

We included 50 patients with sporadic and 59 patients with genetically defined SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 patients with sporadic SVD with longitudinal high-frequency imaging (in total 459 MRIs). Intersite reproducibility was determined in 10 patients with CADASIL scanned back-to-back on 2 different 3T MRI scanners.

RESULTS

Metrics from DKI showed the strongest associations with processing speed performance (R ² up to 21%) and the largest added benefit on top of conventional SVD imaging markers in patients with sporadic SVD and patients with CADASIL with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible.

CONCLUSION

Multi-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available intersite dataset facilitates future studies.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability

OBJECTIVES

Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.

METHODS

Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.

RESULTS

The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81).

CONCLUSIONS

Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

KEY POINTS

• The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis

OBJECTIVE

To perform quantitative analysis on the efficacy of using relative cerebral blood flow (rCBF) in arterial spin labeling (ASL), relative cerebral blood volume (rCBV) in dynamic magnetic sensitivity contrast-enhanced magnetic resonance imaging (DSC-MRI), and mean kurtosis (MK) in diffusion kurtosis imaging (DKI) to grade cerebral gliomas.

METHODS

Literature regarding ASL, DSC-MRI, or DKI in cerebral gliomas grading in both English and Chinese were searched from PubMed, Embase, Web of Science, CBM, China National Knowledge Infrastructure (CNKI), and Wanfang Database as of 2019. A meta-analysis was performed to evaluate the efficacy of ASL, DSC-MRI, and DKI in the grading of cerebral gliomas.

RESULT

A total of 54 articles (11 in Chinese and 43 in English) were included. Three quantitative parameters in the grading of cerebral gliomas, rCBF in ASL, rCBV in DSC-MRI, and MK in DKI had the pooled sensitivity of 0.88 [95% CI (0.83,0.92)], 0.92 [95% CI (0.83,0.96)], 0.88 [95% CI (0.82,0.92)], and the pooled specificity of 0.91 [95% CI (0.84,0.94)], 0.81 [95% CI (0.73,0.88)], 0.86 [95% CI (0.78,0.91)] respectively. The pooled area under the curve (AUC) were 0.95 [95% CI (0.93,0.97)], 0.91 [95% CI (0.89,0.94)], 0.93 [95% CI (0.91,0.95)] respectively.

CONCLUSION

Quantitative parameters rCBF, rCBV and MK have high diagnostic accuracy for preoperative grading of cerebral gliomas.

Comparison of diffusion-weighted imaging mono-exponential mode with diffusion kurtosis imaging for predicting pathological grades of clear cell renal cell carcinoma

PURPOSE

To evaluate the role of diffusion kurtosis imaging (DKI¹) in the characterization of clear cell renal cell carcinoma (ccRCC²) compared with standard diffusion-weighted imaging (DWI³).

METHODS

89 patients with histologically proven ccRCC were evaluated by DKI and DWI on a 3-T scanner. All ccRCCs were classified as grade 1-4 according to the Fuhrman classification system. The apparent diffusion coefficient (ADC⁴), fractional anisotropy (FA⁵), mean diffusivity (MD⁶), mean kurtosis (MK⁷), axial kurtosis (Ka⁸) and radial kurtosis (Kr⁹) values were recorded. The differences in DWI and DKI parameters were evaluated by independent-sample t test and a receiver operating characteristic (ROC¹⁰) analysis was performed. The DeLong test was performed to compare the ROCs.

RESULTS

Compared to normal renal parenchyma, ADC and MD values of ccRCC decreased and MK, Ka, and Kr values increased (p < 0.05). ADC and MD values of ccRCC decreased with the increase in pathological grade, while MK, Ka, and Kr values were increased (p < 0.05). ADC could discriminate G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05) except for G1 vs G2 (p > 0.05). Ka and Kr could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G4, and G3 vs G4 (p < 0.05) except for G2 vs G3 (p > 0.05). MD and MK could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05). The AUC of MK was the highest. The DeLong test showed that there were significant differences regarding ROCs between ADC/MK, ADC/Ka, ADC/Kr in grading G1/G2, and ADC/MK, MK/Ka in grading G3/G4 (p < 0.05).

CONCLUSION

DKI was superior compared to the mono-exponential mode of DWI in grading ccRCC.

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis

Purpose

We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas.

Methods

A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords ‘glioma’ and ‘diffusional kurtosis’. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics.

Results

A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78–0.92) in high-/low-grade gliomas’ differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25–0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I² = 73.8%).

Conclusion

DKI shows good diagnostic accuracy in the differentiation of high- and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed.

Electronic supplementary material

The online version of this article (10.1007/s00234-020-02425-9) contains supplementary material, which is available to authorized users.

Diffusion kurtosis imaging combined with molecular markers as a comprehensive approach to predict overall survival in patients with gliomas

PURPOSE

The purpose of this study was to explore the usefulness of diffusion kurtosis imaging (DKI) and molecular markers in predicting the prognosis of glioma patients.

METHOD

Fifty-one patients with gliomas were examined by conventional MRI and DKI at 3.0 T before operation. The mean kurtosis (MK), mean diffusivity (MD), axial kurtosis (AK), and radial kurtosis (RK) values of tumors were measured and normalized to the contralateral normal-appearing white matter. The molecular markers of gliomas, including isocitrate dehydrogenase-1 (IDH1), α thalassemia/mental retardation syndrome x-linked (ATRX) and O6-methylguanine-DNA methyltransferase (MGMT), were immunohistochemically stained on the resected tumor tissues. Statistical methods, including the chi-square test, independent sample t-test, receiver operating characteristic curve analysis, Kaplan-Meier curve analysis, and Cox regression analysis were performed.

RESULTS

The patients with lower MK, AK, RK, and higher MD values showed significantly better prognosis (P < 0.001). Survival time was better in glioma patients with IDH1 mutation (P < 0.01), ATRX loss of expression (P < 0.05), and MGMT negative expression (P < 0.05). However, among the groups of gliomas with IDH1 wild type, ATRX retention and those with MGMT positive expression, the patients with lower MK showed better outcome (P < 0.01). Cox multivariate regression analysis demonstrated that MK, RK values and ATRX retention could be used as independent prognostic risk factors, and high MK values had the highest risk for prognosis (HR = 65.288).

CONCLUSIONS

Molecular markers and DKI parameters, especially MK values, can be used to effectively evaluate the prognosis of glioma patients.

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient

Background A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADC_min) and mean (ADC_mean) regions of interest were defined in tumor and normal appearing white matter (ADC_NAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADC_mean-to-ADC_NAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

Early assessment of recurrent glioblastoma response to bevacizumab treatment by diffusional kurtosis imaging: a preliminary report

PURPOSE

The purpose of this preliminary study is to apply diffusional kurtosis imaging to assess the early response of recurrent glioblastoma to bevacizumab treatment.

METHODS

This prospective cohort study included 10 patients who had been diagnosed with recurrent glioblastoma and scheduled to receive bevacizumab treatment. Diffusional kurtosis images were obtained from all the patients 0-7 days before (pre-bevacizumab) and 28 days after (post-bevacizumab) initiating bevacizumab treatment. The mean, 10th, and 90th percentile values were derived from the histogram of diffusional kurtosis imaging metrics in enhancing and non-enhancing lesions, selected on post-contrast T1-weighted and fluid-attenuated inversion recovery images. Correlations of imaging measures with progression-free survival and overall survival were evaluated using Spearman's rank correlation coefficient. The significance level was set at P < 0.05.

RESULTS

Higher pre-bevacizumab non-enhancing lesion volume was correlated with poor overall survival (r = -0.65, P = 0.049). Higher post-bevacizumab mean diffusivity and axial diffusivity (D_∥, D_∥10% and D_∥90%) in non-enhancing lesions were correlated with poor progression-free survival (r = -0.73, -0.83, -0.71 and -0.85; P < 0.05). Lower post-bevacizumab axial kurtosis (K_∥10%) in non-enhancing lesions was correlated with poor progression-free survival (r = 0.81, P = 0.008).

CONCLUSIONS

This preliminary study demonstrates that diffusional kurtosis imaging metrics allow the detection of tissue changes 28 days after initiating bevacizumab treatment and that they may provide information about tumor progression.