Diffusion Kurtosis Imaging of Gliomas Grades II and III - A Study of Perilesional Tumor Infiltration, Tumor Grades and Subtypes at Clinical Presentation

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

The outstanding diagnostic value of DKI in multimodal magnetic resonance imaging for benign and malignant breast tumors: A diagnostic accuracy study

To explore the value of applying different magnetic resonance imaging MRI sequences in the differential diagnosis of benign and malignant breast tumors. Routine breast magnetic resonance scans (T1-weighted image, T1WI; T2-weighted image, T2WI), dynamically enhanced scans, diffusion-weighted Imaging, and diffusion kurtosis imaging (DKI) scans were performed on 63 female patients with breast-occupying lesions. The benign and malignant lesions were confirmed by biopsy, excision-histopathology reports. There are 70 lesions, of which 46 are benign and 24 are malignant. Analyze the primary conditions, such as the shape, size, and boundary of the lesion, and determine the apparent diffusion coefficient (ADC), mean kurtosis (MK), and mean diffusion (MD) values. The receiver operating characteristic curve was used to evaluate the value and difference in differentiating benign and malignant lesions. In this study, the results of the 2 testers both showed that the MK of malignant lesions was significantly higher than that of benign lesions (P < .001), and the MD of benign lesions was higher than that of malignant lesions (P < .05). The ADC of benign lesions was higher than that of malignant lesions (P < .05). For MK, the area under the curve of the 2 testers was 0.855/0.869, respectively. When the best cutoff value of MK for tester 1 was 0.515, the sensitivity and specificity of MK for diagnosing malignant tumors were 83.3%/87.0%, respectively. For the 2 testers MD, and ADC, the area under the curve was < 0.5, and the diagnostic value was low. The MK value obtained by DKI has a specific value in the differential diagnosis of benign and malignant breast lesions. DKI is helpful in the identification of benign and malignant breast tumors. The diagnostic value is outstanding, and its importance to the changes in the microstructure of the organization needs to be further explored.

Perifocal Zone of Brain Gliomas: Application of Diffusion Kurtosis and Perfusion MRI Values for Tumor Invasion Border Determination

(1) Purpose: To determine the borders of malignant gliomas with diffusion kurtosis and perfusion MRI biomarkers. (2) Methods: In 50 high-grade glioma patients, diffusion kurtosis and pseudo-continuous arterial spin labeling (pCASL) cerebral blood flow (CBF) values were determined in contrast-enhancing area, in perifocal infiltrative edema zone, in the normal-appearing peritumoral white matter of the affected cerebral hemisphere, and in the unaffected contralateral hemisphere. Neuronavigation-guided biopsy was performed from all affected hemisphere regions. (3) Results: We showed significant differences between the DKI values in normal-appearing peritumoral white matter and unaffected contralateral hemisphere white matter. We also established significant (p < 0.05) correlations of DKI with Ki-67 labeling index and Bcl-2 expression activity in highly perfused enhancing tumor core and in perifocal infiltrative edema zone. CBF correlated with Ki-67 LI in highly perfused enhancing tumor core. One hundred percent of perifocal infiltrative edema tissue samples contained tumor cells. All glioblastoma samples expressed CD133. In the glioblastoma group, several normal-appearing white matter specimens were infiltrated by tumor cells and expressed CD133. (4) Conclusions: DKI parameters reveal changes in brain microstructure invisible on conventional MRI, e.g., possible infiltration of normal-appearing peritumoral white matter by glioma cells. Our results may be useful for plotting individual tumor invasion maps for brain glioma surgery or radiotherapy planning.

Mapping early tumor response to radiotherapy using diffusion kurtosis imaging*

PURPOSE

In this pilot study, DKI measures of diffusivity and kurtosis were compared in active tumor regions and correlated to radiologic response to radiotherapy after completion of 2 weeks of treatment to derive potential early measures of tumor response.

METHODS

MRI and Magnetic Resonance Spectroscopic Imaging (MRSI) data were acquired before the beginning of RT (pre-RT) and 2 weeks after the initiation of treatment (during-RT) in 14 glioblastoma patients. The active tumor region was outlined as the union of the residual contrast-enhancing region and metabolically active tumor region. Average and standard deviation of mean, axial, and radial diffusivity (MD, AD, RD) and mean, axial, and radial kurtosis (MK, AK, RK) values were calculated for the active tumor VOI from images acquired pre-RT and during-RT and paired t-tests were executed to estimate pairwise differences. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive capabilities of changes in diffusion metrics for progression-free survival (PFS).

RESULTS

Analysis showed significant pairwise differences for AD (p = 0.035; Cohen's d of 0.659) and AK (p = 0.019; Cohen's d of 0.753) in diffusion measures after 2 weeks of RT. ROC curve analysis showed that percentage change differences in AD and AK between pre-RT and during-RT scans provided an Area Under the Curve (AUC) of 0.524 and 0.762, respectively, in discriminating responders (PFS>180 days) and non-responders (PFS<180 days).

CONCLUSION

This pilot study, although preliminary in nature, showed significant changes in AD and AK maps, with kurtosis derived AK maps showing an increased sensitivity in mapping early changes in the active tumor regions.

Challenges and opportunities for advanced neuroimaging of glioblastoma

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

Brain Diffusion Weighted Imaging Study of Mongolian Idiopathic Epilepsy

Objective

To evaluate the effectiveness of diffusion-weighted imaging in the assessment of idiopathic epilepsy in Mongolian.

Methods

One hundred Mongolian idiopathic epilepsy patients were enrolled as the observation group and 100 healthy Mongolian volunteers as the control group. All the subjects underwent routine MRI, diffusion kurtosis imaging (DKI), and intra-voxel incoherent motion (IVIM) examination on a 3.0 T scanner. Mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA), true water molecular diffusion coefficient (D), mean diffusion coefficient (MD), pseudo-diffusion coefficient (D∗), and perfusion fraction (f) of each region of interest in the brain were measured. Count data were expressed as rates, and the chi-square test was performed for comparison between groups. Measurement data were first assessed by a normality test, and the t test for independent samples was performed for comparison between groups if they met the normal distribution; for non-normal distribution, the Mann-Whitney U test was performed for comparison between groups. A ROC curve analysis was performed to test the effectiveness of each parameter.

Results

MK values of the hippocampus, thalamus, and white matter of the temporal lobe in the observation group were significantly higher than those in the control group, while D and F values were significantly lower (all P < 0.05). ROC curve analysis showed that MK, D, and F values of the hippocampus, thalamus, and white matter of the temporal lobe had moderate to good diagnostic efficacy for idiopathic epilepsy (AUC = 0.617-0.749, all P < 0.001).

Conclusion

DKI and IVIM can more accurately represent the abnormal changes of brain tissue in patients with epilepsy, and it may have important implications for the clinical diagnosis of Mongolian epileptic patients.

Two Patterns of White Matter Connection in Multiple Gliomas: Evidence from Probabilistic Fiber Tracking

Background: Multiple lesions are uncommon in brain gliomas, and their pathophysiology is poorly understood. Invasive growth along white matter tracts is an important clinicopathological characteristic of gliomas, and a major factor in a poor therapeutic outcome. Here, we used probabilistic fiber tracking and cluster analysis to investigate the inter-focal connectivity relationships of multiple gliomas, in order to seek inferential evidence of common origin. Methods: MRI scans of 46 patients with multiple gliomas were retrospectively analyzed. Before surgery, all patients underwent multimodal functional MR imaging, including diffusion tensor imaging, enhanced 3D T1-weighted imaging, diffusion-weighted imaging, 1H MR spectroscopy, and dynamic susceptibility contrast perfusion-weighted imaging. Probabilistic fiber tracking was used to quantify white matter connectivity between neoplastic foci. Hierarchical cluster analysis was performed to identify patterns of white matter connection. Results: Cluster analysis reveals two patterns of connectivity, one with smaller, and one with greater, connectivity (2675 ± 1098 versus 30432 ± 22707, p < 0.0001). The two subgroups show significant differences in relative cerebral blood volume (2.31 ± 0.95 versus 1.73 ± 0.48, p = 0.002) and lipid/creatine ratio (0.32 ± 0.22 versus 0.060 ± 0.051, p = 0.006). Conclusion: Two distinct patterns of white matter connection exist in multiple gliomas. Those with lower connectivity tend to have independent origins, and can be termed true multicentric glioma, whereas those with greater connectivity tend to share common origin, and spread along white matter tracts. True multicentric gliomas have higher vascularity and more intratumoral necrosis. These findings may help to develop personalized therapeutic strategies for multiple gliomas.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

Background

Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation.

Purpose

To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter.

Materials and Methods

Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm², were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNR_eff).

Results

The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10^–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNR_eff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10^–3, paired U-test). The STE improved CNR_eff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected.

Conclusion

The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.

The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude

Diffuse low-grade gliomas (DLGGs) are heterogeneous and poorly circumscribed neoplasms with isolated tumor cells that extend beyond the margins of the lesion depicted on MRI. Efforts to demarcate the glioma core from the surrounding healthy brain led us to define an intermediate region, the so-called peritumoral zone (PTZ). Although most studies about PTZ have been conducted on high-grade gliomas, the purpose here is to review the cellular, metabolic, and radiological characteristics of PTZ in the specific context of DLGG. A better delineation of PTZ, in which glioma cells and neural tissue strongly interact, may open new therapeutic avenues to optimize both functional and oncological results. First, a connectome-based “supratotal” surgical resection (i.e., with the removal of PTZ in addition to the tumor core) resulted in prolonged survival by limiting the risk of malignant transformation, while improving the quality of life, thanks to a better control of seizures. Second, the timing and order of (neo)adjuvant medical treatments can be modulated according to the pattern of peritumoral infiltration. Third, the development of new drugs specifically targeting the PTZ could be considered from an oncological (such as immunotherapy) and epileptological perspective. Further multimodal investigations of PTZ are needed to maximize long-term outcomes in DLGG patients.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer

BACKGROUND

Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method.

AIM

To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC.

METHODS

A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging.

RESULTS

The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858).

CONCLUSION

MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.

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.

Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model

The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm² ). Three CTRW model parameters, including an anomalous diffusion coefficient D_m , and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (D_m : 1.14 ± 0.37 μm² /ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (D_m : 0.88 ± 0.21 μm² /ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (D_m : 0.73 ± 0.22 μm² /ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.

Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery

Diffusion tensor imaging (DTI)-based fiber tractography is routinely used in clinical applications to visualize major white matter tracts, such as the corticospinal tract (CST), optic radiation (OR), and arcuate fascicle (AF). Nevertheless, DTI is limited due to its capability of resolving intra-voxel multi-fiber populations. Sophisticated models often require long acquisition times not applicable in clinical practice. Diffusion kurtosis imaging (DKI), as an extension of DTI, combines sophisticated modeling of the diffusion process with short acquisition times but has rarely been investigated in fiber tractography. In this study, DTI- and DKI-based fiber tractography of the CST, OR, and AF was investigated in healthy volunteers and glioma patients. For the CST, significantly larger tract volumes were seen in DKI-based fiber tractography. Similar results were obtained for the OR, except for the right OR in patients. In the case of the AF, results of both models were comparable with DTI-based fiber tractography showing even significantly larger tract volumes in patients. In the case of the CST and OR, DKI-based fiber tractography contributes to advanced visualization under clinical time constraints, whereas for the AF, other models should be considered.

Altered Motor Excitability in Patients With Diffuse Gliomas Involving Motor Eloquent Areas: The Impact of Tumor Grading

BACKGROUND

Diffuse gliomas have an increased biological aggressiveness across the World Health Organization (WHO) grading system. The implications of glioma grading on the primary motor cortex (M1)-corticospinal tract (CST) excitability is unknown.

OBJECTIVE

To assess the excitability of the motor pathway with navigated transcranial magnetic stimulation (nTMS).

METHODS

Retrospective cohort study of patients admitted for surgery with diffuse gliomas within motor eloquent areas. Demographic, clinical, and nTMS-related variables were collected. The Cortical Excitability Score (CES 0 to 2 according to the number of abnormal interhemispheric resting motor threshold (RMT) ratios) was calculated for patients where bilateral upper and lower limb mapping was performed.

RESULTS

A total of 45 patients were included: 9 patients had a low-grade glioma and 36 patients had a high-grade glioma. The unadjusted analysis revealed an increase in the latency of the motor evoked potential of the lower limb with an increase of the WHO grade (P = .038). The adjusted analysis confirmed this finding (P = .013) and showed a relation between the increase in the WHO and a decreased RMT (P = .037) of the motor evoked responses in the lower limb. When CES was calculated, an increase in the score was related with an increase in the WHO grade (unadjusted analysis-P = .0001; adjusted analysis-P = .001) and in isocitrate dehydrogenase (IDH) wild-type tumors (unadjusted analysis-P = .020).

CONCLUSION

An increase in the WHO grading system and IDH wild-type tumors are associated with an abnormal excitability of the motor eloquent areas in patients with diffuse gliomas.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

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.

Development of a novel phantom using polyethylene glycol for the visualization of restricted diffusion in diffusion kurtosis imaging and apparent diffusion coefficient subtraction method

The present study aimed to investigate whether polyethylene glycol (PEG) phantoms have the potential to be used as standard phantoms for magnetic resonance imaging (MRI) in order to visualize restricted diffusion in diffusion kurtosis imaging (DKI), the ADC subtraction method (ASM) and the apparent diffusion coefficient (ADC). Diffusion-weighted images of 0-120 mM PEG phantoms were captured to create ADC, DKI and ASM images with post-processing. ASM is a recently developed method for restricted diffusion imaging using the readout segmentation of long variable echo-train sequences. As the PEG concentration increases, the ADC value decreases. Conversely, an increase in DKI and ASM values is associated with increasing PEG concentration. Formulae were constructed to represent the association between PEG concentrations and ADC, DKI and ASM values. These formulae can be used to determine the required PEG concentrations to mimic arbitrary ADC, DKI and ASM values of certain diseases, including tumors and infarctions. Validation experiments were conducted using bio-phantoms and clarified that the PEG phantoms cover the range of ADC and DKI values reported in previous clinical research using 3T MRI. PEG phantoms may be useful for future MRI research involving restricted diffusion.

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.

Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?

PURPOSE

This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases.

MATERIALS AND METHODS

Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier.

RESULTS

The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860-0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895-1.000) and 0.917 (95% CI 0.801-1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935-1.000).

CONCLUSION

Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.

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.

Adult Glioma WHO Classification Update, Genomics, and Imaging: What the Radiologists Need to Know

Recent advances in the understanding of the genetic makeup of gliomas have led to a paradigm shift in the diagnosis and classification of these tumors. Driven by these changes, the World Health Organization (WHO) introduced an update to its classification system of central nervous system (CNS) tumors in 2016. The updated glioma classification system incorporates molecular markers into tumor subgrouping, which has been shown to better correlate with tumor biology and behavior as well as patient prognosis than the previous purely histology-based classification system. Familiarity with this new classification scheme, the individual molecular markers, and corresponding imaging findings is critical for the radiologists who play an important role in diagnostic and surveillance imaging of patients with CNS tumors. The goals of this article are to review these updates to the WHO classification of CNS tumors with a focus on adult gliomas, provide an overview of key genomic markers of gliomas, and review imaging features pertaining to various genomic subgroups of adult gliomas.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose

To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas.

Material and methods

A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas.

Results

There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10^-3 mm²/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%.

Conclusion

TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors

PURPOSE

To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors.

METHODS

B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MK_A ) and isotropic kurtosis (MK_I ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol.

RESULTS

The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MK_A = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MK_I = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03).

CONCLUSION

Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.

Diffusion kurtosis MRI as a predictive biomarker of response to neoadjuvant chemotherapy in high grade serous ovarian cancer

This study assessed the feasibility of using diffusion kurtosis imaging (DKI) as a measure of tissue heterogeneity and proliferation to predict the response of high grade serous ovarian cancer (HGSOC) to neoadjuvant chemotherapy (NACT). Seventeen patients with HGSOC were imaged at 3 T and had biopsy samples taken prior to any treatment. The patients were divided into two groups: responders and non-responders based on Response Evaluation Criteria In Solid Tumours (RECIST) criteria. The following imaging metrics were calculated: apparent diffusion coefficient (ADC), apparent diffusion (Dapp) and apparent kurtosis (Kapp). Tumour cellularity and proliferation were quantified using histology and Ki-67 immunohistochemistry. Mean Kapp before therapy was higher in responders compared to non-responders: 0.69 ± 0.13 versus 0.51 ± 0.11 respectively, P = 0.02. Tumour cellularity correlated positively with Kapp (rho = 0.50, P = 0.04) and negatively with both ADC (rho = -0.72, P = 0.001) and Dapp (rho = -0.80, P < 0.001). Ki-67 expression correlated with Kapp (rho = 0.53, P = 0.03) but not with ADC or Dapp. In conclusion, Kapp was found to be a potential predictive biomarker of NACT response in HGSOC, which suggests that DKI is a promising clinical tool for use oncology and radiology that should be evaluated further in future larger studies.

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.

Value of diffusion kurtosis imaging in assessing low-grade gliomas

BACKGROUND

Diffusion kurtosis imaging (DKI) measures have been shown to provide increased sensitivity relative to diffusion tensor imaging (DTI) in detecting pathologies.

PURPOSE

To compare the sensitivity of DKI-derived kurtosis and diffusion maps for assessment of low-grade gliomas (LGG).

STUDY TYPE

Prospective study.

POPULATION

In all, 19 LGG patients and 26 healthy control subjects were recruited.

FIELD STRENGTH/SEQUENCE

Echo-planar-imaging diffusion-weighted MR images (b-values = 0, 1000, and 2000 with 30 diffusion gradient directions) were acquired on a 3T scanner.

ASSESSMENT

Maps for mean, axial, and radial diffusivity (MD, AD, and RD) and kurtosis (MK, AK, and RK), and fractional anisotropy (FA) were evaluated in the tumor, perilesional white matter, and contralateral normal-appearing white matter regions.

STATISTICAL TESTING

General linear models (GLM), Cohen's d for effect size estimates, false discovery rate (FDR) for multiple corrections, Cochran Q-test.

RESULTS

Pairwise differences were observed for all diffusion and kurtosis measures between the studied regions (FDR P < 0.001), except an FA map that failed to show significant differences between the lesion and perilesional white matter (FDR P = 0.373). Effect size analysis showed that kurtosis metrics were found to be 18.8% (RK, P = 0.144) to 29.1% (AK, P < 0.05) more sensitive in discriminating perilesional regions from the lesion than corresponding diffusion metrics, whereas AK provided a 25.0% (P < 0.05) increase in sensitivity in discriminating perilesional and contralateral white matter. RK was found to be the most sensitive to contralateral white matter differences between low-grade gliomas and controls, with MK and RK providing a significantly greater sensitivity of 587.2% (P < 0.001) and 320.7% (P < 0.001) than MD and RD, respectively.

DATA CONCLUSION

Kurtosis maps showed increased sensitivity, as compared to counterpart diffusion maps, for evaluation of microstructural changes in gliomas with a 3-6-fold increment in assessing changes in contralateral white matter.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1551-1558.