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

Tensor-valued diffusion MRI of human acute stroke

PURPOSE

Tensor-valued diffusion encoding can disentangle orientation dispersion and subvoxel anisotropy, potentially offering insight into microstructural changes after cerebral ischemia. The purpose was to evaluate tensor-valued diffusion MRI in human acute ischemic stroke, assess potential confounders from diffusion time dependencies, and compare to Monte Carlo diffusion simulations of axon beading.

METHODS

Linear (LTE) and spherical (STE) b-tensor encoding with inherently different effective diffusion times were acquired in 21 acute ischemic stroke patients between 3 and 57 h post-onset at 3 T in 2.5 min. In an additional 10 patients, STE with 2 LTE yielding different effective diffusion times were acquired for comparison. Diffusional variance decomposition (DIVIDE) was used to estimate microscopic anisotropy (μFA), as well as anisotropic, isotropic, and total diffusional variance (MKA , MKI , MKT ). DIVIDE parameters, and diffusion tensor imaging (DTI)-derived mean diffusivity and fractional anisotropy (FA) were compared in lesion versus contralateral white matter. Monte Carlo diffusion simulations of various cylindrical geometries for all b-tensor protocols were used to interpret parameter measurements.

RESULTS

MD was ˜40% lower in lesions for all LTE/STE protocols. The DIVIDE parameters varied with effective diffusion time: higher μFA and MKA in lesion versus contralateral white matter for STE with longer effective diffusion time LTE, whereas the shorter effective diffusion time LTE protocol yielded lower μFA and MKA in lesions. Both protocols, regardless of diffusion time, were consistent with simulations of greater beading amplitude and intracellular volume fraction.

CONCLUSION

DIVIDE parameters depend on diffusion time in acute stroke but consistently indicate neurite beading and larger intracellular volume fraction.

Clinical Assessment of Magnetic Resonance Spectroscopy and Diffusion-Weighted Imaging in Diffuse Glioma: Insights Into Histological Grading and IDH Classification

PURPOSE

To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase (IDH) classification in adult diffuse gliomas.

METHODS

A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann-Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan-Meier test.

RESULTS

The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade (P < .01). Subsequent application of Dunn's test showed significant differences in ADC mean among each histological grade (P < .01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas (P < .01). The Mann-Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification (P < .01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI's ADC mean parameter shows significant distinctions in both histological grade and IDH classification.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study

PURPOSE

Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios.

METHODS

Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations.

RESULTS

64 patients (female, 21; male, 43; age, 47.0 ± 13.7 years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05).

CONCLUSIONS

The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Quantitative analysis using intraoperative contrast-enhanced ultrasound in adult-type diffuse gliomas with isocitrate dehydrogenase mutations: association between hemodynamics and molecular features

PURPOSE

The relationship between contrast-enhanced ultrasound (CEUS) hemodynamics and the molecular biomarkers of adult-type diffuse gliomas, particularly isocitrate dehydrogenase (IDH), remains unclear. This study was conducted to provide a comprehensive description of the vascularization of adult-type diffuse gliomas using quantitative indicators. Additionally, it was designed to identify any variables with the potential to intraoperatively predict IDH mutation status.

METHODS

This prospective study enrolled patients with adult-type diffuse gliomas between November 2021 and September 2022. Intraoperative CEUS was performed, and CEUS videos were recorded for 90-second periods. Hemodynamic parameters, including the peak enhancement (PE) difference, were calculated based on the time-intensity curve of the region of interest. A differential analysis was performed on the CEUS parameters with respect to molecular biomarkers and grades. Receiver operating characteristic curves for various parameters were analyzed to evaluate the ability of those parameters to predict IDH mutation status.

RESULTS

Sixty patients with adult-type diffuse gliomas were evaluated. All hemodynamic parameters, apart from rising time, demonstrated significant differences between IDH-mutant and IDH-wildtype adult-type diffuse gliomas. The PE difference emerged as the optimal indicator for differentiating between IDH-wildtype and IDH-mutant gliomas, with an area under the curve of 0.958 (95% confidence interval, 0.406 to 0.785). Additionally, the hemodynamic parameters revealed significant differences across both grades and types of adult-type diffuse gliomas.

CONCLUSION

Hemodynamic parameters can be used intraoperatively to effectively distinguish between IDHwildtype and IDH-mutant adult-type diffuse gliomas. Additionally, quantitative CEUS equips neurosurgeons with dynamic perfusion information for various types and grades of adult-type diffuse gliomas.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.

Optimal experimental design and estimation for q-space trajectory imaging

Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.

An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma

BACKGROUND AND PURPOSE

Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics.

METHODS

Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis.

RESULTS

ADC_min, ADC_mean, rADC_min, rADC_mean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879).

CONCLUSIONS

There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

PURPOSE

Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time.

METHODS

We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps.

RESULTS

T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts.

CONCLUSION

We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.

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.

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.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

BACKGROUND

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.

METHODS

We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.

RESULTS

The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).

CONCLUSIONS

Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.