Utility of multiparametric 3-T MRI for glioma characterization

Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy

PURPOSE

To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs).

METHODS

Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (K^trans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman's correlation coefficients (ρ) were computed.

RESULTS

HGGs had higher 95th percentile rCBV, K^trans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and K^trans were 0.79 (95% CI, 0.67-0.91) and 0.74 (95% CI, 0.59-0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48-0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52-0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and K^trans (P < 0.01), with K^trans showing the highest AUC (0.73, 95% CI 0.57-0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and K^trans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01).

CONCLUSIONS

The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile K^trans may aid predicting IDH status of diffuse gliomas.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI

BACKGROUND AND PURPOSE

Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T₁ perfusion MRI parameters combined under a machine learning framework.

MATERIALS AND METHODS

This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T₁ perfusion and DWI data along with conventional MRI images. Analysis of T₁ perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T₁ perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T₁ perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter.

RESULTS

T₁ perfusion parameter values were significantly different between the two groups. The combination of T₁ perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error.

CONCLUSION

The approach of using quantitative T₁ perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

Cerebral hemodynamic and metabolic dysregulation in the postradiation brain

Technological advances in the delivery of radiation and other novel cancer therapies have significantly improved the 5-year survival rates over the last few decades. Although recent developments have helped to better manage the acute effects of radiation, the late effects such as impairment in cognition continue to remain of concern. Accruing data in the literature have implicated derangements in hemodynamic parameters and metabolic activity of the irradiated normal brain as predictive of cognitive impairment. Multiparametric imaging modalities have allowed us to precisely quantify functional and metabolic information, enhancing the anatomic and morphologic data provided by conventional MRI sequences, thereby contributing as noninvasive imaging-based biomarkers of radiation-induced brain injury. In this review, we have elaborated on the mechanisms of radiation-induced brain injury and discussed several novel imaging modalities, including MR spectroscopy, MR perfusion imaging, functional MR, SPECT, and PET that provide pathophysiological and functional insights into the postradiation brain, and its correlation with radiation dose as well as clinical neurocognitive outcomes. Additionally, we explored some innovative imaging modalities, such as quantitative blood oxygenation level-dependent imaging, susceptibility-based oxygenation measurement, and T2-based oxygenation measurement, that hold promise in delineating the potential mechanisms underlying deleterious neurocognitive changes seen in the postradiation setting. We aim that this comprehensive review of a range of imaging modalities will help elucidate the hemodynamic and metabolic injury mechanisms underlying cognitive impairment in the irradiated normal brain in order to optimize treatment regimens and improve the quality of life for these patients.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas

BACKGROUND AND PURPOSE

T1-PWI with high temporal resolution may provide a reliable relative CBV value as a valid alternative to T2*-PWI under increased susceptibility. The purpose of this study was to assess the technical and clinical performance of T1-relative CBV in patients with postoperative high-grade gliomas.

MATERIALS AND METHODS

Forty-five MRIs of 34 patients with proved high-grade gliomas were included. In all MRIs, T1- and T2*-PWIs were both acquired and processed semiautomatically to generate relative CBV maps using a released commercial software. Lesion masks were overlaid on the relative CBV maps, followed by a histogram of the whole VOI. The intraclass correlation coefficient and Bland-Altman plots were used for quantitative and qualitative comparisons. Signal loss from both methods was compared using the Wilcoxon signed-rank test of zero voxel percentage. The MRIs were divided into a progression group (n = 20) and a nonprogression group (n = 14) for receiver operating characteristic curve analysis.

RESULTS

Fair intertechnique consistency was observed between the 90th percentiles of the T1- and T2*-relative CBV values (intraclass correlation coefficient = 0.558, P < .001). T2*-PWI revealed a significantly higher percentage of near-zero voxels than T1-PWI (17.7% versus 3.1%, P < .001). There was no statistically significant difference between the area under the curve of T1- and T2*-relative CBV (0.811 versus 0.793, P = .835). T1-relative CBV showed 100% sensitivity and 57.1% specificity for the detection of progressive lesions.

CONCLUSIONS

T1-relative CBV demonstrated exquisite diagnostic performance for detecting progressive lesions in postoperative patients with high-grade gliomas, suggesting the potential role of T1-PWI as a valid alternative to the traditional T2*-PWI.

Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism

Proton magnetic resonance spectroscopy (1H-MRS) provides a non-invasive biochemical profile of brain tumors. The conventional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent and development of more sophisticated metabolic imaging and spectroscopic sequences have revolutionized the field of neuro-oncologic metabolomics. In this review article, we will briefly describe the scientific premises of three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) MRI techniques. Several published studies have shown how these emerging techniques can significantly impact the management of patients with glioma by determining histologic grades, molecular profiles, planning treatment strategies, and assessing the therapeutic responses. The purpose of this review article is to summarize the potential clinical applications of these techniques in studying brain tumor metabolism.

Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas

PURPOSE

To investigate the feasibility for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas(GBMs) by intravoxel incoherent motion(IVIM) and dynamic susceptibility contrast(DSC).

METHODS

Preoperative IVIM and DSC images of 71 patients(IDH mutation:45, IDH wildtype: 26; MGMT methylation: 31, MGMT unmethylation:40) with glioblastomas were analyzed retrospectively. Perfusion parameters including microcirculation perfusion coefficient(D*), perfusion fraction(f), cerebral blood volume(CBV) and cerebral blood flow(CBF) were measured. Corrected perfusion parameters containing corrected perfusion coefficient(ADC_perf) and simplified perfusion fraction(SPF) were from the simplified IVIM with 3 b values. Correlations among parameters were analyzed by Spearman correlation. All parameters were compared with Mann-Whitney U test. Univariate and multivariate logistic regression models were constructed. The receiver operating characteristic(ROC) curve was analyzed.

RESULTS

The IVIM parameters showed merely moderate correlations with CBV and showed no correlation with CBF. IDH mutation GBMs showed lower D*, ADC_perf, SPF, CBV and higher f than IDH wildtype GBMs(all p < 0.05). D* was the independent predictor for IDH mutation with the highest AUC of 0.912(95%CI: 0.821-0.966). The D*, ADC_perf, SPF and CBV of MGMT promoter methylation GBMs were lower than unmethylation GBMs while f was higher(all p < 0.05). Multivariate model showed the highest prediction efficacy for MGMT promoter methylation with an AUC of 0.915(95%CI: 0.824-0.968). The CBF was not useful in distinguishing IDH mutation and MGMT promoter methylation status(p = 0.055, 0.215).

CONCLUSION

IDH mutation and MGMT promoter methylation status in GBMs can be assessed effectively by IVIM and DSC. Besides, D* was the independent predictor of IDH mutation status.

Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

RATIONALE AND OBJECTIVE

To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading.

MATERIALS AND METHODS

Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance.

RESULTS

The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade.

CONCLUSION

The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading

BACKGROUND AND PURPOSE

To evaluate the performance of multiparametric MR images in differentiation of different regions of the gross tumor area and for assessment of glioma grade.

METHODS

Forty-six glioma subjects (18 grade II, 11 grade III, and 17 grade IV) underwent a comprehensive MR and spectroscopic imaging procedure. Maps were generated by subtraction of T1-weighted images from contrast-enhanced T1-weighted images (ΔT1 map) and T1-weighted images from T2-weighted images (ΔT2 map). Regions of interest (ROIs) were positioned in normal-appearing white matter (NAWM), enhancing tumor, hyperintense T2, necrotic region, and immediate and distal peritumoral regions (IPR and DPR). Relative signal contrast was estimated as difference between mean intensities in ROIs and NAWM. Classification using support vector machines was applied to all image series to determine the efficacy of regional contrast measures for differentiation of low- and high-grade lesions and grade III and IV lesions.

RESULTS

ΔT1 and ΔT2 maps offered higher contrast as compared to other parametric maps in differentiating enhancing tumor and edematous regions, respectively, and provided the highest classification accuracy for differentiating low- and high-grade tumors, of 91% and 90.4%. Choline/N-acetylaspartate maps provided significant contrast for delineating IPR and DPR. For differentiating high-grade gliomas, ΔT2 and ΔT1 maps provided a mean accuracy of 90.9% and 88.2%, which was lower than that obtained using cerebral blood volume (93.7%) and choline/creatine (93.3%) maps.

CONCLUSION

This study showed that subtraction maps provided significant contrast in differentiating several regions of the gross tumor area and are of benefit for accurate tumor grading.

Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading

PURPOSE

To evaluate the efficacy of optimized T1-Perfusion MRI protocol (protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE (CSENSE) to differentiate high-grade-glioma (HGG) and low-grade-glioma (LGG) and to compare it with the conventional protocol (protocol-1) with partial brain coverage used in our center.

METHODS

This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted (W) fast-field-echo (FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation (COV), Relative-Percentage-Error (RPE), Normalized-Mean-Squared-Error (NMSE) and qualitative scoring were used for the former study. Tracer-kinetic (K^trans,v_e,v_p) and hemodynamic (rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG.

RESULTS

The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artefacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964).

CONCLUSION

CSENSE (R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas.

Comparing amide proton transfer imaging with dynamic susceptibility contrast-enhanced perfusion in predicting histological grades of gliomas: a meta-analysis

Background

As a subtype of chemical exchange saturation transfer imaging without contrast agent administration, amide proton transfer (APT) imaging has demonstrated the potential for differentiating the histologic grades of gliomas. Dynamic susceptibility contrast-enhanced perfusion, a perfusion-weighted imaging technique, is a well-established technique in grading gliomas.

Purpose

To compare the ability of amide proton transfer and dynamic susceptibility contrast-enhanced imaging for predicting the grades of gliomas.

Material and Methods

A comprehensive literature search was performed independently by two observers to identify articles about the diagnostic performance of amide proton transfer and dynamic susceptibility contrast-enhanced perfusion in predicting the grade of gliomas. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

Results

Of 179 studies identified, 23 studies were included the analysis. Eight studies evaluated amide proton transfer and 16 studies evaluated dynamic susceptibility contrast-enhanced perfusion with the parameter rCBV. The pooled sensitivities and specificities of each study’s best performing parameter were 88% (95% confidence interval [CI] 74–95) and 89% (95% CI 78–95) for amide proton transfer, and 95% (95% CI 87–98), 88% (95% CI 81–93) for perfusion-weighted imaging–dynamic susceptibility contrast-enhanced perfusion, respectively. The pooled sensitivities and specificities for grading gliomas using the two most commonly evaluated parameters, were 92% (95% CI 80–97) and 90% (95% CI 75–96) for APTmax, and 97% (95% CI 91–99) and 87% (95% CI 80–92) for rCBVmax, respectively.

Conclusion

Considering the similar performance of APT and dynamic susceptibility contrast-enhanced (DSC) in predicting glioma grade, the former method appears preferable since it needs no contrast agent.

Investigating dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in posterior fossa tumors: differences and similarities with supratentorial tumors

PURPOSE

To assess the accuracy of dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in glioma grading and brain tumor characterization of infratentorial tumors, and to investigate differences from supratentorial tumors.

METHODS

This retrospective study, approved by the institutional review board, included 246 patients with brain tumors (184 supratentorial, 62 infratentorial), grouped by tumor type: high-grade gliomas (HGG), low-grade gliomas (LGG), metastases (Met), and primary central nervous system lymphoma (PCNSL). Relative cerebral blood volume (rCBV) and mean percentage of signal recovery (PSR) were calculated. For statistical analyses, lesions were grouped by location and histology. Differences were tested with Mann-Whitney U tests. From ROC curves, we calculated accuracy, sensitivity, specificity, PPV, and NPV, for rCBV and PSR.

RESULTS

For infratentorial tumors, rCBV was highly accurate in differentiating HGG from LGG (AUC = 0.938). Mean PSR showed high accuracy in differentiating PCNSL and HGG from Met (AUC = 0.978 and AUC = 0.881, respectively). Infratentorial and supratentorial tumors had similarly high rCBV in HGG, high mean PSR in PCNSL, and low mean PSR in Met. The main differences were the optimum threshold rCBV values (3.04 for supratentorial, 1.77 for infratentorial tumors) and the mean PSR, which was significantly higher in LGG than in HGG in supratentorial (p = 0.035), but not infratentorial gliomas. Using infratentorial rCBV threshold values for supratentorial tumors decreased the sensitivity and specificity.

CONCLUSION

rCBV and mean PSR were useful in grading and differentiating infratentorial tumors. Proper cutoff values were important in the accuracy of perfusion-weighted imaging in posterior fossa tumors.

MiRNA-130a-3p inhibits cell proliferation, migration, and TMZ resistance in glioblastoma by targeting Sp1

Specificity protein 1 (Sp1) is aberrantly expressed and involved in the development and metastasis of glioblastoma. In this study, we observed that the expression of Sp1 was upregulated while that of microRNA (miR)-130a-3p was downregulated in glioblastoma cell lines. Sp1 was validated as a target of miR-130a-3p; increased levels of miR-130a-3p inhibited the proliferation, migration, and temozolomide (TMZ) resistance of the glioblastoma cells. However, Sp1 overexpression compromised the inhibitory effects of miR-130a-3p. Our study elucidates the functional interaction between miR-130a-3p and Sp1 in the development and progression of glioblastoma, suggesting a potential therapeutic target for the disease.

Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis

Background

T1‐weighted dynamic contrast‐enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE‐MRI in discriminating between low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs), between tumor recurrence and treatment‐related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs.

Methods

We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE‐MRI for the aforementioned entities. Meta‐analysis was conducted with the use of a random effects model.

Results

Twenty‐seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE‐MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment‐related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment‐related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively.

Conclusions

Dynamic contrast‐enhanced‐Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE‐MRI shows high diagnostic accuracy in discriminating between HGGs and their low‐grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment‐related changes as well as PCNSLs and HGGs.

Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components

BACKGROUND

Glioma grading between intermediate grades (Grade II vs. III and Grade III vs. IV) as well as multiclass grades (Grade II vs. III vs. IV) is challenging and needs to be addressed.

PURPOSE

To develop an artificial intelligence-based methodology for glioma grading using T₁ perfusion parameters and volume of tumor components, and validate the efficacy of the methodology by grading on a cohort of glioma patients.

STUDY TYPE

Retrospective.

POPULATION

The development set consisted of 53 glioma patients and validation consisted of 13 glioma patients.

FIELD STRENGTH/SEQUENCE

Conventional MRI images (2D T₁ -W, dual PD-T₂ -W, and 3D FLAIR) and 3D T₁ perfusion MRI data obtained at 3 T.

ASSESSMENT

Enhancing and nonenhancing components of glioma were segmented out and combined to form the region of interest (ROI) for glioma grading. Prominent vessels were removed from the selected ROI. Different T₁ perfusion parameters from the ROI were combined with volume of tumor components to form the feature set for glioma grading. Optimization was carried out for selection of the statistic of the T₁ perfusion parameters and the features to be used for glioma grading using sequential feature selection and random forest-based feature selection method. An optimized support vector machine (SVM) classifier was used for glioma grading.

STATISTICAL TESTS

Mean ± SD, analysis of variance (ANOVA) followed by the Tukey-Kramer test, ROC analysis.

RESULTS

Classification error for Grade II vs. III was 3.7%, for Grade III vs. IV was 5.26%, and for Grade II vs. III vs. IV was 9.43% using the proposed methodology. The mean of the values above the 90^th percentile value of T₁ perfusion parameters provided a maximum area under the curve (AUC) for intermediate grade differentiation. Random forest obtained optimal feature set provided better grading results than other methods using the SVM classifier.

DATA CONCLUSION

It was feasible to achieve low classification error for intermediate as well as multiclass glioma grading using an SVM classifier based on optimized features obtained from T₁ perfusion MRI and volumes of tumor components.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1295-1306.

Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma

Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three-dimensional echo planar spectroscopic imaging (3D-EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo-radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow-up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N-acetylaspartate (Cho/NAA) were computed and co-registered with post-contrast T₁ -weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D-EPSI in differentiating TP from PsP with high sensitivity and specificity.

Quantitative evaluation of diffusion tensor imaging for clinical management of glioma

Diffusion tensor imaging (DTI), assessing physiological motion of water in vivo, provides macroscopic view of microstructures of white matter in the central nervous system, and such imaging technique had been extensively used for the clinical treatment and research of glioma. This review mainly focuses on illuminating the merits of quantitative evaluation of DTI for glioma management. The content of the article includes DTI's application on tissue characterization, white matter tracts mapping, radiotherapy delineation, post-therapy outcome assessment, and multimodal imaging. At last, we elucidate a synoptic presentation of DTI limitation, which is critical for physicians making DTI-based clinical decisions in glioma management.

Lesion segmentation for MR spectroscopic imaging using the convolution difference method

PURPOSE

Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion.

METHODS

A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out.

RESULTS

Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold.

CONCLUSION

This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

OBJECTIVE

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach.

MATERIALS AND METHODS

Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification.

RESULTS

A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features.

CONCLUSION

In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort.

Multimodal Imaging of Patients With Gliomas Confirms 11C-MET PET as a Complementary Marker to MRI for Noninvasive Tumor Grading and Intraindividual Follow-Up After Therapy

The value of combined L-( methyl-[¹¹C]) methionine positron-emitting tomography (MET-PET) and magnetic resonance imaging (MRI) with regard to tumor extent, entity prediction, and therapy effects in clinical routine in patients with suspicion of a brain tumor was investigated. In n = 65 patients with histologically verified brain lesions n = 70 MET-PET and MRI (T1-weighted gadolinium-enhanced [T1w-Gd] and fluid-attenuated inversion recovery or T2-weighted [FLAIR/T2w]) examinations were performed. The computer software "visualization and analysis framework volume rendering engine (Voreen)" was used for analysis of extent and intersection of tumor compartments. Binary logistic regression models were developed to differentiate between World Health Organization (WHO) tumor types/grades. Tumor sizes as defined by thresholding based on tumor-to-background ratios were significantly different as determined by MET-PET (21.6 ± 36.8 cm³), T1w-Gd-MRI (3.9 ± 7.8 cm³), and FLAIR/T2-MRI (64.8 ± 60.4 cm³; P < .001). The MET-PET visualized tumor activity where MRI parameters were negative: PET positive tumor volume without Gd enhancement was 19.8 ± 35.0 cm³ and without changes in FLAIR/T2 10.3 ± 25.7 cm³. FLAIR/T2-MRI visualized greatest tumor extent with differences to MET-PET being greater in posttherapy (64.6 ± 62.7 cm³) than in newly diagnosed patients (20.5 ± 52.6 cm³). The binary logistic regression model differentiated between WHO tumor types (fibrillary astrocytoma II n = 10 from other gliomas n = 16) with an accuracy of 80.8% in patients at primary diagnosis. Combined PET and MRI improve the evaluation of tumor activity, extent, type/grade prediction, and therapy-induced changes in patients with glioma and serve information highly relevant for diagnosis and management.

Pediatric astrocytic tumor grading: comparison between arterial spin labeling and dynamic susceptibility contrast MRI perfusion

PURPOSE

The aim of this study was to compare arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) MRI perfusion with respect to diagnostic performance in tumor grading in pediatric patients with low- and high-grade astrocytic tumors (AT).

METHODS

We retrospectively analyzed 37 children with histologically proven treatment naive low- and high-grade AT who underwent concomitant pre-operative ASL and DSC MRI perfusion. Studies were performed on a 1.5 T scanner, and a pulsed technique was used for ASL. DSC data were post-processed with a leakage correction software. Normalization of tumor perfusion parameters was performed with contralateral normal appearing gray matter. Normalized cerebral blood volume (nCBV) values in the most perfused area of each neoplasm were compared with normalized DSC-derived cerebral blood flow (nDSC-CBF) and ASL-derived cerebral blood flow (nASL-CBF) data, and correlated with WHO tumor grade. Statistics included Pearson's chi-square and Mann-Whitney U tests, Spearman's rank correlation, and receiver operating characteristic (ROC) analysis.

RESULTS

A significant correlation was demonstrated between DSC and ASL data (p < 0.001). Significant differences in terms of DSC and ASL data were found between low- and high-grade AT (p < 0.001). ROC analysis demonstrated similar performances between all parameters in predicting tumor grade (nCBV: AUC 0.96, p < 0.001; nDSC-CBF: AUC 0.98, p < 0.001; nASL-CBF: AUC 0.96, p < 0.001).

CONCLUSIONS

Normalized pulsed ASL performed with a 1.5 T scanner provides comparable results to DSC MRI perfusion in pediatric AT and may allow distinction between high- and low-grade AT.

[Non-contrast ASL perfusion in preoperative diagnosis of supratentorial gliomas]

The purpose of this study was to investigate the potential of pseudocontinuous arterial spin labeling perfusion (pCASL) in assessing the degree of malignancy of brain gliomas at the preoperative stage. MATERIAL AND METHODS: The study included 126 patients aged 12-75 years with supratentorial gliomas of different malignancy (35 low-grade gliomas and 91 high-grade gliomas). The maximum tumor blood flow (TBF) was measured, and the normalized tumor blood flow (nTBF) was calculated relative to the intact semiovale white matter of the contralateral hemisphere. The TBF and nTBF indicators differed significantly between low-grade and high-grade glioma groups (p<0.001). When using TBF and nTBF in the differential diagnosis of low-grade and high-grade gliomas, the area under the ROC curve was 0.96 in both cases. Our findings suggest that 3D pCASL perfusion is an effective technique for preoperative differential diagnosis of low-grade and high-grade gliomas. The study was supported by the Russian Foundation for Basic Research (grant #18-315-00384).

Differentiation of grade II/III and grade IV glioma by combining "T1 contrast-enhanced brain perfusion imaging" and susceptibility-weighted quantitative imaging

PURPOSE

MRI is a useful method for discriminating low- and high-grade glioma using perfusion MRI and susceptibility-weighted imaging (SWI). The purpose of this study is to evaluate the usefulness of T1-perfusion MRI and SWI in discriminating among grade II, III, and IV gliomas.

METHODS

T1-perfusion MRI was used to measure relative cerebral blood volume (rCBV) in 129 patients with glioma (70 grade IV, 33 grade III, and 26 grade II tumors). SWI was also used to measure the intratumoral susceptibility signal intensity (ITSS) scores for each tumor in these patients. rCBV and ITSS values were compared to seek differences between grade II vs. grade III, grade III vs. grade IV, and grade III+II vs. grade IV tumors.

RESULTS

Significant differences in rCBV values of the three grades of the tumors were noted and pairwise comparisons showed significantly higher rCBV values in grade IV tumors as compared to grade III tumors, and similarly increased rCBV was seen in the grade III tumors as compared to grade II tumors (p < 0.001). Grade IV gliomas showed significantly higher ITSS scores on SWI as compared to grade III tumors (p < 0.001) whereas insignificant difference was seen on comparing ITSS scores of grade III with grade II tumors. Combining the rCBV and ITSS resulted in significant improvement in the discrimination of grade III from grade IV tumors.

CONCLUSION

The combination of rCBV values derived from T1-perfusion MRI and SWI derived ITSS scores improves the diagnostic accuracy for discrimination of grade III from grade IV gliomas.

3D Pseudocontinuous Arterial Spin-Labeling MR Imaging in the Preoperative Evaluation of Gliomas

BACKGROUND AND PURPOSE

Previous studies showed conflicting results concerning the value of CBF maps obtained from arterial spin-labeling MR imaging in grading gliomas. This study was performed to investigate the effectiveness of CBF maps derived from 3D pseudocontinuous arterial spin-labeling in preoperatively assessing the grade, cellular proliferation, and prognosis of gliomas.

MATERIALS AND METHODS

Fifty-eight patients with pathologically confirmed gliomas underwent preoperative 3D pseudocontinuous arterial spin-labeling. The receiver operating characteristic curves for parameters to distinguish high-grade gliomas from low-grade gliomas were generated. Pearson correlation analysis was used to assess the correlation among parameters. Survival analysis was conducted with Cox regression.

RESULTS

Both maximum CBF and maximum relative CBF were significantly higher in high-grade gliomas than in low-grade gliomas (P < .001). The areas under the curve for maximum CBF and maximum relative CBF in distinguishing high-grade gliomas from low-grade gliomas were 0.828 and 0.863, respectively. Both maximum CBF and maximum relative CBF had no correlation with the Ki-67 index in all subjects and had a moderate negative correlation with the Ki-67 index in glioblastomas (r = -0.475, -0.534, respectively). After adjustment for age, a higher maximum CBF (P = .008) and higher maximum relative CBF (P = .005) were associated with worse progression-free survival in gliomas, while a higher maximum relative CBF (P = .033) was associated with better overall survival in glioblastomas.

CONCLUSIONS

3D pseudocontinuous arterial spin-labeling-derived CBF maps are effective in preoperative evaluation of gliomas. Although gliomas with a higher blood flow are more malignant, glioblastomas with a lower blood flow are likely to be more aggressive.

The Significance of Lactate and Lipid Peaks for Predicting Primary Neuroepithelial Tumor Grade with Proton MR Spectroscopy

Purpose:

¹H-MRS is a non-invasive technique used to assess the metabolic activity of brain tumors. The technique is useful for the preoperative prediction of tumor grade, which is important for treatment planning and accurate prognosis. We used ¹H-MRS to study the lactate peak, which appears in various conditions, including hyperglycemia, ischemia, and hypoxia and lipid peak, which is associated with necrotic cells. The purpose of this study was to retrospectively examine the frequency and significance of lactate and lipid peaks in relation to brain tumor grade.

Materials and Methods:

Fifty-five patients diagnosed with neuroepithelial tumors of Grades I (3 cases), II (11 cases), III (15 cases), and IV (26 cases) were enrolled. Biopsies were excluded. Single voxel (TE = 144 ms) point resolved ¹H-MRS spectroscopy sequences were retrospectively analyzed. An inverted doublet peak at 1.3 ppm was defined as lactate, a negative and positive peak was defined as combined lactate and lipid, and a clear upward peak was defined as lipid.

Results:

Lactate peaks were detected in all grades of brain tumors and were least common in Grade II tumors (9.1%). The frequency of combined lactate-lipid peaks was 0% (Grades I and II), 8.3% (Grade III), and 44% (Grade IV). Grade IV tumors were significantly different to the other grades. There were three cases with a lipid peak. All were glioblastoma.

Conclusions:

The presence of a lac peak may be useful to largely rule out the Grade II tumors, and allow the subsequent differentiation of Grade I tumors from Grade III or IV tumors by conventional imaging. The presence of a lipid peak may be associated with Grade IV tumors.

Brain Tumor Imaging

Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

Diagnostic accuracy of proton magnetic resonance spectroscopy and perfusion-weighted imaging in brain gliomas follow-up: a single institutional experience

Objectives The objective of this study was to evaluate whether proton magnetic resonance spectroscopy and perfusion magnetic resonance imaging (MRI) are able to increase diagnostic accuracy in the follow-up of brain gliomas, identifying the progression of disease before it becomes evident in the standard MRI; also to evaluate which of the two techniques has the best diagnostic accuracy. Methods Eighty-three patients with cerebral glioma (50 high-grade gliomas (HGGs), 33 low-grade gliomas (LGGs)) were retrospectively enrolled. All patients underwent standard MRI, H spectroscopic and perfusion echo-planar imaging MRI. For spectroscopy variations of choline/creatine, choline/N-acetyl-aspartate ratio, and lipids and lactates peak were considered. For perfusion 2.0 was considered the cerebral blood volume cut-off for progression. The combination of functional parameters gave a multiparametric score (0-2) to predict outcome. Diagnostic performance was determined by the receiver operating characteristic curve, with sensitivity, specificity, positive predictive and negative predictive values. Results In patients with LGGs a combined score of at least 1 was the best predictor for progression (odds ratio (OR) 3.91) with 8.4 months median anticipation of diagnosis compared to standard MRI. The individual advanced magnetic resonance technique did not show a diagnostic accuracy comparable to the combination of the two. Overall diagnostic accuracy area under the curve (AUC) was 0.881. In patients with HGGs the multiparametric score did not improve diagnostic accuracy significantly. Perfusion MRI was the best predictor of progression (OR 3.65), with 6.7 months median anticipation of diagnosis. Overall diagnostic accuracy AUC was 0.897. Then spectroscopy and perfusion MRI are able to identify tumour progression during follow-up earlier than standard MRI. Conclusion In patients with LGGs the combination of the functional parameters seems to be the best method for diagnosis of progression. In patients with HGGs perfusion is the best diagnostic method.

The roles of ING5 in gliomas: a good marker for tumorigenesis and a potential target for gene therapy

To elucidate the anti-tumor effects and molecular mechanisms of ING5 on glioma cells, we overexpressed it in U87 cells, and examined the phenotypes and their relevant molecules. It was found that ING5 overexpression suppressed proliferation, energy metabolism, migration, invasion, and induced G₂/M arrest, apoptosis, dedifferentiation, senescence, mesenchymal- epithelial transition and chemoresistance to cisplatin, MG132, paclitaxel and SAHA in U87 cells. There appeared a lower expression of N-cadherin, Twist, Slug, Zeb1, Zeb2, Snail, Ac-H3, Ac-H4, Cdc2, Cdk4 and XIAP, but a higher expression of Claudin 1, Histones 3 and 4, p21, p53, Bax, β-catenin, PI₃K, Akt, and p-Akt in ING5 transfectants. ING5 overexpression suppressed tumor growth of U87 cells in nude mice by inhibiting proliferation and inducing apoptosis. Down-regulated ING5 expression was closely linked to the tumorigenesis and histogenesis of glioma. These data indicated that ING5 expression might be considered as a good marker for the tumorigenesis and histogenesis of gliomas. It might be employed as a potential target for gene therapy of glioma. PI₃K/Akt or β-catenin/TCF-4 activation might be positively linked to chemotherapeutic resistance, mediated by ING5.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice.

Conjugation Magnetic PAEEP-PLLA Nanoparticles with Lactoferrin as a Specific Targeting MRI Contrast Agent for Detection of Brain Glioma in Rats

The diagnosis of malignant brain gliomas is largely based on magnetic resonance imaging (MRI) with contrast agents. In recent years, nano-sized contrast agents have been developed for improved MRI diagnosis. In this study, oleylamine-coated Fe3O4 magnetic nanoparticles (OAM-MNPs) were synthesized with thermal decomposition method and encapsulated in novel amphiphilic poly(aminoethyl ethylene phosphate)/poly(L-lactide) (PAEEP-PLLA) copolymer nanoparticles. The OAM-MNP-loaded PAEEP-PLLA nanoparticles (M-PAEEP-PLLA-NPs) were further conjugated with lactoferrin (Lf) for glioma tumor targeting. The Lf-conjugated M-PAEEP-PLLA-NPs (Lf-M-PAEEP-PLLA-NPs) were characterized by photon correlation spectroscopy (PCS), transmission electron microscopy (TEM), Fourier transform infrared (FTIR), thermo-gravimetric analysis (TGA), X-ray diffraction (XRD), and vibrating sample magnetometer (VSM). The average size of OAM-MNPs, M-PAEEP-PLLA-NPs, and Lf-M-PAEEP-PLLA-NPs were 8.6 ± 0.3, 165.7 ± 0.6, and 218.2 ± 0.4 nm, with polydispersity index (PDI) of 0.185 ± 0.023, 0.192 ± 0.021, and 0.224 ± 0.036, respectively. TEM imaging showed that OAM-MNPs were monodisperse and encapsulated in Lf-M-PAEEP-PLLA-NPs. TGA analysis showed that the content of iron oxide nanoparticles was 92.8 % in OAM-MNPs and 45.2 % in Lf-M-PAEEP-PLLA-NPs. VSM results indicated that both OAM-MNPs and Lf-M-PAEEP-PLLA-NPs were superparamagnetic, and the saturated magnetic intensity were 77.1 and 74.8 emu/g Fe. Lf-M-PAEEP-PLLA-NPs exhibited good biocompatibility in cytotoxicity assay. The high cellular uptake of Lf-M-PAEEP-PLLA-NPs in C6 cells indicated that Lf provided effective targeting for the brain tumor cells. The T 2 relaxation rate (r 2) of M-PAEEP-PLLA-NPs and Lf-M-PAEEP-PLLA-NPs were calculated to be 167.2 and 151.3 mM(-1) s(-1). In MRI on Wistar rat-bearing glioma tumor, significant contrast enhancement could clearly appear at 4 h after injection and last 48 h. Prussian blue staining of the section clearly showed the retention of Lf-M-PAEEP-PLLA-NPs in tumor tissues. The results from the in vitro and in vivo MRI indicated that Lf-M-PAEEP-PLLA-NPs possessed strong, long-lasting, tumor targeting, and enhanced tumor MRI contrast ability. Lf-M-PAEEP-PLLA-NPs represent a promising nano-sized MRI contrast agent for brain glioma targeting MRI.

Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme

PURPOSE

To build a framework for investigation of the associations between imaging, clinical target volumes (CTVs), and metabolic tumor volumes (MTVs) features for better understanding of the underlying information in the CTVs and dependencies between these volumes. High-throughput extraction of imaging and metabolomic quantitative features from magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging of glioblastoma multiforme (GBM) results in tens of variables per patient. In radiation therapy of GBM the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl aspartate (NAA) and choline (Cho). The corresponding clinical target volumes (CTVs) for radiation therapy are based on contrast-enhanced T1-weighted (CE-T1w) and T2-weighted (T2w)/fluid-attenuated inversion recovery MRI.

METHODS AND MATERIALS

Necrotic portions, enhancing lesion, and edema were manually contoured on CE-T1w/T2w images for 17 GBM patients. Clinical target volumes and MTVs for NAA (MTV_NAA) and Cho (MTV_Cho) were constructed. Imaging and metabolic features related to size, shape, and signal intensities of the volumes were extracted. Tumors were also scored categorically for 10 semantic imaging traits by a neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and CTVs/MTVs features were visualized as heatmaps. Associations between MTV_NAA and MTV_Cho and imaging features were studied using Spearman correlation.

RESULTS

Forty-eight imaging features were extracted per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. Twenty features were extracted from CTVs and MTVs. A series of semantic imaging traits were replaced with automatically extracted continuous variables. There were multiple (22) significant correlations of imaging measures with CTVs/MTV_NAA, whereas there were only 6 with CTVs/MTV_Cho.

CONCLUSIONS

A framework for investigation of codependencies between MRI and magnetic resonance spectroscopic imaging radiomic features and CTVs/MTVs has been established. The MTV for NAA was found to be closely associated with MRI volumes, whereas very few imaging features were related to MTV_Cho, indicating that Cho provides additional information to imaging.

Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival

MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally,both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features(no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imaging (PWI), diffusion weighted imaging(DWI) and spectroscopy. All the gliomas had histopathological diagnoses. Sensitivity and specificity of cMRI preoperative diagnosis were 78.5 and 38.5 %, respectively, and 85.7 and 53.8 % when a MRI was included, respectively. ROC analysis showed that cut-off values of 1.29 for maximum rCBV, 1.69 for minimum rADC, 2.1 for rCho/Cr ratio could differentiate between LGG and HGG with a sensitivity of 61.5, 53.8, and 53.8 % and a specificity of 54.7, 43 and 64.3 %, respectively. A significantly longer OS was observed in patients with a maximum rCBV<1.46 and minimum rADC>1.69 (80 vs 55 months, p = 0.01; 80 vs 51 months, p = 0.002, respectively). This result was also confirmed when cases were stratified according to pathology (LGG vs HGG). The ability of a MRI to differentiate between LGG and HGG and to predict survival improved as the number of a MRI techniques considered increased. In a selected population of suspected LGG,classification by cMRI underestimated the actual fraction of HGG. aMRI slightly increased the diagnostic accuracy compared to histopathology. However, DWI and PWI were prognostic markers independent of histological grade.

Detecting tumor progression in glioma: current standards and new techniques

INTRODUCTION

The post-treatment monitoring of glioma patients remains an area of active research and development. Conventional imaging with MRI is a highly sensitive modality for detecting and monitoring primary and secondary brain tumors and includes multi-parametric sequences to better characterize the disease. Standardized schemes for measuring response to treatment are in wide clinical use; however, the introduction of new therapeutics have introduced new patterns of response that can confound interpretation of conventional MRI and can cause uncertainty in the proper management following therapy. Areas covered: A summary of current and evolving techniques for assessing glioma response in this era of new therapies that address these challenges are presented in this review. While this review focuses more on clinical and early clinical methodologies for MRI and nuclear medicine techniques some promising pre-clinical techniques are also presented. Expert commentary: While successful single institution results have been widely reported in the literature, any new methodologies must be undertaken in multi-center settings. Additionally, the need for standardization of protocols in quantitative measured are an important area that must be addressed for new and promising techniques to be implemented to a wide array of patients.

Advanced MR imaging in hemispheric low-grade gliomas before surgery; the indications and limits in the pediatric age

INTRODUCTION

Advanced magnetic resonance imaging (MRI) techniques is an umbrella term that includes diffusion (DWI) and diffusion tensor (DTI), perfusion (PWI), spectroscopy (MRS), and functional (fMRI) imaging. These advanced modalities have improved the imaging of brain tumors and provided valuable additional information for treatment planning. Despite abundant literature on advanced MRI techniques in adult brain tumors, few reports exist for pediatric brain ones, potentially because of technical challenges.

REVIEW OF THE LITERATURE

The authors review techniques and clinical applications of DWI, PWI, MRS, and fMRI, in the setting of pediatric hemispheric low-grade gliomas.

PERSONAL EXPERIENCE

The authors propose their personal experience to highlight benefits and limits of advanced MR imaging in diagnosis, grading, and presurgical planning of pediatric hemispheric low-grade gliomas.

DISCUSSION

Advanced techniques should be used as complementary tools to conventional MRI, and in theory, the combined use of the three techniques should ensure achieving the best results in the diagnosis of hemispheric low-grade glioma and in presurgical planning to maximize tumor resection and preserve brain function.

FUTURE PERSPECTIVES

In the setting of pediatric neurooncology, these techniques can be used to distinguish low-grade from high-grade tumor. However, these methods have to be applied on a large scale to understand their real potential and clinical relapse, and further technical development is required to reduce the excessive scan times and other technical limitations.

Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: Single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging

The accurate identification of glioblastoma progression remains an unmet clinical need. The aim of this prospective single-institutional study is to determine and validate thresholds for the main metabolite concentrations obtained by MR spectroscopy (MRS) and the values of the apparent diffusion coefficient (ADC) to enable distinguishing tumor recurrence from pseudoprogression. Thirty-nine patients after the standard treatment of a glioblastoma underwent advanced imaging by MRS and ADC at the time of suspected recurrence — median time to progression was 6.7 months. The highest significant sensitivity and specificity to call the glioblastoma recurrence was observed for the total choline (tCho) to total N-acetylaspartate (tNAA) concentration ratio with the threshold ≥ 1.3 (sensitivity 100.0% and specificity 94.7%). The ADCmean value higher than 1313 × 10^− 6 mm²/s was associated with the pseudoprogression (sensitivity 98.3%, specificity 100.0%). The combination of MRS focused on the tCho/tNAA concentration ratio and the ADCmean value represents imaging methods applicable to early non-invasive differentiation between a glioblastoma recurrence and a pseudoprogression. However, the institutional definition and validation of thresholds for differential diagnostics is needed for the elimination of setup errors before implementation of these multimodal imaging techniques into clinical practice, as well as into clinical trials.

•

For an effective salvage treatment, an accurate diagnosis of GBM recurrence is essential.

•

The standard structural MRI has limited sensitivity and specificity to distinguish GBM progression.

•

GBM recurrence is characterized by the ADCmean value ≤ 1313 × 10^− 6 mm²/s and the tCho/tNAA ratio ≥ 1.3.

•

An institutional definition of thresholds is needed, if advanced imaging should be used accurately in clinical practice.

Increased diffusion in the normal appearing white matter of brain tumor patients: is this just tumor infiltration?

Altered diffusion in the normal appearing white matter (NAWM) of glioma patients has been explained by tumor infiltration. The goal of the present study was to test this explanation indirectly by examining whether these alterations were also present in the contralateral NAWM of non-infiltrative tumors like meningiomas; and to search for other possible reasons for this abnormality. Twenty-seven patients with histologically verified glioma (grade II-IV), 22 meningioma patients and two groups of age- and sex-matched healthy controls underwent diffusion weighted imaging (DWI) on a 3T MR. All patients were examined before treatment. Apparent diffusion coefficient (ADC) values were calculated in the entire NAWM of the hemisphere contralateral to the tumor. ADC values of the NAWM were compared between groups with Mann-Whitney U-test and multiple linear regression. The relations of ADC in NAWM to glioma grade and to tumor volume were also investigated. ADC values of the contralateral NAWM were significantly higher in both glioma and meningioma patients compared to controls (P = 0.0006 and 0.0099, respectively). ADC value was higher in the NAWM of high grade gliomas than in low grade gliomas (P = 0.0181) and in healthy control subjects (P = 0.0003). ADC did not depend on tumor volume in any of the patient groups. Elevated ADC in the NAWM of both glioma and meningioma patients might indicate that the effect of infiltrating tumor cells is not the only reason for the alteration as it has been previously suggested. Although the role of mass effect was not proved, other mechanisms might also contribute to ADC elevation.

The diagnostic performance of magnetic resonance spectroscopy in differentiating high-from low-grade gliomas: A systematic review and meta-analysis

OBJECTIVE

Magnetic resonance spectroscopy (MRS) is a powerful tool for preoperative grading of gliomas. We performed a meta-analysis to evaluate the diagnostic performance of MRS in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs).

METHODS

PubMed and Embase databases were systematically searched for relevant studies of glioma grading assessed by MRS through 27 March 2015. Based on the data from eligible studies, pooled sensitivity, specificity, diagnostic odds ratio and areas under summary receiver operating characteristic curve (SROC) of different metabolite ratios were obtained.

RESULTS

Thirty articles comprising a total sample size of 1228 patients were included in our meta-analysis. Quantitative synthesis of studies showed that the pooled sensitivity/specificity of Cho/Cr, Cho/NAA and NAA/Cr ratios was 0.75/0.60, 0.80/0.76 and 0.71/0.70, respectively. The area under the curve (AUC) of the SROC was 0.83, 0.87 and 0.78, respectively.

CONCLUSIONS

MRS demonstrated moderate diagnostic performance in distinguishing HGGs from LGGs using tumoural metabolite ratios including Cho/Cr, Cho/NAA and NAA/Cr. Although there was no significant difference in AUC between Cho/Cr and Cho/NAA groups, Cho/NAA ratio showed higher sensitivity and specificity than Cho/Cr ratio and NAA/Cr ratio. We suggest that MRS should combine other advanced imaging techniques to improve diagnostic accuracy in differentiating HGGs from LGGs.

KEY POINTS

• MRS has moderate diagnostic performance in distinguishing HGGs from LGGs. • There is no significant difference in AUC between Cho/Cr and Cho/NAA ratios. • Cho/NAA ratio is superior to NAA/Cr ratio. • Cho/NAA ratio shows higher sensitivity and specificity than Cho/Cr and NAA/Cr ratios. • MRS should combine other advanced imaging techniques to improve diagnostic accuracy.

Comparison of the Effect of Vessel Size Imaging and Cerebral Blood Volume Derived from Perfusion MR Imaging on Glioma Grading

BACKGROUND AND PURPOSE

Vascular proliferation is a major criterion for grading gliomas on the basis of histology. Relative cerebral blood volume can provide pathophysiologic information about glioma grading. Vessel size imaging, in some animals, can be used to estimate the microvascular caliber of a glioma, but its clinical use remains unclear. Herein, we aimed to compare the predictive power of relative cerebral blood volume and vessel size imaging in glioma grading, with grading based on histology.

MATERIALS AND METHODS

Seventy patients with glioma participated in the study; 30 patients underwent MR perfusion imaging with a spin-echo sequence and vessel size imaging with a gradient-echo and spin-echo sequence successively at 24-hour intervals before surgery. We analyzed the vessel size imaging values and relative cerebral blood volume of differently graded gliomas. The microvessel parameters were histologically evaluated and compared with those on MR imaging. The cutoff values of vessel size imaging and relative cerebral blood volume obtained from receiver operating characteristic curve analyses were used to predict glioma grading in another 40 patients.

RESULTS

Vessel size imaging values and relative cerebral blood volume were both increased in high-grade gliomas compared with low-grade gliomas (P < .01). Moreover, vessel size imaging values had higher specificity and sensitivity in differentiating high-grade from low-grade gliomas compared with relative cerebral blood volume. In addition, a significant correlation was observed between vessel size imaging values and microvessel diameters (r > 0.8, P < .05) and between relative cerebral blood volume and microvessel area (r = 0.6579, P < .05). Most important, the use of vessel size imaging cutoff values to predict glioma grading was more accurate (100%) than use of relative cerebral blood volume (85%) values.

CONCLUSIONS

Vessel size imaging can provide more accurate information on glioma grading and may serve as an effective biomarker for the prognosis of patients with gliomas.

The Diagnostic Ability of Follow-Up Imaging Biomarkers after Treatment of Glioblastoma in the Temozolomide Era: Implications from Proton MR Spectroscopy and Apparent Diffusion Coefficient Mapping

Objective. To prospectively determine institutional cut-off values of apparent diffusion coefficients (ADCs) and concentration of tissue metabolites measured by MR spectroscopy (MRS) for early differentiation between glioblastoma (GBM) relapse and treatment-related changes after standard treatment. Materials and Methods. Twenty-four GBM patients who received gross total resection and standard adjuvant therapy underwent MRI examination focusing on the enhancing region suspected of tumor recurrence. ADC maps, concentrations of N-acetylaspartate, choline, creatine, lipids, and lactate, and metabolite ratios were determined. Final diagnosis as determined by biopsy or follow-up imaging was correlated to the results of advanced MRI findings. Results. Eighteen (75%) and 6 (25%) patients developed tumor recurrence and pseudoprogression, respectively. Mean time to radiographic progression from the end of chemoradiotherapy was 5.8 ± 5.6 months. Significant differences in ADC and MRS data were observed between those with progression and pseudoprogression. Recurrence was characterized by N-acetylaspartate ≤ 1.5 mM, choline/N-acetylaspartate ≥ 1.4 (sensitivity 100%, specificity 91.7%), N-acetylaspartate/creatine ≤ 0.7, and ADC ≤ 1300 × 10⁻⁶ mm²/s (sensitivity 100%, specificity 100%). Conclusion. Institutional validation of cut-off values obtained from advanced MRI methods is warranted not only for diagnosis of GBM recurrence, but also as enrollment criteria in salvage clinical trials and for reporting of outcomes of initial treatment.

Repeatability of Standardized and Normalized Relative CBV in Patients with Newly Diagnosed Glioblastoma

BACKGROUND AND PURPOSE

For more widespread clinical use advanced imaging methods such as relative cerebral blood volume must be both accurate and repeatable. The aim of this study was to determine the repeatability of relative CBV measurements in newly diagnosed glioblastoma multiforme by using several of the most commonly published estimation techniques.

MATERIALS AND METHODS

The relative CBV estimates were calculated from dynamic susceptibility contrast MR imaging in double-baseline examinations for 33 patients with treatment-naïve and pathologically proved glioblastoma multiforme (men = 20; mean age = 55 years). Normalized and standardized relative CBV were calculated by using 6 common postprocessing methods. The repeatability of both normalized and standardized relative CBV, in both tumor and contralateral brain, was examined for each method with metrics of repeatability, including the repeatability coefficient and within-subject coefficient of variation. The minimum sample size required to detect a parameter change of 10% or 20% was also determined for both normalized relative CBV and standardized relative CBV for each estimation method.

RESULTS

When ordered by the repeatability coefficient, methods using postprocessing leakage correction and ΔR2*(t) techniques offered superior repeatability. Across processing techniques, the standardized relative CBV repeatability in normal-appearing brain was comparable with that in tumor (P = .31), yet inferior in tumor for normalized relative CBV (P = .03). On the basis of the within-subject coefficient of variation, tumor standardized relative CBV estimates were less variable (13%-20%) than normalized relative CBV estimates (24%-67%). The minimum number of participants needed to detect a change of 10% or 20% is 118-643 or 30-161 for normalized relative CBV and 109-215 or 28-54 for standardized relative CBV.

CONCLUSIONS

The ΔR2* estimation methods that incorporate leakage correction offer the best repeatability for relative CBV, with standardized relative CBV being less variable and requiring fewer participants to detect a change compared with normalized relative CBV.

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

AIM

To evaluate the sensitivity and specificity of single-dose dynamic contrast-enhanced (DCE) perfusion magnetic resonance imaging (MRI) in prospective evaluation of glioma grading and to correlate the relative cerebral blood volume (rCBV) values with mitotic and ki-67 indexes obtained at histopathology.

MATERIALS AND METHODS

A total of 53 histologically proven patients with glioma were included in this study. DCE-MRI perfusion with a single dose of contrast medium was included in brain tumour protocol and prospective grading of glioma into low and high grade was done based on a previously reported rCBV cut-off value of 3. Tumours with rCBV ≥ 3 were considered to be high grade and rCBV < 3 were considered to be low grade. The sensitivity and specificity of the cut-off value were estimated. Ki-67 and mitotic indexes were also obtained on histopathological analysis along with histological grading.

RESULTS

Based on pre-defined rCBV cut-off values, prospective grading of low- and high-grade glioma was achieved with a sensitivity and specificity of 97.22% and 100%, respectively. Significant correlation was found between the mitotic/ki-67 indexes and rCBV values when data for high- and low-grade tumours was combined.

CONCLUSION

DCE-MRI performed with a single dose of contrast medium is as effective as a protocol with a double-dose of contrast medium for glioma grading using 3 T MRI and could be added to the routine evaluation protocol of brain tumours.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

Accelerated five-dimensional echo planar J-resolved spectroscopic imaging: Implementation and pilot validation in human brain

PURPOSE

To implement an accelerated five-dimensional (5D) echo-planar J-resolved spectroscopic imaging sequence combining 3 spatial and 2 spectral encoding dimensions and to apply the sequence in human brain.

METHODS

An echo planar readout was used to acquire a single spatial and a single spectral dimension during one readout. Nonuniform sampling was applied to the two phase-encoded spatial directions and the indirect spectral dimension. Nonlinear reconstruction was used to minimize the ℓ1-norm or the total variation and included a spectral mask to enhance sparsity. Retrospective reconstructions at multiple undersamplings were performed in phantom. Ten healthy volunteers were scanned with 8× undersampling and compared to a fully sampled single slice scan.

RESULTS

Retrospective reconstruction of fully sampled phantom data showed excellent quality at 4×, 8×, 12×, and 16× undersampling using either reconstruction method. Reconstruction of prospectively acquired in vivo scans with 8× undersampling showed excellent quality in the occipito-parietal lobes and good quality in the frontal lobe, consistent with the fully sampled single slice scan.

CONCLUSION

By utilizing nonuniform sampling with nonlinear reconstruction, 2D J-resolved spectra can be acquired over a 3D spatial volume with a total scan time of 20 min, which is reasonable for in vivo studies.

[Molecular breast imaging. An update]

CLINICAL/METHODICAL ISSUE

The aim of molecular imaging is to visualize and quantify biological, physiological and pathological processes at cellular and molecular levels. Molecular imaging using various techniques has recently become established in breast imaging.

STANDARD RADIOLOGICAL METHODS

Currently molecular imaging techniques comprise multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), proton MR spectroscopy ((1)H-MRSI), nuclear imaging by breast-specific gamma imaging (BSGI), positron emission tomography (PET) and positron emission mammography (PEM) and combinations of techniques (e.g. PET-CT and multiparametric PET-MRI).

METHODICAL INNOVATIONS

Recently, novel techniques for molecular imaging of breast tumors, such as sodium imaging ((23)Na-MRI), phosphorus spectroscopy ((31)P-MRSI) and hyperpolarized MRI as well as specific radiotracers have been developed and are currently under investigation.

PRACTICAL RECOMMENDATIONS

It can be expected that molecular imaging of breast tumors will enable a simultaneous assessment of the multiple metabolic and molecular processes involved in cancer development and thus an improved detection, characterization, staging and monitoring of response to treatment will become possible.

Multivendor implementation and comparison of volumetric whole-brain echo-planar MR spectroscopic imaging

PURPOSE

To assess volumetric proton MR spectroscopic imaging (MRSI) of the human brain on multivendor MRI instruments.

METHODS

Echo-planar spectroscopic imaging was developed on instruments from three manufacturers, with matched specifications and acquisition protocols that accounted for differences in sampling performance, radiofrequency (RF) power, and data formats. Intersite reproducibility was evaluated for signal-normalized maps of N-acetylaspartate (NAA), creatine (Cre), and choline using phantom and human subject measurements. Comparative analyses included metrics for spectral quality, spatial coverage, and mean values in atlas-registered brain regions.

RESULTS

Intersite differences for phantom measurements were less than 1.7% for individual metabolites and less than 0.2% for ratio measurements. Spatial uniformity ranged from 79% to 91%. The human studies found differences of mean values in the temporal lobe, but good agreement in other white matter regions, with maximum differences relative to their mean of under 3.2%. For NAA/Cre, the maximum difference was 1.8%. In gray matter, a significant difference was observed for frontal lobe NAA. Primary causes of intersite differences were attributed to shim quality, B0 drift, and accuracy of RF excitation. Correlation coefficients for measurements at each site were over 0.60, indicating good reliability.

CONCLUSION

A volumetric intensity-normalized MRSI acquisition can be implemented in a comparable manner across multivendor MR instruments.

Potential role of fractional anisotropy derived from diffusion tensor imaging in differentiating high-grade gliomas from low-grade gliomas: a meta-analysis

BACKGROUND AND PURPOSE

It is crucial to accurately differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) preoperatively, as treatment strategies vary. So we performed a meta-analysis to assess the sensitivity and specificity of fractional anisotropy (FA) value derived from diffusion tensor imaging (DTI) in differentiating HGGs from LGGs.

MATERIALS AND METHODS

Between January 2005 and June 2014, relevant articles were searched from the Embase and Medline databases for analysis. Statistical analyses were performed using Meta-Disc 1.4.

RESULTS

A total of 221 patients included in the FA analysis: 127 with HGGs and 94 LGGs. The pooled sensitivity, specificity and diagnostic odds ratio (DOR) for differentiating HGGs from LGGs were 93% (95% CI 0.87-0.97), 85% (95% CI 0.76-0.92), and 55.41 (95% CI 16.77-183.07), respectively. And computation of heterogeneity metrics revealed an acceptable level of the between-study heterogeneity of DOR (I(2)=30.9%).

CONCLUSIONS

The results of our meta-analysis present that the FA derived from DTI act as a useful diagnostic marker could be used in distinguishing the HGGs from LGGs in the preoperative and the clinical application values are to be confirmed by further larger case-control studies.