Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors

Magnetic resonance spectroscopy for enhanced multiparametric MRI characterization of [18F]FET PET-negative gliomas

BACKGROUND

Approximately 30-36% of gliomas presented with [18F]fluoroethyl-L-tyrosine ([18F]FET) PET-negative at primary diagnosis, which interferes with the differentiation of gliomas from other isolated brain lesions. Preoperative noninvasive identification of [18F]FET PET-negative gliomas to aggressive surgical treatment could reduce ineffective treatment and improve prognosis. This study aimed to assess the potential utility of multiparametric MRI with 1H-magnetic resonance spectroscopy (1H-MRS) in the diagnosis of gliomas within [18F]FET PET-negative isolated cerebral lesions.

RESULTS

A total of 51 patients (mean age 44.35 ± 27.15 years, 26 males) with 37 gliomas and 14 non-gliomas were recruited for the study. More than half of PET-negative gliomas presented T2-FLAIR mismatch sign, whereas non-gliomas were more likely to present absence of T2-FLAIR mismatch sign (54.05% vs. 7.14%, p < 0.001). Choline to creatine (Cho/Cr) ratios in gliomas were significantly higher than those in non-gliomas (2.21 vs. 1.30, p < 0.001). Multiparametric MRI (AUC = 0.88) outperformed conventional MRI (AUC = 0.72) in differentiating gliomas from non-gliomas (NRI = 0.29, p = 0.02). And WHO grade was correlated with Cho/Cr and total lesion tracer standardized uptake (TLU) (r = 0.43 and 0.55; p = 0.007 and < 0.001; respectively). Low-grade PET-negative gliomas exhibit low levels of both TLU and Cho/Cr, but the distribution of TLU and Cho/Cr is more variable in high-grade gliomas. Furthermore, there was a moderated correlation between TLU and Cho/Cr in low-grade PET-negative gliomas (r = 0.54, p = 0.017), whereas there was no correlation in the high-grade PET-negative gliomas (r = -0.017, p = 0.95).

CONCLUSION

Multiparametric MRI with 1H-MRS demonstrates significant promise in enhancing the diagnosis and overall clinical management for [18F]FET PET-negative gliomas. Moreover, the correlation between TLU and Cho/Cr that was affected by tumor grading of 2021 WHO criteria provides a rationale for further research into the mechanisms of reduced [18F]FET uptake in gliomas.

Cerebral blood flow in attention deficit hyperactivity disorder: A systematic review

BACKGROUND AND OBJECTIVES

Attention deficit hyperactivity disorder (ADHD) is one of the most frequent and disabling neurodevelopmental disorders. Recent research on cerebral blood flow (CBF) has enhanced understanding of the underlying pathophysiology in neuropsychiatric disorders. This systematic review aims to synthesize the existing literature on CBF anomalies among individuals with ADHD in comparison to controls.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, a systematic literature search was conducted using PubMed, PsycInfo, and Web of Science to identify relevant studies on CBF in ADHD.

RESULTS

Twenty studies, encompassing a total of 1652 participants with ADHD and 580 controls, were included, employing measurements from SPECT (n = 9), ASL (n = 6), PET (n = 4), and BOLD-derived quantitative maps (n = 1). In individuals with ADHD during resting state, hypoperfusion was frequently observed in the right orbitofrontal gyrus, temporal cortex, basal ganglia and putamen. Conversely, hyperperfusion was noted in frontal lobes, left postcentral gyrus, and occipital lobes. During cognitive tasks, hyperperfusion was observed in frontal areas, temporal regions, cingulate cortex and the precuneus. Furthermore, the administration of methylphenidate was associated with increased CBF in striatal and posterior periventricular regions, the right thalamus, and the precentral gyrus.

CONCLUSION

This review highlights diverse CBF anomalies in ADHD. The most consistently reported findings suggest hypoperfusion during resting state in prefrontal and temporal areas, along with the basal ganglia, while there is a hyperperfusion in frontal, parietal and occipital regions. Further research, including longitudinal studies, is essential to develop a comprehensive understanding of CBF implications in ADHD.

The cortical high-flow sign in oligodendroglioma, IDH-mutant and 1p/19q-codeleted is correlated with histological cortical vascular density

BACKGROUND AND PURPOSE

The cortical high-flow sign has been more commonly reported in oligodendroglioma, IDH-mutant and 1p/19q-codeleted (ODG IDHm-codel) compared to diffuse glioma with IDH-wildtype or astrocytoma, IDH-mutant. Besides tumor types, higher grades of glioma might also contribute to the cortical high flow. Therefore, we investigated whether the histological cortical vascular density or CNS WHO grade was associated with the cortical high-flow sign in patients with ODG IDHm-codel.

MATERIALS AND METHODS

This retrospective study consisted of pathologically confirmed 25 adult patients with ODG IDHm-codel. We implemented pseudo-continuous arterial spin labeling technique with background suppression. Subtraction images were generated from paired control and label images. Tumor-affecting cortices without intense contrast enhancement on conventional MR imaging were targeted for the determination of the cortical high-flow sign. Immunohistochemical staining of CD31 antibody was performed for the identification of vascular endothelial cells. A microscopic field of the most intense vascularization was captured in each specimen. The vessel number and the relative vascular density (%Vessel) were compared between the positive cortical high-flow sign (CHFS+) and the negative cortical high-flow sign (CHFS-) groups using the Mann-Whitney U test. Second, Fisher's exact test was used to compare the difference between the presence or absence of cortical high-flow sign and CNS WHO grades. Finally, the vessel number and %Vessel were compared between the CNS WHO grade 2 and grade 3 using the Mann-Whitney U test.

RESULTS

The vessel number and %Vessel were higher in patients with the CHFS+ group than in patients with CHFS- group (p = 0.016 and p = 0.005, respectively). We observed no significant differences (p = 1.00) in the frequency of cortical high-flow sign between the CNS WHO grade 2 and grade 3. In addition, no significant differences are found in the vessel number and %Vessel between the CNS WHO grade 2 and grade 3 (p = 0.121 and p = 0.475, respectively).

CONCLUSION

The cortical high-flow sign on ASL, which is more commonly found in ODG IDHm-codel than in diffuse glioma with IDH-wildtype or astrocytoma, is associated with the histological cortical vascular density in patients with ODG IDHm-codel.

Bench-to-bedside imaging in brain metastases: a road to precision oncology

Radiology has seen tremendous evolution in the last few decades. At the same time, oncology has made great strides in diagnosing and treating cancer. Distant metastases of neoplasms are being encountered more often in light of longer patient survival due to better therapeutic strategies and diagnostic methods. Brain metastasis (BM) is a dismal manifestation of systemic cancer. In the present scenario, magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are playing a big role in providing molecular information about cancer. Lately, molecular imaging has emerged as a stirring arena of dynamic imaging techniques that have enabled clinicians and scientists to noninvasively visualize and understand biological processes at the cellular and molecular levels. This knowledge has impacted etiopathogenesis, detection, personalized treatment, drug development, and our understanding of carcinogenesis. This article offers insight into the molecular biology underlying brain metastasis, its pathogenesis, imaging protocols, and algorithms. It also discusses disease-specific molecular imaging features, focusing on common tumors that spread to the brain, such as lung, breast, colorectal cancer, melanoma, and renal cell carcinoma. Additionally, it covers various targeted treatment options, criteria for assessing treatment response, and the role of artificial intelligence in diagnosing, managing, and predicting prognosis for patients with brain metastases.

A Prospective Study on Perfusion MRI Changes in Intracranial Meningiomas Following Gamma Knife Therapy

BACKGROUND

Radiosurgery plays an important role as a treatment modality for intracranial meningiomas. Perfusion MR imaging can be performed by using arterial spin-labeling (ASL) which is a relatively new and advanced technique.

OBJECTIVES

To assess the changes in perfusion parameters on ASL perfusion MRI in intracranial meningioma after radiosurgery and correlate with histopathological grade of meningioma.

MATERIALS AND METHODS

In this Prospective study done at the our institute over a period of 20 months (Jan 2016-Aug 2017), patients with intracranial meningiomas had perfusion MRI with ASL sequence on GE Optima 450W®, 1.5T MRI (GE Medical Systems) prior to GKT and at 6 months after GKT were included in the study.

RESULTS

Twenty-seven patients were included in this study. Mean cerebral blood flow (CBF) was higher in angiomatous meningiomas. Though mean values of average CBF, maximum, minimum, and SD derived from the ASL MR perfusion were relatively higher in post GKT group as compared to those obtained in pre-GKT but it was not clinically significant. Mean baseline volume of whole cohort was 5.71 cm3 and decreased significantly post GKT in a follow up of 6 months to 5.59 cm3 (P value 0.0018). On comparing volumes of primary and secondary group, volumes were not found be significantly decreased in primary group (P value = 0.1361), 0.1361), but significantly reduced in secondary group (7.13 vs 7.034 cm3) (P value of = 0.0038).

CONCLUSION

Our preliminary observations support ASL as a sensitive MRI sequence for the evaluation of meningioma perfusion patterns.

Distinguishment between high-grade gliomas and solitary brain metastases in peritumoural oedema: quantitative analysis using synthetic MRI at 3 T

AIM

To investigate the efficacy of synthetic magnetic resonance imaging (MRI) in distinguishing high-grade gliomas (HGGs) from solitary brain metastases (SBMs) in peritumoural oedema.

MATERIALS AND METHODS

Thirty-five patients with HGGs and 25 patients with SBMs were recruited and scanned using synthetic MRI using a 3 T scanner. Two radiologists measured synthetic MRI-derived relaxation values independently (T1, T2, proton density [PD]) in the peritumoural oedema, which was used to generate quantitative metrics before (T1native, T2native, and PDnative) and after (T1post, T2post, and PDpost) contrast agent injection. Student's t-test or the Mann-Whitney U-test was performed to detect statistically significant differences in the aforementioned metrics in peritumoural oedema between HGGs and SBMs. The receiver operating characteristic (ROC) curves were plotted to evaluate the efficacy of each metric in distinguishing the two groups, and the areas under the curves (AUCs) were compared pairwise by performing the Delong test.

RESULTS

The mean T1native, T2native, and T1post values in the peritumoural oedema of HGGs were significantly lower compared with SBMs (all p<0.05). The T1post value had a higher AUC (0.843) in differentiating HGGs and SBMs than all other individual metrics (all p<0.05). The combined T1native, T2native, and T1post model had the best distinguishing performance with an AUC, sensitivity, and specificity of 0.987, 94.3%, and 100%, respectively.

CONCLUSIONS

Synthetic MRI may be a potential supplement to the preoperative diagnosis of HGGs and SBMs in clinical practice, as the synthetic MRI-derived tri-parametric model in the peritumoural oedema showed significantly improved diagnostic performance in distinguishing HGGs from SBMs.

The cortical high-flow sign of oligodendroglioma, IDH-mutant and 1p/19q-codeleted: comparison between arterial spin labeling and dynamic susceptibility contrast methods

PURPOSE

The cortical high-flow sign with the non-enhancing area was reportedly found to be more frequent with oligodendroglioma, IDH-mutant and 1p/19q codeleted (ODG IDHm-codel) than with IDH-wildtype or astrocytoma, IDH-mutant on arterial spin labeling (ASL) in diffuse gliomas. This study aimed to compare the identification rate of the cortical high-flow sign on ASL in patients with ODG IDHm-codel to that on dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI).

METHODS

Participants consisted of 32 adult ODG IDHm-codel patients with pathologically confirmed. Subtraction images were generated from paired control and label images on ASL. For DSC, dynamic T2*-weighted perfusion weighted images were obtained after pre-bolus of gadolinium-based contrast agent. Regional cerebral blood flow/volume maps were generated based on the concentration-time curve and arterial input function. Tumor-affecting cortices without contrast enhancement on conventional MR imaging were targeted. The identification rate of the cortical high-flow sign was compared between ASL and DSC using the Pearson's Chi-Square test.

RESULTS

Frequency of the cortical high-flow sign was significantly higher on ASL (18/32, 56.3%; p < 0.001) than on DSC (5/32, 15.6%). All cases with the positive cortical high-flow sign on DSC were identified on ASL.

CONCLUSION

ASL effectively identifies the cortical high-flow sign in ODG IDHm-codel, surpassing DSC in identification rates.

Arterial Spin Labeling and Amide Proton Transfer Imaging can Differentiate Glioblastoma from Brain Metastasis: A Systematic Review and Meta-Analysis

BACKGROUND

Currently, arterial spin labeling (ASL) and amide proton transfer (APT) imaging have shown potential for distinguishing glioblastoma from brain metastases. Thus, a meta-analysis was conducted to investigate this further.

METHODS

An extensive and comprehensive search was conducted in 6 English and Chinese databases according to predefined inclusion and exclusion criteria, encompassing data up to July 2023. Data from eligible literature were extracted, and bivariate models were employed to calculate pooled sensitivities, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve.

RESULTS

The meta-analysis included 11 articles. For ASL, the pooled sensitivity was 0.77 (95% confidence interval [CI], 0.63-0.87), and the pooled specificity was 0.87 (95% CI, 0.77-0.93). The pooled PLR was 5.89 (95% CI, 2.97-11.69), the pooled NLR was 0.26 (95% CI, 0.15-0.47), the pooled DOR was 22.33 (95% CI, 6.89-72.34), and AUC was 0.90 (95% CI, 0.87-0.92). For APT imaging, the pooled sensitivity was 0.78 (95% CI, 0.70-0.85), and the pooled specificity was 0.86 (95% CI, 0.77-0.92). The pooled PLR was 5.51 (95% CI, 3.24-9.37), the pooled NLR was 0.25 (95% CI, 0.17-0.37), the pooled DOR was 21.99 (95% CI, 10.28-47.03), and the AUC was 0.90 (95% CI, 0.87-0.92).

CONCLUSIONS

This meta-analysis suggest that both ASL and APT imaging exhibit high accuracy in distinguishing between glioblastoma and brain metastasis.

Diffuse glioma molecular profiling with arterial spin labeling and dynamic susceptibility contrast perfusion MRI: A comparative study

Background

Evaluation of molecular markers (IDH, pTERT, 1p/19q codeletion, and MGMT) in adult diffuse gliomas is crucial for accurate diagnosis and optimal treatment planning. Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labeling (ASL) perfusion MRI techniques have both shown good performance in classifying molecular markers, however, their performance has not been compared side-by-side.

Methods

Pretreatment MRI data from 90 patients diagnosed with diffuse glioma (54 men/36 female, 53.1 ± 15.5 years, grades 2-4) were retrospectively analyzed. DSC-derived normalized cerebral blood flow/volume (nCBF/nCBV) and ASL-derived nCBF in tumor and perifocal edema were analyzed in patients with available IDH-mutation (n = 67), pTERT-mutation (n = 39), 1p/19q codeletion (n = 33), and MGMT promoter methylation (n = 31) status. Cross-validated uni- and multivariate logistic regression models assessed perfusion parameters' performance in molecular marker detection.

Results

ASL and DSC perfusion parameters in tumor and edema distinguished IDH-wildtype (wt) and pTERT-wt tumors from mutated ones. Univariate classification performance was comparable for ASL-nCBF and DSC-nCBV in IDH (maximum AUROCC 0.82 and 0.83, respectively) and pTERT (maximum AUROCC 0.70 and 0.81, respectively) status differentiation. The multivariate approach improved IDH (DSC-nCBV AUROCC 0.89) and pTERT (ASL-nCBF AUROCC 0.8 and DSC-nCBV AUROCC 0.86) classification. However, ASL and DSC parameters could not differentiate 1p/19q codeletion or MGMT promoter methylation status. Positive correlations were found between ASL-nCBF and DSC-nCBV/-nCBF in tumor and edema.

Conclusions

ASL is a viable gadolinium-free replacement for DSC for molecular characterization of adult diffuse gliomas.

Experimenting with ASL-based arterialized cerebral blood volume as a novel imaging biomarker in grading glial neoplasms

BACKGROUND

Perfusion imaging is one of the methods used to grade glial neoplasms, and in this study we evaluated the role of ASL perfusion in grading brain glioma.

PURPOSE

The aim is to evaluate the role of arterialized cerebral blood volume (aCBV) of multi-delay ASL perfusion for grading glial neoplasm.

MATERIALS AND METHODS

This study is a prospective observational study of 56 patients with glial neoplasms of the brain who underwent surgery, and only cases with positive diagnosis of glioma are included to evaluate the novel diagnostic parameter.

RESULTS

In the study, ASL-derived normalized aCBV (naCBV) and T2*DSC-derived normalized CBV (nCBV) are showing very high correlation (Pearson's correlation coefficient value of 0.94) in grading glial neoplasms. naCBV and nCBF are also showing very high correlation (Pearson's correlation coefficient value of 0.876). The study also provides cutoff values for differentiating LGG from HGG for normalized aCBV(naCBV) of ASL, normalized CBV (nCBV), and normalized nCBF derived from T2* DCS as 1.12, 1.254, and 1.31, respectively. ASL-derived aCBV also shows better diagnostic accuracy than ASL-derived CBF.

CONCLUSION

This study is one of its kind to the best of our knowledge where multi-delay ASL perfusion-derived aCBV is used as a novel imaging biomarker for grading glial neoplasms, and it has shown high statistical correlation with T2* DSC-derived perfusion parameters.

Using Radiomics to Differentiate Brain Metastases From Lung Cancer Versus Breast Cancer, Including Predicting Epidermal Growth Factor Receptor and human Epidermal Growth Factor Receptor 2 Status

OBJECTIVE

We evaluated the feasibility of using multiregional radiomics to identify brain metastasis (BM) originating from lung adenocarcinoma (LA) and breast cancer (BC) and assess the epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) status.

METHODS

Our experiment included 160 patients with BM originating from LA (n = 70), BC (n = 67), and other tumor types (n = 23), between November 2017 and December 2021. All patients underwent contrast-enhanced T1- and T2-weighted magnetic resonance imaging (MRI) scans. A total of 1967 quantitative MRI features were calculated from the tumoral active area and peritumoral edema area and selected using least absolute shrinkage and selection operator regression with 5-fold cross-validation. We constructed radiomic signatures (RSs) based on the most predictive features for preoperative assessment of the metastatic origins, EGFR mutation, and HER2 status. Prediction performance of the constructed RSs was evaluated based on the receiver operating characteristic curve analysis.

RESULTS

The developed multiregion RSs generated good area under the receiver operating characteristic curve (AUC) for identifying the LA and BC origin in the training (AUCs, RS-LA vs RS-BC, 0.767 vs 0.898) and validation (AUCs, RS-LA vs RS-BC, 0.778 and 0.843) cohort and for predicting the EGFR and HER2 status in the training (AUCs, RS-EGFR vs RS-HER2, 0.837 vs 0.894) and validation (AUCs, RS-EGFR vs RS-HER2, 0.729 vs 0.784) cohorts.

CONCLUSIONS

Our results revealed associations between brain MRI-based radiomics and their metastatic origins, EGFR mutations, and HER2 status. The developed multiregion combined RSs may be considered noninvasive predictive markers for planning early treatment for BM patients.

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

The utility of arterial spin labelled perfusion-weighted magnetic resonance imaging in measuring the vascularity of high grade gliomas - A prospective study

Background

Dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) currently remains the gold standard technique for measuring cerebral perfusion in glioma diagnosis and surveillance. Arterial spin labelling (ASL) PWI is a non-invasive alternative that does not require gadolinium contrast administration, although it is yet to be applied in widespread clinical practice. This study aims to assess the utility of measuring signal intensity in ASL PWI in predicting glioma vascularity by measuring maximal tumour signal intensity in patients based on pre-operative imaging and comparing this to maximal vessel density on histopathology.

Methods

Pseudocontinuous ASL (pCASL) and DSC images were acquired pre-operatively in 21 patients with high grade gliomas. The maximal signal intensity within the gliomas over a region of interest of 100 mm² was measured and also normalised to the contralateral cerebral cortex (nTBF-C), and cerebellum (nTBF-Cb). Maximal vessel density per 1 mm² was determined on histopathology using CD31 and CD34 immunostaining on all participants.

Results

Using ASL, statistically significant correlation was observed between maximal signal intensity (p < 0.05) and nTBF-C (p < 0.05) to maximal vessel density based on histopathology. Although a positive trend was also observed nTBF-Cb, this did not reach statistical significance. Using DSC, no statistically significant correlation was found between signal intensity, nTBF-C and nTBF-Cb. There was no correlation between maximal signal intensity between ASL and DSC. Average vessel density did not correlate with age, sex, previous treatment, or IDH status.

Conclusions

ASL PWI imaging is a reliable marker of evaluating the vascularity of high grade gliomas and may be used as an adjunct to DSC PWI.

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

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

Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging

This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.

Clinical utility of intraoperative arterial spin labeling for resection control in brain tumor surgery at 3 T

Resection control in brain tumor surgery can be achieved in real time with intraoperative MRI (iMRI). Arterial spin labeling (ASL), a technique that measures cerebral blood flow (CBF) non-invasively without the use of intravenous contrast agents, can be performed intraoperatively, providing morpho-physiological information. This study aimed to evaluate the feasibility, image quality and potential to depict residual tumor of a pseudo-continuous ASL (PCASL) sequence at 3 T. Seventeen patients with brain tumors, primary (16) or metastatic (1), undergoing resection surgery with iMRI monitoring, were prospectively recruited (nine men, age 56 ± 16.6 years). A PCASL sequence with long labeling duration (3000 ms) and postlabeling delay (2000 ms) was added to the conventional protocol, which consisted of pre- and postcontrast 3D T₁ -weighted (T1w) images, optional 3D-FLAIR, and diffusion. Three observers independently assessed the image quality (four-point scale) of PCASL-derived CBF maps. In those with diagnostic quality (Scores 2-4) they evaluated the presence of residual tumor using the conventional sequences first, and the CBF maps afterwards (three-point scale). Inter-observer agreement for image quality and the presence of residual tumor was assessed using Fleiss kappa statistics. The intraoperative CBF ratio of the surgical margins (i.e., perilesional CBF values normalized to contralateral gray matter CBF) was compared with preoperative CBF ratio within the tumor (Wilcoxon's test). Diagnostic ASL image quality was observed in 94.1% of patients (interobserver Fleiss κ = 0.76). PCASL showed additional foci suggestive of high-grade residual component in three patients, and a hyperperfused area extending outside the enhancing component in one patient. Interobserver agreement was almost perfect in the evaluation of residual tumor with the conventional sequences (Fleiss κ = 0.92) and substantial for PCASL (Fleiss κ = 0.80). No significant differences were found between pre and intraoperative CBF ratios (p = 0.578) in patients with residual tumor (n = 7). iMRI-PCASL perfusion is feasible at 3 T and is useful for the intraoperative assessment of residual tumor, providing in some cases additional information to the conventional sequences.

Evaluation of Brain Tumors Using Amide Proton Transfer Imaging: A Comparison of Normal Amide Proton Transfer Signal With Abnormal Amide Proton Transfer Signal Value

OBJECTIVE

The aim of the study was to evaluate the relationship of amide proton transfer (APT) signal characteristics in brain tumors and uninvolved brain tissue for patients with glioblastoma and those with brain metastases.

METHODS

Using the mDIXON 3D-APT sequence of the fast spin echo method, an APT image was obtained. The mean APT signal values of tumor core, peritumor edema, ipsilateral normal-appearing white matter (INAWM), and contralateral normal white matter (CNAWM) were obtained and compared between glioblastoma and brain metastases. Receiver operating characteristic curves were used to evaluate parameters for distinguishing between glioblastoma and brain metastases. In addition, the difference and change rate in APT signal values between tumor core and peritumoral edema (PE) and CNAWM were evaluated, respectively.

RESULTS

The APT signal values of glioblastoma were the highest in tumor core (3.41% ± 0.49%), followed by PE (2.24% ± 0.29%), INAWM (1.35% ± 0.15%), and CNAWM (1.26% ± 0.12%, P < 0.001). The APT signal value of brain metastases was the highest in tumor core (2.74% ± 0.34%), followed by PE (1.86% ± 0.35%), INAWM (1.17% ± 0.13%), and CNAWM (1.2% ± 0.09%, P < 0.01). The APT change rate (between PE and CNAWM) was not significantly different at 78% and 56% for glioblastoma and brain metastases, respectively ( P > 0.05).

CONCLUSIONS

Performing APT imaging under the same parameters used in this study may aid in the identification of brain tumors.

Differentiation of high grade glioma and solitary brain metastases by measuring relative cerebral blood volume and fractional anisotropy: a systematic review and meta-analysis of MRI diagnostic test accuracy studies

OBJECTIVE

This study aims to research the efficacy of MRI (I) for differentiating high-grade glioma (HGG) (P) with solitary brain metastasis (SBM) (C) by creating a combination of relative cerebral blood volume (rCBV) (O) and fractional anisotropy (FA) (O) in patients with intracerebral tumors.

METHODS

Searches were conducted on September 2021 with no publication date restriction, using an electronic search for related articles published in English, from PubMed (1994 to September 2021), Scopus (1977 to September 2021), Web of Science (1985 to September 2021), and Cochrane (1997 to September 2021). A total of 1056 studies were found, with 23 used for qualitative and quantitative data synthesis. Inclusion criteria were: patients diagnosed with HGG and SBM without age, sex, or race restriction; MRI examination of rCBV and FA; reliable histopathological diagnostic method as the gold-standard for all conditions of interest; observational and clinical studies. Newcastle-Ottawa quality assessment Scale (NOS) and Cochrane risk of bias tool (ROB) for observational and clinical trial studies were managed to appraise the quality of individual studies included. Data extraction results were managed using Mendeley and Excel, pooling data synthesis was completed using the Review Manager 5.4 software with random effect model to discriminate HGG and SBM, and divided into four subgroups.

RESULTS

There were 23 studies included with a total sample size of 597 HGG patients and 373 control groups/SBM. The analysis was categorized into four subgroups: (1) the subgroup with rCBV values in the central area of the tumor/intratumoral (399 HGG and 232 SBM) shows that HGG patients are not significantly different from SBM/controls group (SMD [95% CI] = -0.27 [-0.66, 0.13]), 2) the subgroup with rCBV values in the peritumoral area (452 HGG and 274 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] =  -1.23 [-1.45 to -1.01]), (3) the subgroup with FA values in the central area of the tumor (249 HGG and 156 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = - 0.44 [-0.84,-0.04]), furthermore (4) the subgroup with FA values in the peritumoral area (261 HGG and 168 SBM) shows that the HGG patients are significantly higher than the SBM (SMD [95% CI] = -0.59 [-1.02,-0.16]).

CONCLUSION

Combining rCBV and FA measurements in the peritumoral region and FA in the intratumoral region increase the accuracy of MRI examination to differentiate between HGG and SBM patients effectively. Confidence in the accuracy of our results may be influenced by major interstudy heterogeneity. Whereas the I2 for the rCBV in the intratumoral subgroup was 80%, I2 for the rCBV in the peritumoral subgroup was 39%, and I2 for the FA in the intratumoral subgroup was 69%, and I2 for the FA in the peritumoral subgroup was 74%. The predefined accurate search criteria, and precise selection and evaluation of methodological quality for included studies, strengthen this studyOur study has no funder, no conflict of interest, and followed an established PROSPERO protocol (ID: CRD42021279106).

ADVANCES IN KNOWLEDGE

The combination of rCBV and FA measurements' results is promising in differentiating HGG and SBM.

High-grade glioma and solitary metastasis: differentiation by spectroscopy and advanced magnetic resonance techniques

Background

The differentiation by means of magnetic resonance between high-grade gliomas and intracranial solitary single metastasis is of the utmost importance since they condition both surgical and complementary treatment.

Results

Retrospective study that analyzes the parameters of advanced magnetic resonance imaging: spectroscopy, diffusion and perfusion, specifically focused on the differences in the coefficients of the metabolites Cho/Cr, Cho/NAA and NAA/Cr in peritumoral edema between high-grade gliomas and metastases. The data have been statistically analyzed using ROC (receiver operating characteristic) curves, and cutoff values were obtained.

A total of 79 patients with histologically analyzed tumors were analyzed: 49 high-grade gliomas (40 multiform glioblastomas and 9 anaplastic astrocytomas) and 30 metastases. A statistically significant mean difference was obtained in the three metabolite ratios. The area under the curve for the Cho/NAA ratio was 0.958 (CI: 0.903–1), for Cho/Cr 0.922 (CI: 0.859–0.985) and for NAA/Cr 0.163 (CI: 0.068–0.258; p < 0.001). The cutoff values were 1.115 for Cho/NAA (sensitivity 93.87%, specificity 93.33%, global precision 93.67%); 1.18 for the Cho/Cr ratio (sensitivity 89.79%, specificity 93.33% and precision 91.13%) and 1.155 for the NAA/Cr ratio (sensitivity 67.34%, specificity 93.33%, global precision 44.30%).

Conclusion

The results of the study support the premise that spectroscopy at the level of peritumoral edema is able to differentiate between high-grade gliomas and metastases by showing tumor infiltration in peritumoral edema.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Diagnostic accuracy of arterial spin labeling in differentiating between primary central nervous system lymphoma and high-grade glioma: a systematic review and meta-analysis

BACKGROUND

Existing studies have confirmed the accuracy of arterial spin labeling (ASL) in differentiating between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG). We aimed to consolidate the existing evidence with a meta-analysis.

METHODS

Six literature databases were searched for relevant papers. After assessing the quality of studies, bivariate regression was performed, and the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic score, diagnostic odds ratio (DOR), and the area under the curve (AUC) of the summary receiver operating characteristic (SROC) curve were calculated, along with the corresponding 95% confidence intervals (CIs). Deeks' test was used to determine risk of publication bias.

RESULTS

Ten high-quality studies, comprising 151 patients with PCNSL and 455 with HGG, were included. The pooled SEN was 0.79 (95% CI: 0.72-0.85), pooled SPE was 0.90 (95% CI: 0.84-0.94), pooled PLR was 8.07 (95% CI: 5.01-13.02), pooled NLR was 0.23 (95% CI: 0.17-0.32), pooled diagnostic score was 3.56 (95% CI: 2.94-4.18), and pooled DOR was 35.10 (95% CI: 18.83-65.45). The AUC of SROC was 0.86 (95% CI: 0.83-0.89). No publication bias was found.

CONCLUSIONS

ASL demonstrated high diagnostic accuracy in differentiating between PCNSL and HGG.

Ophthalmic Magnetic Resonance Imaging: Where Are We (Heading To)?

Magnetic resonance imaging of the eye and orbit (MReye) is a cross-domain research field, combining (bio)physics, (bio)engineering, physiology, data sciences and ophthalmology. A growing number of reports document technical innovations of MReye and promote their application in preclinical research and clinical science. Realizing the progress and promises, this review outlines current trends in MReye. Examples of MReye strategies and their clinical relevance are demonstrated. Frontier applications in ocular oncology, refractive surgery, ocular muscle disorders and orbital inflammation are presented and their implications for explorations into ophthalmic diseases are provided. Substantial progress in anatomically detailed, high-spatial resolution MReye of the eye, orbit and optic nerve is demonstrated. Recent developments in MReye of ocular tumors are explored, and its value for personalized eye models derived from machine learning in the treatment planning of uveal melanoma and evaluation of retinoblastoma is highlighted. The potential of MReye for monitoring drug distribution and for improving treatment management and the assessment of individual responses is discussed. To open a window into the eye and into (patho)physiological processes that in the past have been largely inaccessible, advances in MReye at ultrahigh magnetic field strengths are discussed. A concluding section ventures a glance beyond the horizon and explores future directions of MReye across multiple scales, including in vivo electrolyte mapping of sodium and other nuclei. This review underscores the need for the (bio)medical imaging and ophthalmic communities to expand efforts to find solutions to the remaining unsolved problems and technical obstacles of MReye, with the objective to transfer methodological advancements driven by MR physics into genuine clinical value.

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.

Percentage signal intensity recovery: A step ahead of rCBV in DSC MR perfusion imaging for the differentiation of common neoplasms of brain

Context

Relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from T2* dynamic susceptibility contrast magnetic resonance imaging are important parameters for brain tumor assessment.

Aim

To study the accuracy of PSR in the differentiation of low-grade glioma, high-grade glioma, lymphoma, and metastases particularly in comparison to rCBV.

Settings and Design

Retrospective observational study.

Subjects and Methods

Study included pathologically confirmed cases of 10 low-grade glioma, 22 high-grade glioma, 6 lymphoma, and 12 metastases (Total 50). PSR, relative PSR (rPSR), and rCBV were calculated.

Statistical Analysis Used

Accuracy of these parameters studied statistically using analysis of variance and ROC (Receiver operating characteristic) curves.

Results

rCBV was higher in metastases (3.45 ± 2.82) and high-grade glioma (3.47 ± 1.62), whereas was low in lymphoma (1.03 ± 0.74) and low-grade glioma (1.43 ± 0.47) with P value of 0.030. PSR was low in metastases (48 ± 16.18), intermediate in glioma (73.24 ± 6.39 and 88.26 ± 6.05, high and low grade), and high in lymphoma (112.16 ± 10.57) with P value < 0.000. rPSR was higher for lymphoma (1.73 ± 0.57) than high-grade glioma (0.85 ± 0.11) and metastasis (0.69 ± 0.19) with P value <.000. Area under ROC for PSR was greater than rCBV in differentiating metastases from lymphoma (1.00 vs 0.13), high-grade glioma from lymphoma (1.00 vs 0.38), high-grade glioma from metastases (0.89 vs 0.58), and high-grade glioma from low-grade glioma (0.96 vs 0.03) with excellent curve characteristics. F values for PSR and rPSR from ANOVA analysis were 71.47 and 36.77, was better than rCBV (3.84) in differentiating these groups.

Conclusions

Percentage of signal recovery shows low recovery values in metastases, intermediate recovery values in glioma, and overshoot in lymphoma. PSR values show lower overlap than rCBV between lymphoma and metastases; and between high grade glioma and metastases. PSR difference is also higher than rCBV between low- and high-grade gliomas. Hence, PSR can potentially help as an additional perfusion parameter in the preoperative differentiation of these tumors.

Visualising brain capillaries in magnetic resonance images via supramolecular self-assembly

We report the supramolecular self-assembly of one fluorescein and three Gd-chelate conjugated 8-arm polyethylene glycols (8-arm PEG-FGd₃) for visualising the capillaries of the brain in magnetic resonance imaging (MRI).

Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen's d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch's test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.

The Fluctuations of Blood Oxygen Level-Dependent Signals as a Method of Brain Tumor Characterization: A Preliminary Report

OBJECTIVE

In this study we present the nature and characteristic of the fluctuation of blood oxygen level-dependent (BOLD) signals measured from brain tumors.

METHODS

Supratentorial astrocytomas, which were neither operated nor previously managed with chemotherapy or radiotherapy, were segmented, and the time series of the BOLD signal fluctuations were extracted. The mean (across patients) power spectra were plotted for the different World Health Organization tumor grades. One-way analysis of variance (ANOVA) was performed to identify significant differences between the power spectra of different tumor grades. Results were considered significant at P < 0.05.

RESULTS

A total of 58 patients were included in the study. This group of patients included 1 patient with grade I glioma; 15 with grade II; 12 with grade III; and 30 with grade IV. The power spectra of the tumor time series were individually inspected, and all tumors exhibited high peaks at the lower frequency signals, but these were more pronounced in high-grade tumors. ANOVA showed a significant difference in power spectra between groups (P = 0.000). Post hoc analysis with Bonferroni correction showed a significant difference between grade II and grade III (P = 0.012) and grade IV (P = 0.000). There was no significant power spectra difference between grade III and IV tumors (P = 1).

CONCLUSIONS

The power spectra of BOLD signals from tumor tissue showed fluctuations in the low-frequency signals and were significantly correlated with tumor grade. These signals could have a misleading effect when analyzing resting state functional magnetic resonance imaging and could be also viewed as a potential method of tumor characterization.

Discrimination between Glioblastoma and Solitary Brain Metastasis: Comparison of Inflow-Based Vascular-Space-Occupancy and Dynamic Susceptibility Contrast MR Imaging

BACKGROUND AND PURPOSE

Accurate differentiation between glioblastoma and solitary brain metastasis is of vital importance clinically. This study aimed to investigate the potential value of the inflow-based vascular-space-occupancy MR imaging technique, which has no need for an exogenous contrast agent, in differentiating glioblastoma and solitary brain metastasis and to compare it with DSC MR imaging.

MATERIALS AND METHODS

Twenty patients with glioblastoma and 22 patients with solitary brain metastasis underwent inflow-based vascular-space-occupancy and DSC MR imaging with a 3T clinical scanner. Two neuroradiologists independently measured the maximum inflow-based vascular-space-occupancy-derived arteriolar CBV and DSC-derived CBV values in intratumoral regions and peritumoral T2-hyperintense regions, which were normalized to the contralateral white matter (relative arteriolar CBV and relative CBV, inflow-based vascular-space-occupancy relative arteriolar CBV, and DSC-relative CBV). The intraclass correlation coefficient, Student t test, or Mann-Whitney U test and receiver operating characteristic analysis were performed.

RESULTS

All parameters of both regions had good or excellent interobserver reliability (0.74∼0.89). In peritumoral T2-hyperintese regions, DSC-relative CBV (P < .001), inflow-based vascular-space-occupancy arteriolar CBV (P = .001), and relative arteriolar CBV (P = .005) were significantly higher in glioblastoma than in solitary brain metastasis, with areas under the curve of 0.94, 0.83, and 0.72 for discrimination, respectively. In the intratumoral region, both inflow-based vascular-space-occupancy arteriolar CBV and relative arteriolar CBV were significantly higher in glioblastoma than in solitary brain metastasis (both P < .001), with areas under the curve of 0.91 and 0.90, respectively. Intratumoral DSC-relative CBV showed no significant difference (P = .616) between the 2 groups.

CONCLUSIONS

Inflow-based vascular-space-occupancy has the potential to discriminate glioblastoma from solitary brain metastasis, especially in the intratumoral region.

Arterial spin labeling imaging correlates with the angiographic and clinical vascularity of vestibular schwannomas

PURPOSE

Hypervascular vestibular schwannomas (HVSs) are a type of the vestibular schwannomas (VSs) that are extremely difficult to remove. We examined whether HVSs can be predicted by using arterial spin labeling (ASL) imaging.

METHODS

A total of 103 patients with VSs underwent ASL imaging and digital subtraction angiography (DSA) before surgery. Regional cerebral blood flow (CBF) of gray matter and regional tumor blood flow (TBF) were calculated from ASL imaging, and we defined the ratio of TBF to CBF as the relative TBF (rTBF = TBF/CBF). Angiographic vascularity was evaluated by DSA, and clinical vascularity was evaluated by the degree of intraoperative tumor bleeding. Based on the angiographic and clinical vascularity, the VSs were divided into two categories: HVS and non-HVS. We compared rTBF with angiographic and clinical vascularities, retrospectively.

RESULTS

The mean rTBFs of angiographic non-HVSs and HVSs were 1.29 and 2.58, respectively (p < 0.0001). At a cutoff value of 1.55, the sensitivity and specificity were 93.9% and 72.9%, respectively. The mean rTBFs of clinical non-HVS and HVSs were 1.45 and 2.22, respectively (p = 0.0002). At a cutoff value of 1.55, the sensitivity and specificity were 79.4% and 66.7%, respectively.

CONCLUSION

The rTBF calculated from ASL imaging correlates well with tumor vascularity and may be useful for predicting HVSs before surgery.

Extracellular vesicles and their role in glioblastoma

Research on the role of extracellular vesicles (EVs) in disease pathogenesis has been rapidly growing over the last two decades. As EVs can mediate intercellular communication, they can ultimately facilitate both normal and pathological processes through the delivery of their bioactive cargo, which may include nucleic acids, proteins and lipids. EVs have emerged as important regulators of brain tumors, capable of transferring oncogenic proteins, receptors, and small RNAs that may support brain tumor progression, including in the most common type of brain cancer, glioma. Investigating the role of EVs in glioma is crucial, as the most malignant glioma, glioblastoma (GBM), is incurable with a dismal median survival of 12-15 months. EV research in GBM has primarily focused on circulating brain tumor-derived vesicles in biofluids, such as blood and cerebrospinal fluid (CSF), investigating their potential as diagnostic and prognostic biomarkers. Gaining a greater understanding of the role of EVs and their cargo in brain tumor progression may contribute to the discovery of novel diagnostics and therapeutics. In this review, we summarize the known and emerging functions of EVs in glioma biology and pathogenesis, as well as their emerging biomarker potential.

Intravascular signal suppression and microvascular signal mapping using delays alternating with nutation for tailored excitation (DANTE) pulse for arterial spin labeling perfusion imaging

OBJECTIVE

To optimize the delays alternating with nutation for tailored excitation (DANTE) pulse as a vascular crushing gradient to eliminate macro-and micro-vascular signals and to generate a macrovascular space-related map by applying DANTE with multiple conditions.

MATERIALS AND METHODS

Numerical simulation was performed to estimate the optimal flip angle (FA) of the DANTE. A phantom study was conducted to evaluate the impact of the FA and gradient area (GA) of the DANTE with three flow velocities and various parameters of the DANTE. Finally, an in vivo study was performed to assess the optimal DANTE parameters and to map the estimated macrovascular signal of the arterial spin labeling (ASL) signal.

RESULTS

Numerical simulation revealed that the decrease of magnetization plateaued at 12.5° of FA. The phantom study showed that the setting of larger FA or GA decreased the ASL signals. The decrease of the ASL signal depended on the flow velocity, and the dependence increased with decreasing GA. The in vivo study revealed that larger FA and GA decreased the perfusion signal.

DISCUSSION

An optimized DANTE makes it possible to efficiently suppress the macro-and-micro vascular signals depending on the flow velocity. Moreover, macrovascular signal mapping may be useful to assess altered hemodynamic states.

Accelerated whole-brain perfusion imaging using a simultaneous multislice spin-echo and gradient-echo sequence with joint virtual coil reconstruction

PURPOSE

Dynamic susceptibility contrast imaging requires high temporal sampling, which poses limits on achievable spatial coverage and resolution. Additionally, more encoding-intensive multi-echo acquisitions for quantitative imaging are desired to mitigate contrast leakage effects, which further limits spatial encoding. We present an accelerated sequence that provides whole-brain coverage at an improved spatio-temporal resolution, to allow for dynamic quantitative R2 and R2 * mapping during contrast-enhanced imaging.

METHODS

A multi-echo spin and gradient-echo sequence was implemented with simultaneous multislice acquisition. Complementary k-space sampling between repetitions and joint virtual coil reconstruction were used along with a dynamic phase-matching technique to achieve high-quality reconstruction at 9-fold acceleration, which enabled 2 × 2 × 5 mm whole-brain imaging at TR of 1.5 to 1.7 seconds. The multi-echo images from this sequence were fit to achieve quantitative R2 and R2 * maps for each repetition, and subsequently used to find perfusion measures including cerebral blood flow and cerebral blood volume.

RESULTS

Images reconstructed using joint virtual coil show improved image quality and g-factor compared with conventional reconstruction methods, resulting in improved quantitative maps with a 9-fold acceleration factor and whole-brain coverage during the dynamic perfusion acquisition.

CONCLUSION

The method presented shows the advantage of using a joint virtual coil-GRAPPA reconstruction to allow for high acceleration factors while maintaining reliable image quality for quantitative perfusion mapping, with the potential to improve tumor diagnostics and monitoring.

Differences between primary central nervous system lymphoma and glioblastoma: topographic analysis using voxel-based morphometry

AIM

To evaluate the diagnostic feasibility of probabilistic analysis using voxel-based morphometry (VBM) in differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM).

MATERIALS AND METHODS

In total, 118 patients with GBM (57 males, 61 females; mean [± standard deviation] age, 56.9±19.3 years; median, 61 years) and 52 patients with PCNSL (37 males, 15 females; mean age, 62±13.3 years, median, 66 years) were studied retrospectively. Each patient underwent preoperative contrast-enhanced T1-weighted imaging (CE-T1WI) using a 1.5 or 3 T magnetic resonance imaging (MRI) system. To assess preferential occurrence sites, images from CE-T1WI were co-registered and spatially normalised using the MNI152 T1 template. Subsequently, a region of interest (ROI) was placed in the centre of the enhancing tumour in normalised images with 1-mm isotropic resolution. The same ROI between normalised and T1 template images was set up using an ROI manager function in ImageJ software. A spherical volume of interest (VOI) with a radius of 10 mm was determined. A probability map was created by overlaying each image with the VOI. Each VOI was removed from T1 template images for VBM analysis. VBM analysis was performed using statistical parametric mapping (SPM) 12 software under default settings.

RESULTS

VBM analysis showed significantly higher frequency in the splenium of the corpus callosum among PCNSL patients than among GBM patients (p<0.05; family-wise error correction).

CONCLUSION

Topographic analysis using VBM provides useful information for differentiating PCNSL from GBM.

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging

Background

Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities.

Methods

Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images.

Results

Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases.

Conclusions

Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation.

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.

•

Signal profiles of dynamic susceptibility contrast MRI data of brain tumors reflect hemodynamic properties of tumor tissue.

•

Features in signal profiles extracted by machine learning methods revealed the hemodynamic heterogeneity of the gliomas.

•

The reported approach is a new strategy to characterize the intra-tumor heterogeneity and physiological properties of gliomas.

Glioma grading using structural magnetic resonance imaging and molecular data

A glioma grading method using conventional structural magnetic resonance image (MRI) and molecular data from patients is proposed. The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture analysis, multifractal detrended fluctuation analysis, and multiresolution fractal Brownian motion in structural MRI. The proposed method is evaluated using two multicenter MRI datasets: (1) the brain tumor segmentation (BRATS-2017) challenge for high-grade versus low-grade (LG) and (2) the cancer imaging archive (TCIA) repository for glioblastoma (GBM) versus LG glioma grading. The grading performance using MRI is compared with that of digital pathology (DP) images in the cancer genome atlas (TCGA) data repository. The results show that the mean area under the receiver operating characteristic curve (AUC) is 0.88 for the BRATS dataset. The classification of tumor grades using MRI and DP images in TCIA/TCGA yields mean AUC of 0.90 and 0.93, respectively. This work further proposes and compares tumor grading performance using molecular alterations (IDH1/2 mutations) along with MRI and DP data, following the most recent World Health Organization grading criteria, respectively. The overall grading performance demonstrates the efficacy of the proposed noninvasive glioma grading approach using structural MRI.

Application of MRS- and ASL-guided navigation for biopsy of intracranial tumors

BACKGROUND

The diagnosis of a tumor depends on accurate identification of the target area for biopsy. However, tumor heterogeneity and the inability of conventional structural data for identifying the most malignant areas can reduce this accuracy.

PURPOSE

To evaluate the feasibility and practicality of magnetic resonance spectroscopy (MRS)- and arterial spin labeling (ASL)-guided MRI navigation for needle biopsy of intracranial tumors.

MATERIAL AND METHODS

Thirty patients with intracranial tumors who underwent intraoperative stereotactic biopsy were retrospectively analyzed. Contrast-enhanced 3D-BRAVO or 3D-T2FLAIR structural data, combined with MRS and ASL data, were used to identify the target area for biopsy. High-choline or high-perfusion sites were chosen preferentially, and then the puncture trajectory was optimized to obtain specimens for histopathologic examination.

RESULTS

Twenty-two specimens were collected from 20 glioma patients (two specimens each were collected from two patients) and ten specimens were collected from ten lymphoma patients. The diagnosis rate after the biopsy was 93.3% (28/30). Two gliomas were initially diagnosed as gliosis and subsequently diagnosed correctly after the collection of a second biopsy specimen. Combined MRS and ASL helped target selection in 23 cases (76.7%), including three cases each of low-enhancing and non-enhancing gliomas. In two cases, the target selection decision was changed because the areas initially chosen on the basis of positron emission tomography data did not match the high-perfusion areas identified with ASL.

CONCLUSION

Compared with conventional MRI, combined MRS and ASL improved the accuracy of target selection for the stereotactic biopsy of intracranial tumors.

The value of arterial spin labelling in adults glioma grading: systematic review and meta-analysis

This study aimed to evaluate the diagnostic performance of arterial spin labelling (ASL) in grading of adult gliomas. Eighteen studies matched the inclusion criteria and were included after systematic searches through EMBASE and MEDLINE databases. The quality of the included studies was assessed utilizing Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The quantitative values were extracted and a meta-analysis was subsequently based on a random-effect model with forest plot and joint sensitivity and specificity modelling. Hierarchical summary receiver operating characteristic (HROC) curve analysis was also conducted. The absolute tumour blood flow (TBF) values can differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) and grade II from grade IV tumours. However, it lacked the capacity to differentiate grade II from grade III tumours and grade III from grade IV tumours. In contrast, the relative TBF (rTBF) is effective in differentiating HGG from LGG and in glioma grading. The maximum rTBF (rTBFmax) demonstrated the best results in glioma grading. These results were also reflected in the sensitivity/specificity analysis in which the rTBFmax showed the highest discrimination performance in glioma grading. The estimated effect size for the rTBF was approximately similar between HGGs and LGGs, and grade II and grade III tumours, (-1.46 (-2.00, -0.91), p-value < 0.001), (-1.39 (-1.89, -0.89), p-value < 0.001), respectively; while it exhibited smaller effect size between grade III and grade IV (-1.05 (-1.82, -0.27)), p < 0.05). Sensitivity and specificity analysis replicate these results as well. This meta-analysis suggests that ASL is useful for glioma grading, especially when considering the rTBFmax parameter.

Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems. We provide experimental results in 3 critical data-driven security scenarios, according to the amount of footstep data made available for model training: at airports security checkpoints (smallest training set), workspace environments (medium training set) and home environments (largest training set). We report state-of-the-art footstep recognition rates with an optimal equal false acceptance and false rejection rate (equal error rate) of 0.7 percent an improvement ratio of 371 percent compared to previous state-of-the-art. We perform a feature analysis of deep residual neural networks showing effective clustering of client's footstep data and to provide insights of the feature learning process.

MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis

BACKGROUND

Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.

PURPOSE

To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

Ovid-MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."

FIELD STRENGTH/SEQUENCE

Patients underwent at least one MRI sequence including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced imaging (DSC), dynamic contrast-enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.

ASSESSMENT

Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

STATISTICAL TESTS

Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta-regression was performed.

RESULTS

Twenty-two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87-93%) and specificity of 89% (95% CI, 85-93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90-0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89-97%] and specificity of 91% [95% CI, 86-96%]). Heterogeneity was only detected in specificity (I² = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.

DATA CONCLUSION

MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:560-572.

Role of Diffusion and Perfusion Magnetic Resonance Imaging in Predicting the Histopathological Grade of Gliomas - A Prospective Study

Context:

Gliomas are the most common brain tumors. In addition to conventional magnetic resonance imaging (MRI) techniques, a variety of new techniques offers more than the anatomic information. The new MRI techniques include perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI).

Aims:

The aim of this study is to assess the sensitivity, specificity, predictive value, and accuracy of diffusion- and perfusion-weighted MRI in the preoperative grading of gliomas.

Setting/Design:

The study was conducted in the Department of Neurosurgery, Pathology, and Radiodiagnosis, Sher-e-Kashmir Institute of Medical Sciences, Kashmir, India, which is the only tertiary care neurosurgical center in the state. It was a prospective study.

Patients and Methods:

Thirty-one consecutive patients with gliomas were included in the study. All the patients were evaluated by a standard conventional contrast-enhanced study on Siemens 1.5 Tesla MRI. In addition to the standard MRI, diffusion- and perfusion-weighted MRI were also performed. The histopathological grading of the tumor was done as per the WHO classification of 2007. The sensitivity, specificity, predictive value, and accuracy of diffusion- and perfusion-weighted MRI in determining tumor grade were calculated. Comparison was done between PWI, DWI findings, and WHO histopathological grading.

Analysis Method:

The statistical analysis was done using the Statistical Package for the Social Sciences, and receiver operating characteristic curves were used to estimate sensitivity, specificity, and accuracy.

Results:

The overall sensitivity of PWI (with regional cerebral blood volume cutoff of 1.7) in the preoperative assessment of high-grade gliomas was 82.6% and specificity was 75%, the positive predictive value (PPV) was 90.48%, and the negative predictive value (NPV) was 60%. The overall accuracy was 80.65%. In case of DWI, the sensitivity was 69.57% and the specificity was 75%, and the PPV and NPVs were 88.8% and 46.15%, respectively. The overall accuracy was 71%.

Conclusion:

Our results clearly show higher accuracy of diffusion- and perfusion-weighted MRI in assessment of glioma grade as compared to conventional MRI. This information can prove very useful for the operating neurosurgeon in preoperative assessment and surgical planning. Postoperatively, the neuropathologist can also benefit from such information.

Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type

Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image features of different brain metastases in a multiclass machine learning approach for patients with unknown primary lesion at the time of diagnosis. Materials and methods This single-center retrospective analysis included radiomic features of 658 brain metastases from T1-weighted contrast material-enhanced, T1-weighted nonenhanced, and fluid-attenuated inversion recovery (FLAIR) images in 189 patients (101 women, 88 men; mean age, 61 years; age range, 32-85 years). Images were acquired over a 9-year period (from September 2007 through December 2016) with different MRI units, reflecting heterogeneous image data. Included metastases originated from breast cancer (n = 143), small cell lung cancer (n = 151), non-small cell lung cancer (n = 225), gastrointestinal cancer (n = 50), and melanoma (n = 89). A total of 1423 quantitative image features and basic clinical data were evaluated by using random forest machine learning algorithms. Validation was performed with model-external fivefold cross validation. Comparative analysis of 10 randomly drawn cross-validation sets verified the stability of the results. The classifier performance was compared with predictions from a respective conventional reading by two radiologists. Results Areas under the receiver operating characteristic curve of the five-class problem ranged between 0.64 (for non-small cell lung cancer) and 0.82 (for melanoma); all P values were less than .01. Prediction performance of the classifier was superior to the radiologists' readings. Highest differences were observed for melanoma, with a 17-percentage-point gain in sensitivity compared with the sensitivity of both readers; P values were less than .02. Conclusion Quantitative features of routine brain MR images used in a machine learning classifier provided high discriminatory accuracy in predicting the tumor type of brain metastases. © RSNA, 2018 Online supplemental material is available for this article.

Arterial spin labeling perfusion: Prospective MR imaging in differentiating neoplastic from non-neoplastic intra-axial brain lesions

PURPOSE

The purpose of this article is to assess the diagnostic performance of arterial spin-labeling (ASL) magnetic resonance perfusion imaging to differentiate neoplastic from non-neoplastic brain lesions.

MATERIAL AND METHODS

This prospective study included 60 consecutive, newly diagnosed, untreated patients with intra-axial lesions with perilesional edema (PE) who underwent clinical magnetic resonance imaging including ASL sequences at 3T. Region of interest analysis was performed to obtain mean cerebral blood flow (CBF) values from lesion (L), PE and normal contralateral white matter (CWM). Normalized (n) CBF ratio was obtained by dividing the mean CBF value of L and PE by mean CBF value of CWM. Discriminant analyses were performed to determine the best cutoff value of nCBFL and nCBFPE in differentiating neoplastic from non-neoplastic lesions.

RESULTS

Thirty patients were in the neoplastic group (15 high-grade gliomas (HGGs), 15 metastases) and 30 in the non-neoplastic group (12 tuberculomas, 10 neurocysticercosis, four abscesses, two fungal granulomas and two tumefactive demyelination) based on final histopathology and clincoradiological diagnosis. We found higher nCBFL (6.65 ± 4.07 vs 1.68 ± 0.80, p < 0.001) and nCBFPE (1.86 ± 1.43 vs 0.74 ± 0.21, p < 0.001) values in the neoplastic group than non-neoplastic. For predicting neoplastic lesions, we found an nCBFL cutoff value of 1.89 (AUC 0.917; 95% CI 0.854 to 0.980; sensitivity 90%; specificity 73%) and nCBFPE value of 0.76 (AUC 0.783; 95% CI 0.675 to 0.891; sensitivity 80%; specificity 58%). Mean nCBFL was higher in HGGs (8.70 ± 4.16) compared to tuberculomas (1.98 ± 0.87); and nCBFPE was higher in HGGs (3.06 ± 1.53) compared to metastases (0.86 ± 0.34) and tuberculomas (0.73 ± 0.22) ( p < 0.001).

CONCLUSION

ASL perfusion may help in distinguishing neoplastic from non-neoplastic brain lesions.

Sensitivity of Multiphase Pseudocontinuous Arterial Spin Labelling (MP pCASL) Magnetic Resonance Imaging for Measuring Brain and Tumour Blood Flow in Mice

Brain and tumour blood flow can be measured noninvasively using arterial spin labelling (ASL) magnetic resonance imaging (MRI), but reliable quantification in mouse models remains difficult. Pseudocontinuous ASL (pCASL) is recommended as the clinical standard for ASL and can be improved using multiphase labelling (MP pCASL). The aim of this study was to optimise and validate MP pCASL MRI for cerebral blood flow (CBF) measurement in mice and to assess its sensitivity to tumour perfusion. Following optimization of the MP pCASL sequence, CBF data were compared with gold-standard autoradiography, showing close agreement. Subsequently, MP pCASL data were acquired at weekly intervals in models of primary and secondary brain tumours, and tumour microvessel density was determined histologically. MP pCASL measurements in a secondary brain tumour model revealed a significant reduction in blood flow at day 35 after induction, despite a higher density of blood vessels. Tumour core regions also showed reduced blood flow compared with the tumour rim. Similarly, significant reductions in CBF were found in a model of glioma 28 days after tumour induction, together with an increased density of blood vessels. These findings indicate that MP pCASL MRI provides accurate and robust measurements of cerebral blood flow in naïve mice and is sensitive to changes in tumour perfusion.

Rapid and accurate dictionary-based T2 mapping from multi-echo turbo spin echo data at 7 Tesla

BACKGROUND

Using lower refocusing flip angles in multi-echo turbo spin echo (ME-TSE) sequences at ultra-high magnetic field leads to non-monoexponential signal decay and overestimation of T₂ values due to stimulated and secondary echoes.

PURPOSE

To investigate the feasibility of a fast and accurate reconstruction of quantitative T₂ values using an ME-TSE sequence with reduced refocusing flip angles at 7 Tesla, a dictionary-based reconstruction method was developed and is presented in this work.

STUDY TYPE

Prospective.

SUBJECTS

Phantom measurements with relaxation phantom, four healthy volunteers.

FIELD STRENGTH/SEQUENCE

7 Tesla MRI, multi-echo turbo spin echo (ME-TSE), spin echo (SE), and B₁ mapping.

ASSESSMENT

Based on Bloch simulations and the extended phase graph model, signal decay curves were calculated to account for nonrectangular slice profile, B₁ inhomogeneity, and reduced refocusing flip angles and stored in a dictionary. Data obtained with an ME-TSE sequence at 7 Tesla were matched to this dictionary to obtain T₂ values. To compare the proposed method to reference T₂ values, a spin echo sequence with different echo times was used.

STATISTICAL TESTS

Welch's t-test was used to compare T₂ values in phantom measurements.

RESULTS

T₂ values obtained with the proposed ME-TSE method coincided with the T₂ values from the spin echo experiment in phantom measurements (P = 0.89 for 120° flip angle, P = 0.75 for 180° flip angle). Only for very low B₁ transmit fields, a slight overestimation of T₂ values was observed. In vivo measurements showed lower T₂ values in gray matter (55 ± 2 millisecond) and white matter (39 ± 5 millisecond) compared with literature values of 3 Tesla data.

DATA CONCLUSIONS

The proposed dictionary-based ME-TSE approach provided accurate T₂ values in short measurement time at 7 Tesla with low specific absorption rate burden due to the reduction of refocusing flip angles. Therefore, it can provide new opportunities in clinical high-field MRI to further improve radiographic diagnosis by using quantitative imaging.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1253-1262.

Arterial spin labeling MR imaging for differentiation between high- and low-grade glioma-a meta-analysis

Background

Arterial spin labeling is an MR imaging technique that measures cerebral blood flow (CBF) non-invasively. The aim of the study is to assess the diagnostic performance of arterial spin labeling (ASL) MR imaging for differentiation between high-grade glioma and low-grade glioma.

Methods

Cochrane Library, Embase, Medline, and Web of Science Core Collection were searched. Study selection ended November 2017. This study was prospectively registered in PROSPERO (CRD42017080885). Two authors screened all titles and abstracts for possible inclusion. Data were extracted independently by 2 authors. Bivariate random effects meta-analysis was used to describe summary receiver operating characteristics. Trial sequential analysis (TSA) was performed.

Results

In total, 15 studies with 505 patients were included. The diagnostic performance of ASL CBF for glioma grading was 0.90 with summary sensitivity 0.89 (0.79-0.90) and specificity 0.80 (0.72-0.89). The diagnostic performance was similar between pulsed ASL (AUC 0.90) with a sensitivity 0.85 (0.71-0.91) and specificity 0.83 (0.69-0.92) and pseudocontinuous ASL (AUC 0.88) with a sensitivity 0.86 (0.79-0.91) and specificity 0.80 (0.65-0.87). In astrocytomas, the diagnostic performance was 0.89 with sensitivity 0.86 (0.79 to 0.91) and specificity 0.79 (0.63 to 0.89). Sensitivity analysis confirmed the robustness of the findings. TSA revealed that the meta-analysis was adequately powered.

Conclusion

Arterial spin labeling MR imaging had an excellent diagnostic accuracy for differentiation between high-grade and low-grade glioma. Given its low cost, non-invasiveness, and efficacy, ASL MR imaging should be considered for implementation in the routine workup of patients with glioma.