Percentage signal recovery derived from MR dynamic susceptibility contrast imaging is useful to differentiate common enhancing malignant lesions of the brain

The Utility of Multiparametric Magnetic Resonance Imaging in Reducing Diagnostic Uncertainty for Primary Central Nervous System Lymphoma

BACKGROUND

A key limitation in treatment initiation in primary central nervous system lymphoma (PCNSL) is the diagnostic delay caused by lack of recognition of a lesion as a possible lymphoma, steroid initiation, and lesion involution, often resulting in an inconclusive biopsy result. We highlight the importance of multiparametric magnetic resonance imaging (MRI), which incorporates diffusion-weighted imaging, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and proton magnetic resonance spectroscopy in addition to standard MRI sequences in resolving diagnostic uncertainty for PCNSL.

METHODS

At our center, a consecutive series of 10 patients with histology-proven PCNSL (specifically, diffuse large B-cell lymphoma of the central nervous system) underwent multiparametric MRI. We retrospectively analyzed qualitative and semiquantitative parameters and assessed their radiological concordance for this diagnosis.

RESULTS

We noted overall low apparent diffusion coefficient on diffusion-weighted imaging (mean minimum apparent diffusion coefficient of 0.74), high percentage signal recovery on perfusion-weighted imaging (mean 170%), a high choline-to-creatine ratio, and a high-grade lipid peak on proton magnetic resonance spectroscopy giving an appearance of twin towers. Of 10 patients, 9 had MRI findings concordant for PCNSL, defined as at least 3 of 4 parameters being consistent for PCNSL.

CONCLUSIONS

Concordance between these imaging multiparametric modalities could be used as a radiological predictor of PCNSL, reducing diagnostic delays, providing a more accurate biopsy target, and resulting in quicker treatment initiation.

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles

OBJECTIVE

Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation.

METHODS

Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation.

RESULTS

The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation.

CONCLUSIONS

Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes.

CLINICAL RELEVANCE STATEMENT

The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial.

KEY POINTS

• The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.

DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions

PURPOSE

The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values.

METHODS

This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC.

RESULTS

The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values.

CONCLUSION

Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.

Differentiation between primary central nervous system lymphomas and gliomas according to pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging

Purpose

It is difficult to differentiate between primary central nervous system lymphoma and primary glioblastoma due to their similar MRI findings. This study aimed to assess whether pharmacokinetic parameters derived from dynamic contrast-enhanced MRI could provide valuable insights for differentiation.

Methods

Seventeen cases of primary central nervous system lymphoma and twenty-one cases of glioblastoma as confirmed by pathology, were retrospectively analyzed. Pharmacokinetic parameters, including Ktrans, Kep, Ve, and the initial area under the Gd concentration curve, were measured from the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma. Statistical comparisons were made using Mann-Whitney U tests for Ve and Matrix Metallopeptidase-2, while independent samples t-tests were used to compare pharmacokinetic parameters in the mentioned regions and pathological indicators of enhancing tumor parenchyma, such as vascular endothelial growth factor and microvessel density. The pharmacokinetic parameters with statistical differences were evaluated using receiver-operating characteristics analysis. Except for the Wilcoxon rank sum test for Ve, the pharmacokinetic parameters were compared within the enhancing tumor parenchyma, peritumoral parenchyma, and contralateral normal parenchyma of the primary central nervous system lymphomas and glioblastomas using variance analysis and the least-significant difference method.

Results

Statistical differences were observed in Ktrans and Kep within the enhancing tumor parenchyma and in Kep within the peritumoral parenchyma between these two tumor types. Differences were also found in Matrix Metallopeptidase-2, vascular endothelial growth factor, and microvessel density within the enhancing tumor parenchyma of these tumors. When compared with the contralateral normal parenchyma, pharmacokinetic parameters within the peritumoral parenchyma and enhancing tumor parenchyma exhibited variations in glioblastoma and primary central nervous system lymphoma, respectively. Moreover, the receiver-operating characteristics analysis showed that the diagnostic efficiency of Kep in the peritumoral parenchyma was notably higher.

Conclusion

Pharmacokinetic parameters derived from dynamic contrast-enhanced MRI can differentiate primary central nervous system lymphoma and glioblastoma, especially Kep in the peritumoral parenchyma.

Primary central nervous system lymphoma: Imaging features and differential diagnosis

Primary central nervous system lymphoma (PCNSL) represents 5% of malignant primary brain tumors. The clinical presentation typically includes focal neurological symptoms, increased intracranial pressure, seizures, and psychiatric symptoms. Although histological examination remains the gold standard for diagnostic confirmation, non-invasive imaging plays a crucial role for the diagnosis. In immunocompetent individuals, PCNSL usually appears as a single, well-defined, supratentorial lesion with a predilection for periventricular areas, iso- or hypointense on T1- and T2-weighted magnetic resonance imaging, with restricted diffusion, slightly increased perfusion, and homogenous gadolinium-enhancement. Differential diagnoses include high-grade glioma and pseudotumoral demyelinating disease. In immunocompromised patients, PCNSL may present as multiple lesions, with a higher likelihood of hemorrhage and necrosis and less restricted diffusion than immunocompetent individuals. Differential diagnoses include neurotoxoplasmosis, progressive multifocal leukoencephalopathy, and cerebral abscess. Atypical forms of lymphoma are characterized by extra-axial lymphoma, lymphomatosis cerebri, and intravascular lymphoma. Extra-axial lymphoma presents as single or multiple extra-axial dural lesions with diffuse leptomeningeal contrast-enhancement. Lymphomatosis cerebri appears as an infiltrative and symmetric lesion, primarily affecting deep white matter and basal ganglia, appearing hyperintense on T2-weighted imaging, without significant contrast-enhancement or perfusion changes. Intravascular lymphoma presents as multiple rounded or oval-shaped "infarct-like" lesions, located cortically or subcortically. This study aims to highlight the imaging characteristics of PCNSL, focusing on magnetic resonance imaging and its differential diagnosis.

Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI

OBJECTIVE

To determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection.

MATERIALS AND METHODS

Thirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression).

RESULTS

In IDH wild-type gliomas (IDHwt-i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09-0.14). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%.

CONCLUSIONS

Dynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood-brain barrier disruption (ΔR1,ss), with a single contrast injection.

CLINICAL RELEVANCE STATEMENT

Simultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood-brain barrier disruption and tumor tissue cytoarchitecture.

KEY POINTS

• Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions. • Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging. • Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood-brain barrier disruption and cytoarchitecture characteristics.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review

This AJR Expert Panel Narrative explores the current status of advanced MRI and PET techniques for the post-therapeutic response assessment of high-grade adult-type gliomas, focusing on ongoing clinical controversies in current practice. Discussed techniques that complement conventional MRI and aid the differentiation of recurrent tumor from post-treatment effects include DWI and diffusion tensor imaging; perfusion MRI techniques including dynamic susceptibility contrast (DSC), dynamic contrast-enhanced MRI, and arterial spin labeling; MR spectroscopy including assessment of 2-hydroxyglutarate (2HG) concentration; glucose- and amino acid (AA)-based PET; and amide proton transfer imaging. Updated criteria for Response Assessment in Neuro-Oncology are presented. Given the abundant supporting clinical evidence, the panel supports a recommendation that routine response assessment after HGG treatment should include perfusion MRI, particularly given the development of a consensus recommended DSC-MRI protocol. Although published studies support 2HG MRS and AA PET, these techniques' widespread adoption will likely require increased availability (for 2HG MRS) or increased insurance funding in the United States (for AA PET). The article concludes with a series of consensus opinions from the author panel, centered on the clinical integration of the advanced imaging techniques into posttreatment surveillance protocols.

Multiparametric analysis from dynamic susceptibility contrast-enhanced perfusion MRI to evaluate malignant brain tumors

BACKGROUND AND PURPOSE

Dynamic susceptibility contrast-enhanced (DSC) MR perfusion is a valuable technique for distinguishing brain tumors. Diagnostic potential of measurable parameters derived from preload leakage-corrected-DSC-MRI remains somewhat underexplored. This study aimed to evaluate these parameters for differentiating primary CNS lymphoma (PCNSL), glioblastoma, and metastasis.

METHODS

Thirty-nine patients with pathologically proven PCNSL (n = 14), glioblastoma (n = 14), and metastasis (n = 11) were analyzed. Five DSC parameters-relative CBV (rCBV), percentage of signal recovery (PSR), downward slope (DS), upward slope (US), and first-pass slope ratio-were derived from tumor-enhancing areas. Diagnostic performance was assessed using receiver operating characteristic curve analysis.

RESULTS

RCBV was higher in metastasis (4.58; interquartile range [IQR]: 2.54) and glioblastoma (3.98; IQR: 1.87), compared with PCNSL (1.46; IQR: 0.29; p = .00006 for both). rCBV better distinguished metastasis and glioblastoma from PCNSL, with an area under the curve (AUC) of 0.97 and 0.99, respectively. PSR was higher in PCNSL (88.11; IQR: 21.21) than metastases (58.30; IQR: 22.28; p = .0002), while glioblastoma (74.54; IQR: 21.23) presented almost significant trend-level differences compared to the others (p≈.05). AUCs were 0.79 (PCNSL vs. glioblastoma), 0.91 (PCNSL vs. metastasis), and 0.78 (glioblastoma vs. metastasis). DS and US parameters were statistically significant between glioblastoma (-109.92; IQR: 152.71 and 59.06; IQR: 52.87) and PCNSL (-47.36; IQR: 44.30 and 21.68; IQR: 16.85), presenting AUCs of 0.86 and 0.87.

CONCLUSION

Metastasis and glioblastoma can be better differentiated from PCNSL through rCBV. PSR demonstrated higher differential performance compared to the other parameters and seemed useful, allowing a proper distinction among all, particularly between metastasis and glioblastoma, where rCBV failed. Finally, DS and US were only helpful in differentiating glioblastoma from PCNSL.

Standardized brain tumor imaging protocols for clinical trials: current recommendations and tips for integration

Standardized MRI acquisition protocols are crucial for reducing the measurement and interpretation variability associated with response assessment in brain tumor clinical trials. The main challenge is that standardized protocols should ensure high image quality while maximizing the number of institutions meeting the acquisition requirements. In recent years, extensive effort has been made by consensus groups to propose different "ideal" and "minimum requirements" brain tumor imaging protocols (BTIPs) for gliomas, brain metastases (BM), and primary central nervous system lymphomas (PCSNL). In clinical practice, BTIPs for clinical trials can be easily integrated with additional MRI sequences that may be desired for clinical patient management at individual sites. In this review, we summarize the general concepts behind the choice and timing of sequences included in the current recommended BTIPs, we provide a comparative overview, and discuss tips and caveats to integrate additional clinical or research sequences while preserving the recommended BTIPs. Finally, we also reflect on potential future directions for brain tumor imaging in clinical trials.

Brain Tumor Imaging: Review of Conventional and Advanced Techniques

Approaches to central nervous system (CNS) tumor classification and evaluation have undergone multiple iterations over the past few decades, in large part due to our growing understanding of the influence of genetics on tumor behavior and our refinement of brain tumor imaging techniques. Computed tomography and magnetic resonance imaging (MRI) both play a critical role in the diagnosis and monitoring of brain tumors, although MRI has become especially important due to its superior soft tissue resolution. The purpose of this article will be to briefly review the fundamentals of conventional and advanced techniques used in brain tumor imaging. We will also highlight the applications of these imaging tools in the context of commonly encountered tumors based on the most recently updated 2021 World Health Organization (WHO) classification of CNS tumors framework.

Improved performance of non-preloaded and high flip-angle dynamic susceptibility contrast perfusion-weighted imaging sequences in the presurgical differentiation of brain lymphoma and glioblastoma

OBJECTIVE

This study aimed to compare the accuracy of relative cerebral blood volume (rCBV) and percentage signal recovery (PSR) obtained from high flip-angle dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) sequences with and without contrast agent (CA) preload for presurgical discrimination of brain glioblastoma and lymphoma.

METHODS

Consecutive 336 patients (glioblastoma, 236; PCNSL, 100) were included. All the patients underwent DSC-PWI on 3.0-T magnetic resonance units before surgery. The rCBV and PSR with preloaded and non-preloaded CA were measured. The means of the continuous variables were compared using Welch's t-test. The diagnostic accuracies of the individual parameters were compared using the receiver operating characteristic curve analysis.

RESULTS

The rCBV was higher with preloaded CA than with non-preloaded CA (glioblastoma, 10.20 vs. 8.90, p = 0.020; PCNSL, 3.88 vs. 3.27, p = 0.020). The PSR was lower with preloaded CA than with non-preloaded CA (glioblastoma, 0.59 vs. 0.90; PCNSL, 0.70 vs. 1.63; all p < 0.001). Regarding the differentiation of glioblastoma and PCNSL, the AUC of rCBV with preloaded CA was indistinguishable from that of non-preloaded CA (0.940 vs. 0.949, p = 0.703), whereas the area under the curve of PSR with preloaded CA was lower than non-preloaded CA (0.529 vs. 0.884, p < 0.001).

CONCLUSION

With preloaded CA, diagnostic performance in differentiating glioblastoma and PCNSL did not improve for rCBV and it was decreased for PSR. Therefore, high flip-angle non-preload DSC-PWI sequences offer excellent accuracy and may be of choice sequence for presurgical discrimination of brain lymphoma and glioblastoma.

CLINICAL RELEVANCE STATEMENT

High flip-angle DSC-PWI using non-preloaded CA may be an excellent diagnostic method for distinguishing glioblastoma from PCNSL.

KEY POINTS

• Differentiating primary central nervous system lymphoma and glioblastoma accurately is critical for their management. • DSC-PWI sequences optimised for the most accurate CBV calculations may not be the optimal sequences for presurgical brain tumour diagnosis as they could be masquerading leakage phenomena that may provide interesting information in terms of differential diagnosis. • High flip-angle non-preloaded DSC-PWI sequences render the best accuracy in the presurgical differentiation of brain lymphoma and glioblastoma.

Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data

Background

To investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key pathophysiological processes involved in the hierarchization of the decision-making algorithms of the models.

Methods

From 2013 to 2020, 180 consecutive patients with histopathologically proved lymphomas (n = 77), glioblastomas (n = 45), and metastases (n = 58) were included in machine learning analysis after undergoing MRI. The perfusion parameters (rCBVmax, PSRmax) and spectroscopic concentration ratios (lac/Cr, Cho/NAA, Cho/Cr, and lip/Cr) were applied to construct Classification and Regression Tree (CART) models for multiclass classification of these brain tumors. A 5-fold random cross validation was performed on the dataset.

Results

The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, 0.98 for GBM and 1.00 for METs. The model accuracy was 0.96 with a RSquare of 0.887. Five rules of classifier combinations were extracted with a predicted probability from 0.907 to 0.989 for that end nodes of the decision tree for tumor multiclass classification. In hierarchical order of importance, the root node (Cho/NAA) in the decision tree algorithm was primarily based on the proliferative, infiltrative, and neuronal destructive characteristics of the tumor, the internal node (PSRmax), on tumor tissue capillary permeability characteristics, and the end node (Lac/Cr or Cho/Cr), on tumor energy glycolytic (Warburg effect), or on membrane lipid tumor metabolism.

Conclusion

Our study shows potential implementation of machine learning decision tree model algorithms based on a hierarchical, convenient, and personalized use of perfusion and spectroscopy MRI data for multiclass classification of these brain tumors.

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.

Neuroimaging features of FOXR2-activated CNS neuroblastoma: A case series and systematic review

BACKGROUND AND PURPOSE

CNS neuroblastoma, FOXR2-activated (CNS NB-FOXR2) is a newly recognized tumor type in the 2021 World Health Organization classification of central nervous system (CNS) tumors. We aimed to investigate the clinical and neuroimaging findings of CNS NB-FOXR2 and systematically review previous publications and three new cases.

METHODS

We searched PubMed, SCOPUS, and Embase databases for patients with pathologically proven CNS NB-FOXR2 with sufficient information for preoperative CT and MRI findings. Two board-certified radiologists reviewed the studies and imaging data.

RESULTS

Thirty-one patients from six previous publications and 3 patients from our hospital comprised the study population (median age, 4.2 [range: 1.4-16] years; 19 girls). Clinically, CNS NB-FOXR2 mainly affected children between 2 and 6 years (24/34, 67.6%). Nausea/vomiting and seizures were reported as the main presenting symptoms (100% in total). The tumors frequently showed hyperdensity compared to the cortex on nonenhanced CT (4/5, 80%) with calcification along the inner rim of the tumor (4/5, 80%). More than half of patients showed susceptibility artifacts indicating intratumoral hemorrhage and/or calcification (15/28, 53.6%) on T2*- and/or susceptibility-weighted imaging. Elevated relative cerebral blood volume and flow and percentile signal recovery were observed in one case with dynamic susceptibility contrast MRI.

CONCLUSIONS

Characteristic imaging features including hyperdense attenuation of the solid components and calcification along the inner rim on CT and susceptibility-weighted imaging may assist with preoperative diagnosis of CNS NB-FOXR2 in pediatric patients.

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma

BACKGROUND AND PURPOSE

An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma.

MATERIALS AND METHODS

Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index.

RESULTS

Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55).

CONCLUSIONS

The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.

Evaluation of the Value of Perfusion-Weighted Magnetic Resonance Imaging in the Differential Diagnosis of Sellar and Parasellar Tumors

The purpose of this study was to assess the value of perfusion-weighted imaging (PWI) in the differential diagnosis of sellar and parasellar tumors, as an additional sequence in the magnetic resonance imaging (MRI) protocol. Analysis was based on a substantial group of subjects and included 124 brain and pituitary MRI examinations with a dynamic susceptibility contrast (DSC) PWI sequence. The following perfusion parameters were determined for the tumors: relative cerebral blood volume (rCBV), relative peak height (rPH) and relative percentage of signal intensity recovery (rPSR). To ensure greater repeatability, each of the aforementioned parameters was calculated as: arithmetic mean of the values of the whole tumor, arithmetic mean of the maximum values on each axial slice within the tumor and maximum values derived from the whole tumor. In our study, we established that meningiomas compared to both non-functional and hormone-secreting pituitary adenomas (pituitary neuroendocrine tumors-PitNET) had significantly higher values of rCBV with cut-off points set at 3.45 and 3.54, respectively (mean rCBV). Additionally, meningiomas presented significantly higher maximum and mean maximum rPH values compared to adenomas. DSC PWI imaging adds significant value to conventional MRI examinations and can be helpful in differentiating equivocal pituitary tumors.

Imaging Recommendations for the Diagnosis, Staging, and Management of Adult Brain Tumors

Neuroimaging plays a pivotal role in the clinical practice of brain tumors aiding in the diagnosis, genotype prediction, preoperative planning, and prognostication. The brain tumors most commonly seen in adults are extra-axial lesions like meningioma, intra-axial lesions like gliomas and lesions of the pituitary gland. Clinical features may be localizing like partial seizures, weakness, and sensory disturbances or nonspecific like a headache. On clinical suspicion of a brain tumor, the primary investigative workup should focus on imaging. Other investigations like fundoscopy and electroencephalography may be performed depending on the clinical presentation. Obtaining a tissue sample after identifying a brain tumor on imaging is crucial for confirming the diagnosis and planning further treatment. Tissue sample may be obtained by techniques such as stereotactic biopsy or upfront surgery. The magnetic resonance (MR) imaging protocol needs to be standardized and includes conventional sequences like T1-weighted (T1W) imaging with and without contrast, T2w imaging, fluid-attenuated axial inversion recovery, diffusion-weighted imaging (DWI), susceptibility-weighted imaging, and advanced imaging sequences like MR perfusion and MR spectroscopy. Various tumor characteristics in each of these sequences can help us narrow down the differential diagnosis and also predict the grade of the tumor. Multidisciplinary co-ordination is needed for proper management and care of brain tumor patients. Treatment protocols need to be adapted and individualized for each patient depending on the age, general condition of the patient, histopathological characteristics, and genotype of the tumor. Treatment options include surgery, radiotherapy, and chemotherapy. Imaging also plays a vital role in post-treatment follow-up. Sequences like DWI, MR perfusion, and MR spectroscopy are useful to distinguish post-treatment effects like radiation necrosis and pseudoprogression from true recurrence. Radiological reporting of brain tumor images should follow a structured format to include all the elements that could have an impact on the treatment decisions in patients.

Differentiation of pilocytic astrocytoma, medulloblastoma, and hemangioblastoma on diffusion-weighted and dynamic susceptibility contrast perfusion MRI

This study aimed to evaluate the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging and apparent diffusion coefficient (ADC) for differentiating common posterior fossa tumors, pilocytic astrocytoma (PA), medulloblastoma (MB), and hemangioblastoma (HB). Between January 2016 and April 2022, we enrolled 23 (median age, 7 years [range, 2-26]; 12 female), 13 (10 years [1-24]; 3 female), and 12 (43 years [23-73]; 7 female) patients with PA, MB, and HB, respectively. Normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and normalized mean ADC (nADCmean) were calculated from volume-of-interest and statistically compared. nADCmean was significantly higher in PA than in MB (PA: median, 2.2 [range, 1.59-2.65] vs MB: 0.93 [0.70-1.37], P < .001). nrCBF was significantly higher in HB than in PA and MB (PA: 1.10 [0.54-2.26] vs MB: 1.62 [0.93-3.16] vs HB: 7.83 [2.75-20.1], all P < .001). nrCBV was significantly different between all 3 tumor types (PA: 0.89 [0.34-2.28] vs MB: 1.69 [0.93-4.23] vs HB: 8.48 [4.59-16.3], P = .008 for PA vs MB; P < .001 for PA vs HB and MB vs HB). All tumors were successfully differentiated using an algorithmic approach with a threshold value of 4.58 for nrCBV and subsequent threshold value of 1.38 for nADCmean. DSC parameters and nADCmean were significantly different between PA, MB, and HB. An algorithmic approach combining nrCBV and nADCmean may be useful for differentiating these tumor types.

Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics

BACKGROUND AND PURPOSE

Immunodeficiency-associated CNS lymphoma may occur in different clinical scenarios beyond AIDS. This subtype of CNS lymphoma is diffuse large B-cell and Epstein-Barr virus-positive. Its accurate presurgical diagnosis is often unfeasible because it appears as ring-enhancing lesions mimicking glioblastoma or metastasis. In this article, we describe clinicoradiologic features and test the performance of DSC-PWI metrics for presurgical identification.

MATERIALS AND METHODS

Patients without AIDS with histologically confirmed diffuse large B-cell Epstein-Barr virus-positive primary CNS lymphoma (December 2010 to January 2022) and diagnostic MR imaging without onco-specific treatment were retrospectively studied. Clinical, demographic, and conventional imaging data were reviewed. Previously published DSC-PWI time-intensity curve analysis methodology, to presurgically identify primary CNS lymphoma, was used in this particular lymphoma subtype and compared with a prior cohort of 33 patients with Epstein-Barr virus-negative CNS lymphoma, 35 with glioblastoma, and 36 with metastasis data. Normalized curves were analyzed and compared on a point-by-point basis, and previously published classifiers were tested. The standard percentage of signal recovery and CBV values were also evaluated.

RESULTS

Seven patients with Epstein-Barr virus-positive primary CNS lymphoma were included in the study. DSC-PWI normalized time-intensity curve analysis performed the best for presurgical identification of Epstein-Barr virus-positive CNS lymphoma (area under the receiver operating characteristic curve of 0.984 for glioblastoma and 0.898 for metastasis), followed by the percentage of signal recovery (0.833 and 0.873) and CBV (0.855 and 0.687).

CONCLUSIONS

When a necrotic tumor is found in a potentially immunocompromised host, neuroradiologists should consider Epstein-Barr virus-positive CNS lymphoma. DSC-PWI could be very useful for presurgical characterization, with especially strong performance of normalized time-intensity curves.

Evaluation of Diffusion-Perfusion Mismatch in Acute Ischemic Stroke with a New Automated Perfusion-Weighted Imaging Software: A Retrospective Study

INTRODUCTION

The aim of this study was to evaluate the accuracy of automated software (iStroke) on magnetic resonance (MR) apparent diffusion coefficient (ADC) and perfusion-weighted imaging (PWI) against ground truth in assessing infarct core, and compare the hypoperfusion volume and mismatch volume on iStroke with those on Food and Drug Administration-approved software (RAPID) in patients with acute ischemic stroke.

METHODS

We used the single-volume decomposition method to develop the iStroke (iStroke; Beijing Tiantan Hospital, Beijing, China) software. Patients with ischemic stroke were collected from two educational hospitals in China with MR-PWI performed in the emergency department within 24 h of symptom onset. Infarct core volume was defined as ADC < 620 × 10^-6 mm²/s and hypoperfusion volume was defined as Tmax > 6 s. We compared the accuracy of infarct core volume using iStroke and RAPID (iSchema View Inc, Menlo Park, CA) software with ground truth.

RESULTS

We included 405 patients with acute ischemic stroke with MR ADC and PWI sequences. The infarct core volume on iStroke (median 2.43 ml, interquartile range [IQR] 0.60-10.32 ml) was not significantly different from the ground truth (median 2.89 ml, IQR 0.77-9.17 ml) (P = 0.07); Bland-Altman curves showed that the core volume of iStroke and RAPID software were comparable with each other on individual agreement with ground truth. The hypoperfusion volume and mismatch volume on iStroke were not statistically different from those on the RAPID software, respectively. In patients with large vessel occlusion (n = 74), the agreement between iStroke and RAPID was substantial (kappa = 0.76) according to DEFUSE 3 criteria (infarct core < 70 ml, mismatch volume ≥ 15 ml, and mismatch ratio ≥ 1.8).

CONCLUSIONS

The iStroke automatic processing of ADC and PWI is a reliable software for the identification of diffusion-perfusion mismatch in acute ischemic stroke.

Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: a comparison study

PURPOSE

To investigate the association and agreement between magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and computed tomography perfusion (CTP) in determining vascularity and permeability of primary and secondary brain tumors.

MATERIAL AND METHODS

DSC-PWI and CTP studies from 97 patients with high-grade glioma, low-grade glioma and solitary brain metastasis were retrospectively reviewed. Normalized cerebral blood flow (nCBF), cerebral blood volume (nCBV), capillary transfer constant (nK2) and permeability surface area product (nPS) values were obtained. Variables among groups were compared, and correlation and agreement between DSC-PWI and CTP were tested.

RESULTS

All DSC-PWI and CTP parameters were higher in high-grade than in low-grade gliomas (p < 0.01 and p < 0.001). Metastases had greater DSC-PWI nCBV (p < 0.05), nCTP-CBF (p < 0.05), nCTP-CBV (p < 0.01) and nCTP-PS (p < 0.0001) than low-grade gliomas and more elevated nCTP-PS (p < 0.01) than high-grade gliomas. The correlation was strong between DSC-PWI nCBF and CTP nCBF (r = 0.79; p < 0.00001) and between DSC-PWI nCBV and CTP nCBV (r = 0.83; p < 0.00001), weaker between DSC-PWI nK2 and CTP nPS (r = 0.29; p < 0.01). Bland-Altman plots indicated that the agreement was strong between DSC-PWI nCBF and CTP nCBF, good between DSC-PWI nCBV and CTP nCBV and poorer between DSC-PWI nK2 and CTP nPS.

CONCLUSION

DSC-PWI and CTP CBF and CBV maps were comparable and interchangeable in the assessment of tumor vascularity, unlike DSC-PWI K2 and CTP PS maps that were more discordant in the analysis of tumor permeability. CTP could be an alternative method to quantify tumor neoangiogenesis when MRI is not available or when the patient does not tolerate it.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI

RATIONALE AND OBJECTIVES

We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs).

MATERIALS AND METHODS

A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance.

RESULTS

The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001).

CONCLUSION

The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.

Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis

OBJECTIVE

Standard DSC-PWI analyses are based on concrete parameters and values, but an approach that contemplates all points in the time-intensity curves and all voxels in the region-of-interest may provide improved information, and more generalizable models. Therefore, a method of DSC-PWI analysis by means of normalized time-intensity curves point-by-point and voxel-by-voxel is constructed, and its feasibility and performance are tested in presurgical discrimination of glioblastoma and metastasis.

METHODS

In this retrospective study, patients with histologically confirmed glioblastoma or solitary-brain-metastases and presurgical-MR with DSC-PWI (August 2007-March 2020) were retrieved. The enhancing tumor and immediate peritumoral region were segmented on CE-T1wi and coregistered to DSC-PWI. Time-intensity curves of the segmentations were normalized to normal-appearing white matter. For each participant, average and all-voxel-matrix of normalized-curves were obtained. The 10 best discriminatory time-points between each type of tumor were selected. Then, an intensity-histogram analysis on each of these 10 time-points allowed the selection of the best discriminatory voxel-percentile for each. Separate classifier models were trained for enhancing tumor and peritumoral region using binary logistic regressions.

RESULTS

A total of 428 patients (321 glioblastomas, 107 metastases) fulfilled the inclusion criteria (256 men; mean age, 60 years; range, 20-86 years). Satisfactory results were obtained to segregate glioblastoma and metastases in training and test sets with AUCs 0.71-0.83, independent accuracies 65-79%, and combined accuracies up to 81-88%.

CONCLUSION

This proof-of-concept study presents a different perspective on brain MR DSC-PWI evaluation by the inclusion of all time-points of the curves and all voxels of segmentations to generate robust diagnostic models of special interest in heterogeneous diseases and populations. The method allows satisfactory presurgical segregation of glioblastoma and metastases.

KEY POINTS

• An original approach to brain MR DSC-PWI analysis, based on a point-by-point and voxel-by-voxel assessment of normalized time-intensity curves, is presented. • The method intends to extract optimized information from MR DSC-PWI sequences by impeding the potential loss of information that may represent the standard evaluation of single concrete perfusion parameters (cerebral blood volume, percentage of signal recovery, or peak height) and values (mean, maximum, or minimum). • The presented approach may be of special interest in technically heterogeneous samples, and intrinsically heterogeneous diseases. Its application enables satisfactory presurgical differentiation of GB and metastases, a usual but difficult diagnostic challenge for neuroradiologist with vital implications in patient management.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis

BACKGROUND

Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other.

PURPOSE

The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels.

MATERIAL AND METHODS

Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups.

RESULTS

Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case.

CONCLUSION

Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.

Differentiation of Primary Central Nervous System Lymphoma and High-Grade Glioma with Dynamic Susceptibility Contrast-Derived Metrics: Pilot Study

OBJECTIVE

Preoperative differentiation of lymphoma from other aggressive intracranial neoplasms is important as the surgical and adjuvant therapy may be fundamentally different between the 2 types of tumors. The purpose of this study was to assess the ability of the dynamic susceptibility contrast-derived metrics, percentage signal recovery (PSR) ratio, and relative cerebral blood volume (rCBV) to distinguish between primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG).

METHODS

Twenty-six patients (15 with HGG and 11 with PCNSL) with histologically confirmed diagnoses were retrospectively analyzed. Mean PSR and rCBV were calculated from dynamic susceptibility contrast imaging. The 2 groups were compared using an independent samples t-test. Receiver operating characteristic analyses were performed to determine the area under the curve and identify threshold values to differentiate PCNSL from GBM.

RESULTS

Both rCBV and PSR values were significantly different, at both the group level and subject level, between the PCNSL and HGG patients. The mean rCBV was significantly lower in PCNSL (1.38 ± 0.64) compared with HGG (5.19 ± 2.21, df = 11.24, P < 0.001). The mean PSR ratio was significantly higher in PCNSL (1.04 ± 0.11) compared with HGG (0.72 ± 0.16, df = 17.23, P < 0.001). An rCBV threshold value of 2.67 provided a 100% sensitivity and 100% specificity (area under the curve 1.0) for differentiating PCNSL from HGG. A PSR ratio threshold value of 0.9 was 100% sensitive and 90.91% specific for differentiating PCNSL from HGG.

CONCLUSIONS

The findings of our study show that rCBV and PSR ratio are different in HGG and PCNSL at both the group level and subject level. Incorporation of perfusion in routine magnetic resonance imaging of contrast-enhancing lesions can have a significant impact on patient management.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

Imaging Inflammation - From Whole Body Imaging to Cellular Resolution

Imaging techniques have evolved impressively lately, allowing whole new concepts like multimodal imaging, personal medicine, theranostic therapies, and molecular imaging to increase general awareness of possiblities of imaging to medicine field. Here, we have collected the selected (3D) imaging modalities and evaluated the recent findings on preclinical and clinical inflammation imaging. The focus has been on the feasibility of imaging to aid in inflammation precision medicine, and the key challenges and opportunities of the imaging modalities are presented. Some examples of the current usage in clinics/close to clinics have been brought out as an example. This review evaluates the future prospects of the imaging technologies for clinical applications in precision medicine from the pre-clinical development point of view.

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.

Precise discrimination between meningiomas and schwannomas using time-to-signal intensity curves and percentage signal recoveries obtained from dynamic susceptibility perfusion imaging

BACKGROUND AND PURPOSE

Meningiomas and schwannomas are common extra-axial brain tumors. Discrimination is challenging in some locations when characteristic imaging features are absent. This study investigated the accuracy of percentage signal recoveries obtained from dynamic susceptibility contrast perfusion imaging (DSC-PI) in discriminating meningiomas and schwannomas.

MATERIAL AND METHODS

Retrospective database research was conducted. Sixty nine meningioma and 15 schwannoma having DSC-PI between January 2016 and February 2020 were included. Time to signal intensity curves (TSIC) were analyzed and grouped as T1-dominant leakage, T2*-dominant leakage and return to baseline. Relative cerebral blood volume (rCBV), relative mean transit time (rMTT), percentage signal recovery 1 (PSR 1) and PSR 2 values were calculated. The differences between the groups were investigated. Receiver operating characteristic curves were operated.

RESULTS

rCBV, rMTT, PSR 1 and PSR 2 values were statistically different between meningiomas and schwannomas. PSR 2 provided the best discrimination. With the cut off value of 1.08 for PSR 2, meningiomas and schwannomas were differentiated with 95.7% sensitivity and 93.3% specificity. TSICs were also different between two groups. Most of meningiomas showed T2*-dominant leakage (78.2%), whereas most of shwannomas showed T1-dominant leakage (93.3%).

CONCLUSION

DSC-PI is a useful imaging tool for non-invasive discrimination of meningiomas and schwannomas. Particularly, percentage signal recoveries discriminates meningiomas and schwannomas with high sensitivity and specificity.

Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI

BACKGROUND AND PURPOSE

DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma.

MATERIALS AND METHODS

Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset.

RESULTS

A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%).

CONCLUSIONS

The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.

Validation of combined use of DWI and percentage signal recovery-optimized protocol of DSC-MRI in differentiation of high-grade glioma, metastasis, and lymphoma

PURPOSE

With conventional MRI, it is often difficult to effectively differentiate between contrast-enhancing brain tumors, including primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastasis. This study aimed to assess the discrimination ability of the parameters obtained from DWI and the percentage signal recovery- (PSR-) optimized protocol of DSC-MRI between these three tumor types at an initial step.

METHODS

DSC-MRI using a PSR-optimized protocol (TR/TE = 1500/30 ms, flip angle = 90°, no preload) and DWI of 99 solitary enhancing tumors (60 HGGs, 24 metastases, 15 PCNSLs) were retrospectively assessed before treatment. rCBV, PSR, ADC in the tumor core and rCBV, and ADC in peritumoral edema were measured. The differences were evaluated using one-way ANOVA, and the diagnostic performance was evaluated using ROC curve analysis.

RESULTS

PSR in the tumor core showed the best discriminating performance in differentiating these three tumor types with AUC values of 0.979 for PCNSL vs. others and 0.947 for HGG vs. metastasis. The ADC was only helpful in the tumor core and distinguishing PCNSLs from others (AUC = 0.897).

CONCLUSION

Different from CBV-optimized protocols (preload, intermediate FA), PSR derived from the PSR-optimized protocol seems to be the most important parameter in the differentiation of HGGs, metastases, and PCNSLs at initial diagnosis. This property makes PSR remarkable and carries the need for comprehensive DSC-MRI protocols, which provides PSR sensitivity and CBV accuracy together, such as the preload use of the PSR-optimized protocol before the CBV-optimized protocol.

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.

Primary central nervous system lymphoma and 5-aminolevulinic acid

Background:

Despite surgical resection of primary central nervous system lymphomas (PCNSL) having been always discouraged, recent evidence supports that it might improve prognosis in this patient population. Five- aminolevulinic acid-derived fluorescence is widely used for the resection of malignant gliomas, but its role in PCNSL surgery remains unclear.

Case Description:

We present two patients with a solitary solid intraparenchymal mass. As high-grade glioma leaded the list of differential diagnosis (other possibilities were metastasis, abscess, and PCNSL), a five- aminolevulinic acid-guided complete resection (with strong fluorescence in both cases) was done. Surgery was uneventfully carried on with complete resection until five-aminolevulinic acid-induced fluorescence was no longer evident. After surgery, patients have no neurological deficits and had good recovery. Pathological examination revealed that both tumors were PCNSL. Adjuvant radiotherapy and chemotherapy were started. After 1 year of follow-up, patients have good evolution and have no recurrences.

Conclusion:

These cases add to the growing literature which shows that surgery might play an important role in the management of PCNSL with an accessible and single lesion. Five-aminolevulinic acid could also be a useful tool to achieve complete resection and improve prognosis in this group of patients.

Advanced multimodal imaging in differentiating glioma recurrence from post-radiotherapy changes

Gliomas are the most common malignant primary brain tumor, and their prognosis is extremely poor. Radiotherapy is an important treatment for glioma patients, but the changes caused by radiotherapy have brought difficulties in clinical image evaluation because differentiating glioma recurrence from post-radiotherapy changes including pseudo-progression (PD) and radiation necrosis (RN) remains a challenge. Therefore, accurate and reliable imaging evaluation is very important for making clinical decisions. In recent years, advanced multimodal imaging techniques have been applied to achieve the goal of better differentiating glioma recurrence from post-radiotherapy changes for minimizing errors associated with interpretation of treatment effects. In this review, we discuss the recent applications of advanced multimodal imaging such as diffusion MRI sequences, amide proton transfer MRI sequences, perfusion MRI sequences, MR spectroscopy and multinuclides PET/CT in the evaluation of post-radiotherapy treatment response in glioma patients and highlight their potential role in differentiating post-radiotherapy changes from glioma recurrence.

Imaging of skull vault tumors in adults

The skull vault, formed by the flat bones of the skull, has a limited spectrum of disease that lies between the fields of neuro- and musculoskeletal radiology. Its unique abnormalities, as well as other ubiquitous ones, present particular features in this location. Moreover, some benign entities in this region may mimic malignancy if analyzed using classical bone-tumor criteria, and proper patient management requires being familiar with these presentations. This article is structured as a practical review offering a systematic diagnostic approach to focal calvarial lesions, broadly organized into four categories: (1) pseudolesions: arachnoid granulations, meningo-/encephaloceles, vascular canals, frontal hyperostosis, parietal thinning, parietal foramina, and sinus pericrani; (2) lytic: fibrous dysplasia, epidermal inclusion and dermoid cysts, eosinophilic granuloma, hemangioma, aneurysmal bone cyst, giant cell tumor, metastasis, and myeloma; (3) sclerotic: osteomas, osteosarcoma, and metastasis; (4) transdiploic: meningioma, hemangiopericytoma, lymphoma, and metastasis, along with other less common entities. Tips on the potential usefulness of functional imaging techniques such as MR dynamic susceptibility (T2*) perfusion, MR spectroscopy, diffusion-weighted imaging, and PET imaging are provided.

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.

Utility of Percentage Signal Recovery and Baseline Signal in DSC-MRI Optimized for Relative CBV Measurement for Differentiating Glioblastoma, Lymphoma, Metastasis, and Meningioma

BACKGROUND AND PURPOSE

The percentage signal recovery in non-leakage-corrected (no preload, high flip angle, intermediate TE) DSC-MR imaging is known to differ significantly for glioblastoma, metastasis, and primary CNS lymphoma. Because the percentage signal recovery is influenced by preload and pulse sequence parameters, we investigated whether the percentage signal recovery can still differentiate these common contrast-enhancing neoplasms using a DSC-MR imaging protocol designed for relative CBV accuracy (preload, intermediate flip angle, low TE).

MATERIALS AND METHODS

We retrospectively analyzed DSC-MR imaging of treatment-naïve, pathology-proved glioblastomas (n = 14), primary central nervous system lymphomas (n = 7), metastases (n = 20), and meningiomas (n = 13) using a protocol designed for relative CBV accuracy (a one-quarter-dose preload and single-dose bolus of gadobutrol, TR/TE = 1290/40 ms, flip angle = 60° at 1.5T). Mean percentage signal recovery, relative CBV, and normalized baseline signal intensity were compared within contrast-enhancing lesion volumes. Classification accuracy was determined by receiver operating characteristic analysis.

RESULTS

Relative CBV best differentiated meningioma from glioblastoma and from metastasis with areas under the curve of 0.84 and 0.82, respectively. The percentage signal recovery best differentiated primary central nervous system lymphoma from metastasis with an area under the curve of 0.81. Relative CBV and percentage signal recovery were similar in differentiating primary central nervous system lymphoma from glioblastoma and from meningioma. Although neither relative CBV nor percentage signal recovery differentiated glioblastoma from metastasis, mean normalized baseline signal intensity achieved 86% sensitivity and 50% specificity.

CONCLUSIONS

Similar to results for non-preload-based DSC-MR imaging, percentage signal recovery for one-quarter-dose preload-based, intermediate flip angle DSC-MR imaging differentiates most pair-wise comparisons of glioblastoma, metastasis, primary central nervous system lymphoma, and meningioma, except for glioblastoma versus metastasis. Differences in normalized post-preload baseline signal for glioblastoma and metastasis, reflecting a snapshot of dynamic contrast enhancement, may motivate the use of single-dose multiecho protocols permitting simultaneous quantification of DSC-MR imaging and dynamic contrast-enhanced MR imaging parameters.

Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object

PURPOSE

Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T₁ and T 2 ∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T₁ leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol.

METHODS

Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols.

RESULTS

Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV.

CONCLUSIONS

Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T 2 ∗ leakage effects remains compulsory for high rCBV accuracy.

Comparative Study of Dynamic Susceptibility Contrast Perfusion MR Images between Warthin&apos;s Tumor and Malignant Parotid Tumors

Objectives To identify diagnostically meaningful differences between Warthin's tumor and malignant masses in the parotid gland by dynamic susceptibility contrast (DSC) MR imaging. Methods Eleven malignant parotid tumors and 9 Warthin's tumors were included. MR imaging was performed on all patients. Signal intensity time curves of tumors were obtained by DSC MR imaging and dynamic susceptibility contrast percentages (DSC%) were calculated. Results No significant difference was observed between malignant tumors and Warthin's tumors (P = 0.437), although DSC% values tended to be higher for Warthin's tumors. Conclusions Warthin's tumor tended to have higher DSC% values than malignant parotid tumors, but this difference was not significantly different.

Neuroimaging of acute and chronic unilateral and bilateral thalamic lesions

The thalami are bilateral ovoid grey matter cerebral structures bordering the third ventricle on both sides, which participate in functions such as relaying of sensory and motor signals, regulation of consciousness, and alertness. Pathologies affecting the thalami can be of neoplastic, infectious, vascular, toxic, metabolic, or congenital origin.The purpose of this review is to provide a comprehensive approach to the thalamus focusing on its anatomy, the main pathologies affecting this structure and their radiological semiology on CT and MRI. We will also illustrate the importance of multimodal MR imaging (morphologic sequences, diffusion-weighted imaging, perfusion, spectroscopy) for the diagnosis and treatment of these conditions.

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.

The role of diffusion and perfusion magnetic resonance imaging in differentiation of haemangioblastomas and pilocytic astrocytomas

Purpose

Haemangioblastomas (HABLs) and pilocytic astrocytomas (PAs) are brain tumours presenting similar appearance and location in conventional magnetic resonance (MR) imaging. The purpose of our study was to determine whether a detailed analysis of diffusion (DWI) and perfusion (PWI) characteristics can be useful in preoperative differentiation of these tumours.

Material and methods

The study group consisted of biopsy proven six HABLs and six PAs, which underwent preoperative standard MR examinations including PWI and DWI. In PWI relative cerebral blood volume (rCBV) and the shape of perfusion curves (parameters of peak height - rPH and percentage of signal recovery - rPSR) were analysed. All perfusion parameters were measured for the entire tumour core (mean rCBV, mean rPH, mean rPSR) and in regions with maximal values (max rCBV, max rPH, max rPSR). In DWI parameters of apparent diffusion coefficient (ADC) from the entire tumour core (mean ADC) and in regions with minimal values (min ADC) were evaluated.

Results

Compared to PAs, HABLs presented significantly higher rCBV and rPH values and lower mean rPSR value. PAs showed significantly lower rCBV and rPH values and higher mean rPSR value. Mean rCBV showed no overlap in the values between HABLs and PAs, and thus it provided the highest accuracy in differentiating between them. Max rPSR, mean ADC, and min ADC did not show any significant differences.

Conclusions

High rCBV values and deep perfusion curves with only partial return to the baseline are characteristic features of HABLs differentiating them from PAs, which show lower rCBV values and perfusion curves overshooting the baseline. Diffusion parameters are not useful in differentiation of these tumours.

Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object

BACKGROUND AND PURPOSE

The accuracy of DSC-MR imaging CBV maps in glioblastoma depends on acquisition and analysis protocols. Multisite protocol heterogeneity has challenged standardization initiatives due to the difficulties of in vivo validation. This study sought to compare the accuracy of routinely used protocols using a digital reference object.

MATERIALS AND METHODS

The digital reference object consisted of approximately 10,000 simulated voxels recapitulating typical signal heterogeneity encountered in vivo. The influence of acquisition and postprocessing methods on CBV reliability was evaluated across 6912 parameter combinations, including contrast agent dosing schemes, pulse sequence parameters, field strengths, and postprocessing methods. Accuracy and precision were assessed using the concordance correlation coefficient and coefficient of variation.

RESULTS

Across all parameter space, the optimal protocol included full-dose contrast agent preload and bolus, intermediate (60°) flip angle, 30-ms TE, and postprocessing with a leakage-correction algorithm (concordance correlation coefficient = 0.97, coefficient of variation = 6.6%). Protocols with no preload or fractional dose preload and bolus using these acquisition parameters were generally less robust. However, a protocol with no preload, full-dose bolus, and low (30°) flip angle performed very well (concordance correlation coefficient = 0.93, coefficient of variation = 8.7% at 1.5T and concordance correlation coefficient = 0.92, coefficient of variation = 8.2% at 3T).

CONCLUSIONS

Schemes with full-dose preload and bolus maximize CBV accuracy and reduce variability, which could enable smaller sample sizes and more reliable detection of CBV changes in clinical trials. When a lower total contrast agent dose is desired, use of a low flip angle, no preload, and full-dose bolus protocol may provide an attractive alternative.