Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

Solitary Tumefactive Demyelinating Lesions in Children: Clinical and Magnetic Resonance Imaging Features, Pathologic Characteristics, and Outcomes

BACKGROUND

Isolated tumefactive demyelinating lesions (≥2 cm) may be difficult to distinguish from contrast-enhancing brain tumors, central nervous system infections, and (rarely) tissue dysgenesis, which may all occur with increased signal on T2-weighted images. Establishing an accurate diagnosis is essential for management, and we delineate our single-center experience.

METHODS

We performed a retrospective review of medical records, imaging, and biopsy specimens for patients under 18 years presenting with isolated tumefactive demyelination over a 10-year period.

RESULTS

Ten children (eight female) met inclusion criteria, with a median age of 14.1 years. Lesions were most likely to involve the thalamus (six of 10), brainstem (five of 10), basal ganglia (four of 10), or corpus callosum (four of 10). Eighty percent had perilesional edema at presentation, and 60% had midline shift. Biopsies demonstrated demyelination with perivascular lymphocytic infiltration and axonal damage ranging from mild to severe. All patients were initially treated with high-dose corticosteroids, and eight of 10 required additional medical therapies such as intravenous immunoglobulin, plasmapheresis, cyclophosphamide, or rituximab. Increased intracranial pressure was managed aggressively with two of 10 patients requiring decompressive craniectomies. Clinical outcomes varied.

CONCLUSIONS

Solitary tumefactive demyelinating lesions are rare, and aggressive management of inflammation and increased intracranial pressure is essential. Biopsy is helpful to evaluate for other diagnoses on the differential and maximize therapies. Treatment beyond initial therapy with corticosteroids is often required. Isolated tumefactive demyelinating lesions are uncommon; multicenter natural history studies are needed to better delineate differential diagnoses and optimal therapies.

Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging

Purpose

To investigate the predictive value of multi-parameters derived from advanced MR imaging for Ki-67 labeling index (LI) in glioma patients.

Materials and Methods

One hundred and nine patients with histologically confirmed gliomas were evaluated retrospectively. These patients underwent advanced MR imaging, including dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), MR spectroscopy imaging (MRS), diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), before treatment. Twenty-one parameters were extracted, including the maximum, minimum and mean values of relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative mean transit time (rMTT), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA) and relative mean diffusivity (rMD) respectively, and ration of choline (Cho)/creatine (Cr), Cho/N-acetylaspartate (NAA) and NAA/Cr. Stepwise multivariate regression was performed to build multivariate models to predict Ki-67 LI. Pearson correlation analysis was used to investigate the correlation between imaging parameters and the grade of glioma. One-way analysis of variance (ANOVA) was used to explore the differences of the imaging parameters among the gliomas of grade II, III, and IV.

Results

The multivariate regression showed that the model of five parameters, including rCBVmax (RC=0.282), rCBFmax (RC=0.151), rADCmin (RC= -0.14), rFAmax (RC=0.325) and Cho/Cr ratio (RC=0.157) predicted the Ki-67 LI with a root mean square (RMS) error of 0. 0679 (R2 = 0.8025).The regression check of this model showed that there were no multicollinearity problem (variance inflation factor: rCBVmax, 3.22; rCBFmax, 3.14; rADCmin, 1.96; rFAmax, 2.51; Cho/Cr ratio, 1.64), and the functional form of this model was appropriate (F test: p=0.682). The results of Pearson correlation analysis showed that the rCBVmax, rCBFmax, rFAmax, the ratio of Cho/Cr and Cho/NAA were positively correlated with Ki-67 LI and the grade of glioma, while the rADCmin and rMDmin were negatively correlated with Ki-67 LI and the grade of glioma.

Conclusion

Combining multiple parameters derived from DSC, DTI, DWI and MRS can precisely predict the Ki-67 LI in glioma patients.

Arterial spin labeled perfusion MRI for the assessment of radiation-treated meningiomas

Purpose

Conventional contrast-enhanced MRI is currently the primary imaging technique used to evaluate radiation treatment response in meningiomas. However, newer perfusion-weighted MRI techniques, such as 3D pseudocontinuous arterial spin labeling (3D pCASL) MRI, capture physiologic information beyond the structural information provided by conventional MRI and may provide additional complementary treatment response information. The purpose of this study is to assess 3D pCASL for the evaluation of radiation-treated meningiomas.

Methods

Twenty patients with meningioma treated with surgical resection followed by radiation, or by radiation alone, were included in this retrospective single-institution study. Patients were evaluated with 3D pCASL and conventional contrast-enhanced MRI before and after radiation (median follow up 6.5 months). Maximum pre- and post-radiation ASL normalized cerebral blood flow (ASL-nCBF) was measured within each meningioma and radiation-treated meningioma (or residual resected and radiated meningioma), and the contrast-enhancing area was measured for each meningioma. Wilcoxon signed-rank tests were used to compare pre- and post-radiation ASL-nCBF and pre- and post-radiation area.

Results

All treated meningiomas demonstrated decreased ASL-nCBF following radiation (p < 0.001). Meningioma contrast-enhancing area also decreased after radiation (p = 0.008) but only for approximately half of the meningiomas (9), while half (10) remained stable. A larger effect size (Wilcoxon signed-rank effect size) was seen for ASL-nCBF measurements (r = 0.877) compared to contrast-enhanced area measurements (r = 0.597).

Conclusions

ASL perfusion may provide complementary treatment response information in radiation-treated meningiomas. This complementary information could aid clinical decision-making and provide an additional endpoint for clinical trials.

Velocity-Selective Arterial Spin Labeling Perfusion in Monitoring High Grade Gliomas Following Therapy: Clinical Feasibility at 1.5T and Comparison with Dynamic Susceptibility Contrast Perfusion

MR perfusion imaging is important in the clinical evaluation of primary brain tumors, particularly in differentiating between true progression and treatment-induced change. The utility of velocity-selective ASL (VSASL) compared to the more commonly utilized DSC perfusion technique was assessed in routine clinical surveillance MR exams of 28 patients with high-grade gliomas at 1.5T. Using RANO criteria, patients were assigned to two groups, one with detectable residual/recurrent tumor ("RT", n = 9), and the other with no detectable residual/recurrent tumor ("NRT", n = 19). An ROI was drawn to encompass the largest dimension of the lesion with measures normalized against normal gray matter to yield rCBF and tSNR from VSASL, as well as rCBF and leakage-corrected relative CBV (lc-rCBV) from DSC. VSASL (rCBF and tSNR) and DSC (rCBF and lc-rCBV) metrics were significantly higher in the RT group than the NRT group allowing adequate discrimination (p < 0.05, Mann-Whitney test). Lin's concordance analyses showed moderate to excellent concordance between the two methods, with a stronger, moderate correlation between VSASL rCBF and DSC lc-rCBV (r = 0.57, p = 0.002; Pearson's correlation). These results suggest that VSASL is clinically feasible at 1.5T and has the potential to offer a noninvasive alternative to DSC perfusion in monitoring high-grade gliomas following therapy.

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.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient

BACKGROUND

This study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.

METHODS

A retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δeff = 7.1 ms) and conventional pulsed gradient (Δeff = 44.5 ms). In addition to ADC maps at the two diffusion times (ADC7.1 ms and ADC44.5 ms), we generated maps of the ADC changes (cADC) and the relative ADC changes (rcADC) between the two diffusion times. Regions of interest were placed on enhancing regions and non-enhancing peritumoral regions. The mean and the fifth and 95th percentile values of each parameter were compared between PCNSLs and GBMs. The area under the receiver operating characteristic curve (AUC) values were used to compare the discriminating performances among the indices.

RESULTS

In enhancing regions, the mean and fifth and 95th percentile values of ADC44.5 ms and ADC7.1 ms in PCNSLs were significantly lower than those in GBMs (p = 0.02 for 95th percentile of ADC44.5 ms, p = 0.04 for ADC7.1 ms, and p < 0.01 for others). Furthermore, the mean and fifth and 95th percentile values of cADC and rcADC were significantly higher in PCNSLs than in GBMs (each p < 0.01). The AUC of the best-performing index for ADC7.1 ms was significantly lower than that for ADC44.5 ms (p < 0.001). The mean rcADC showed the highest discriminating performance (AUC = 0.920) among all indices. In peritumoral regions, no significant difference in any of the three indices of ADC44.5 ms, ADC7.1 ms, cADC, and rcADC was observed between PCNSLs and GBMs.

CONCLUSIONS

Effective diffusion time setting can have a crucial impact on the performance of ADC in differentiating between PCNSLs and GBMs. The time-dependent diffusion MRI parameters may be useful in the differentiation of these lesions.

Comparison of clinically available dynamic susceptibility contrast post processing software to differentiate progression from pseudoprogression in post-treatment high grade glioma

INTRODUCTION

The purpose of this retrospective study was to compare two, widely available software packages for calculation of Dynamic Susceptibility Contrast (DSC) perfusion MRI normalized relative Cerebral Blood Volume (rCBV) values to differentiate tumor progression from pseudoprogression in treated high-grade glioma patients.

MATERIAL AND METHODS

rCBV maps processed by Siemens Syngo.via (Siemens Healthineers) and Olea Sphere (Olea Medical) software packages were co-registered to contrast-enhanced T1 (T1-CE). Regions of interest based on T1-CE were transferred to the rCBV maps. rCBV was calculated using mean values and normalized using contralateral normal- appearing white matter. The Wilcoxon test was performed to assess for significant differences, and software-specific optimal rCBV cutoff values were determined using the Youden index. Interrater reliability was evaluated for two raters using the intraclass correlation coefficient.

RESULTS

41 patients (18 females; median age = 59 years; range 21-77 years) with 49 new or size-increasing post-treatment contrast-enhancing lesions were included (tumor progression = 40 lesions; pseudoprogression = 9 lesions). Optimal rCBV cutoffs of 1.31 (Syngo.via) and 2.40 (Olea) were significantly different, with an AUC of 0.74 and 0.78, respectively. Interrater reliability was 0.85.

DISCUSSION

We demonstrate that different clinically available MRI DSC-perfusion software packages generate significantly different rCBV cutoff values for the differentiation of tumor progression from pseudoprogression in standard-of-care treated high grade gliomas. Physicians may want to determine the unique value of their perfusion software packages on an institutional level in order to maximize diagnostic accuracy when faced with this clinical challenge. Furthermore, combined with implementation of current DSC-perfusion recommendations, multi-center comparability will be improved.

Advanced Meningioma Imaging

Noninvasive imaging methods are used to accurately diagnose meningiomas and track their growth and location. These techniques, including computed tomography, MRI, and nuclear medicine, are also being used to gather more information about the biology of the tumors and potentially predict their grade and impact on prognosis. In this article, we will discuss the current and developing uses of these imaging techniques including additional analysis using radiomics in the diagnosis and treatment of meningiomas, including treatment planning and prediction of tumor behavior.

Efficacy of three-dimensional arterial spin labeling and how it compares against that of contrast enhanced magnetic resonance imaging in preoperative grading of brain gliomas

PURPOSE

To evaluate the efficacy of three-dimensional arterial spin labeling (3D-ASL) imaging in preoperative grading of brain gliomas, and compare the discrepancy between images obtained from 3D-ASL and contrast enhanced magnetic resonance imaging (CE-MRI) in grading of gliomas.

METHODS

Fifty-one patients with brain gliomas received plain MRI, CE-MRI and 3D-ASL scanning before surgery. In 3D-ASL images, the maximum tumor blood flow (TBF) of tumor parenchyma was measured, relative TBF-M and rTBF-WM were calculated. The cases were categorized into "ASL dominant" and "CE dominant" to compare the discrepancy between 3D-ASL and CE-MRI results. Independent samples t test, Mann-Whitney and U test and one-way analysis of variance (ANOVA) were performed to test the differences of TBF, rTBF-M and rTBF-WM values among brain gliomas with different grades. Spearman rank correlation analysis was performed to assess the correlation between TBF, rTBF-M, rTBF-WM and glioma grades respectively. To compare the discrepancy between 3D-ASL and CE-MRI results.

RESULTS

In high-grade gliomas (HGG) group, TBF, rTBF-M and rTBF-WM values were higher than those in low-grade gliomas (LGG) group (p < .05). Multiple comparison showed TBF and rTBF-WM values were different between grade I and IV gliomas, grade II and IV gliomas (both p < .05), the rTBF-M value was different between grade I and IV gliomas (p < .05). The values of all 3D-ASL derived parameters were positively correlated with gliomas grading (all p < .001). TBF showed highest specificity (89.3%) and rTBF-WM showed highest sensitivity (96.4%) when discriminating LGG and HGG using ROC curve. There were 29 CE dominant cases (23 cases were HGG), 9 ASL dominant cases (4 cases were HGG).  CONCLUSION: 3D-ASL is of significance to preoperative grading of brain gliomas and might be more sensitive than CE-MRI in detection of tumor perfusion.

Radiological Biomarkers for Brain Metastases Prognosis: Quantitative Magnetic Resonance Imaging (MRI) Modalities As Non-invasive Biomarkers for the Effect of Radiotherapy

Radiotherapy effect is achieved by its ability to cause DNA damage and induce apoptosis. In contrast, radiation can induce tumor cells' proliferation, invasiveness, and epithelial-mesenchymal transition (EMT). Besides developing radioresistance, this paradoxical effect of radiotherapy is considered a challenging problem in the field of radiotherapy. This highlights the importance of developing new modalities to diagnose radioresistance early to avoid any unnecessary exposure to radiation and differentiate between metastases recurrence versus post-radiation changes. Quantitative magnetic resonance imaging (MRI) techniques including diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast-enhanced (DCE) represent potential biomarkers to diagnose metastases recurrence and radioresistance. In this review, we will focus on recent studies discussing the possibility of using DWI, DSC, ASL, and DCE to diagnose radioresistance and recurrence in patients with brain metastases.

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.

Challenges and opportunities for advanced neuroimaging of glioblastoma

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

Neuroimaging for differential diagnosis of transient neurological attacks

BACKGROUND

Rapid yet comprehensive neuroimaging protocols are required for patients with suspected acute stroke. However, stroke mimics can account for approximately one in five clinically diagnosed acute ischemic strokes and the rate of thrombolyzed mimics can be as high as 17%. Therefore, to accurately determine the diagnosis and differentiate mimics from true transient ischemic attacks, acute ischemic stroke is a challenge to every clinician.

DISCUSSION

Medical history and neurological examination, noncontract head computed tomography, and routine magnetic resonance imaging play important roles in the assessment and management of patients with transient neurological attacks in the emergency department. This review attempts to summarize how neuroimaging can be utilized to help differentiate the most common mimics from transient ischemic attack and acute ischemic stroke.

CONCLUSION

Although imaging can help direct critical triage decisions for intravenous thrombolysis or endovascular therapy, more detailed medical history and neurological examination are crucial for making a prompt and accurate diagnosis for transient neurological attack patients.

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.

Reliability of dynamic susceptibility contrast perfusion metrics in pre- and post-treatment glioma

Background

In neuro-oncology, dynamic susceptibility contrast magnetic resonance (DSC-MR) perfusion imaging emerged as a tool to aid in the diagnostic work-up and to surveil effectiveness of treatment. However, it is believed that a significant variability exists with regard to the measured in DSC-MR perfusion parameters. The aim of this study was to assess the observer variability in measured DSC-MR perfusion parameters in patients before and after treatment. In addition, we investigated whether region-of-interest (ROI) shape impacted the observer variability.

Materials and methods

Twenty non-treated patients and a matched group of twenty patients post-treatment (neurosurgical resection and post-chemoradiotherapy) were included. Six ROIs were independently placed by three readers: circular ROIs and polygonal ROIs covering 1) the tumor hotspot; 2) the peritumoral region (T2/FLAIR-hyperintense region) and 3) the whole tumor region. A two-way random Intra-class coefficient (ICC) model was used to assess variability in measured DSC-MRI perfusion parameters. The perfusion metrics as assessed by the circular and the polygonal ROI were compared by use of the dependent T-test.

Results

In the non-treated group, circular ROIs showed good–excellent overlap (ICC-values ranging from 0.741–0.963) with the exception of those representing the tumor hotspot. Polygonal ROIs showed lower ICC-values, ranging from 0.113 till 0.856. ROI-placement in the posttreatment group showed to be highly variable with a significant deterioration of ICC-values. Furthermore, perfusion metric assessment in similar tumor regions was not impacted by ROI shape.

Discussion

This study shows that posttreatment quantitative interpretation of DSC-MR perfusion imaging is highly variable and should be carried out with precaution. Pretreatment assessment of DSC-MR images, however, could be carried out be a single reader in order to provide valid data for further analyses.

• DSC-MR perfusion imaging measurements in non-treated glioma is highly reliable between readers, even readers with little experience.

• DSC-MR perfusion imaging measurements in treated glioma is show to be inconsistent between readers.

• When using DSC-MR perfusion imaging as a quantitative surveillance tool for the recurrence of glioma after treatment, double-reading should be preferred.

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

OBJECTIVE

To compare conventional magnetic resonance imaging (MRI), susceptibility-weighted imaging (SWI), and perfusion-weighted imaging (PWI) characteristics in different grades of trigone meningiomas.

METHODS

Thirty patients with trigone meningiomas were enrolled in this retrospective study. Conventional MRI was performed in all patients; SWI (17 cases), dynamic contrast-enhanced PWI (10 cases), and dynamic susceptibility contrast PWI (6 cases) were performed. Demographics, conventional MRI features, SWI- and PWI-derived parameters were compared between different grades of trigone meningiomas.

RESULTS

On conventional MRI, the irregularity of tumor shape (ρ = 0.497, P = 0.005) and the extent of peritumoral edema (ρ = 0.187, P = 0.022) might help distinguish low-grade and high-grade trigone meningiomas. On multiparametric functional MRI, rTTPmax (1.17 ± 0.06 vs 1.30 ± 0.05, P = 0.048), Kep, Ve, and iAUC demonstrated their potentiality to predict World Health Organization grades I, II, and III trigone meningiomas.

CONCLUSIONS

Conventional MRI combined with dynamic susceptibility contrast and dynamic contrast-enhanced can help predict the World Health Organization grade of trigone meningiomas.

Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma

Background

Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients.

Methods

The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis.

Results

The perfusion parameter with the least RC was the plasma volume fraction (v _p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v _e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K ^trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF.

Conclusions

As much as 72% change in K ^trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K ^trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.

Preliminary study of multiple b-value diffusion-weighted images and T1 post enhancement magnetic resonance imaging images fusion with Laplacian Re-decomposition (LRD) medical fusion algorithm for glioma grading

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LRD medical image fusion algorithm can be used for glioma grading.

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We can use the LRD fusion algorithm with MRI image for glioma grading.

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Fusing of DWI (b50) and T₁ enhancement (T₁Gd) by LRD, have highest diagnostic value for glioma grading.

Background

Grade of brain tumor is thought to be the most significant and crucial component in treatment management. Recent development in medical imaging techniques have led to the introduce non-invasive methods for brain tumor grading such as different magnetic resonance imaging (MRI) protocols. Combination of different MRI protocols with fusion algorithms for tumor grading is used to increase diagnostic improvement. This paper investigated the efficiency of the Laplacian Re-decomposition (LRD) fusion algorithms for glioma grading.

Procedures

In this study, 69 patients were examined with MRI. The T1 post enhancement (T1Gd) and diffusion-weighted images (DWI) were obtained. To evaluated LRD performance for glioma grading, we compared the parameters of the receiver operating characteristic (ROC) curves.

Findings

We found that the average Relative Signal Contrast (RSC) for high-grade gliomas is greater than RSCs for low-grade gliomas in T1Gd images and all fused images. No significant difference in RSCs of DWI images was observed between low-grade and high-grade gliomas. However, a significant RSCs difference was detected between grade III and IV in the T1Gd, b50, and all fussed images.

Conclusions

This research suggests that T1Gd images are an appropriate imaging protocol for separating low-grade and high-grade gliomas. According to the findings of this study, we may use the LRD fusion algorithm to increase the diagnostic value of T1Gd and DWI picture for grades III and IV glioma distinction. In conclusion, this article has emphasized the significance of the LRD fusion algorithm as a tool for differentiating grade III and IV gliomas.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

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.

Assessment of tumor hypoxia and perfusion in recurrent glioblastoma following bevacizumab failure using MRI and 18F-FMISO PET

Tumoral hypoxia correlates with worse outcomes in glioblastoma (GBM). While bevacizumab is routinely used to treat recurrent GBM, it may exacerbate hypoxia. Evofosfamide is a hypoxia-targeting prodrug being tested for recurrent GBM. To characterize resistance to bevacizumab and identify those with recurrent GBM who may benefit from evofosfamide, we ascertained MRI features and hypoxia in patients with GBM progression receiving both agents. Thirty-three patients with recurrent GBM refractory to bevacizumab were enrolled. Patients underwent MR and ¹⁸F-FMISO PET imaging at baseline and 28 days. Tumor volumes were determined, MRI and ¹⁸F-FMISO PET-derived parameters calculated, and Spearman correlations between parameters assessed. Progression-free survival decreased significantly with hypoxic volume [hazard ratio (HR) = 1.67, 95% confidence interval (CI) 1.14 to 2.46, P = 0.009] and increased significantly with time to the maximum value of the residue (Tmax) (HR = 0.54, 95% CI 0.34 to 0.88, P = 0.01). Overall survival decreased significantly with hypoxic volume (HR = 1.71, 95% CI 1.12 to 12.61, p = 0.01), standardized relative cerebral blood volume (srCBV) (HR = 1.61, 95% CI 1.09 to 2.38, p = 0.02), and increased significantly with Tmax (HR = 0.31, 95% CI 0.15 to 0.62, p < 0.001). Decreases in hypoxic volume correlated with longer overall and progression-free survival, and increases correlated with shorter overall and progression-free survival. Hypoxic volume and volume ratio were positively correlated (r_s = 0.77, P < 0.0001), as were hypoxia volume and T1 enhancing tumor volume (r_s = 0.75, P < 0.0001). Hypoxia is a key biomarker in patients with bevacizumab-refractory GBM. Hypoxia and srCBV were inversely correlated with patient outcomes. These radiographic features may be useful in evaluating treatment and guiding treatment considerations.

Vessel Type Determined by Vessel Architectural Imaging Improves Differentiation between Early Tumor Progression and Pseudoprogression in Glioblastoma

BACKGROUND AND PURPOSE

Currently available perfusion parameters are limited in differentiating early tumor progression and pseudoprogression with no insight about vessel size and type. We aimed to investigate differences in vessel size and type between early tumor progression and pseudoprogression in posttreatment glioblastoma and to demonstrate diagnostic performance using vessel architectural imaging.

MATERIALS AND METHODS

Fifty-eight patients with enlarging contrast-enhancing masses in posttreatment glioblastomas underwent simultaneous gradient recalled-echo and spin-echo dynamic susceptibility contrast imaging. Relative CBV and vessel architectural imaging parameters, including the relative vessel size index, peak shift between gradient recalled echo and spin-echo bolus signal peaks, and arterial dominance scores using spatial dominance of arterial/venous vessel type, were calculated and compared between the 2 conditions. The area under the curve and cross-validation were performed to compare the diagnostic performance of the relative CBV, vessel architectural imaging parameters, and their combinations.

RESULTS

There were 41 patients with early tumor progression and 17 patients with pseudoprogression. Relative to pseudoprogression, early tumor progression showed a lower peak shift (-0.02 versus 0.33, P = .02) and a lower arterial dominance score (1.46 versus 2.11, P = .001), indicating venous dominance. Patients with early tumor progression had higher relative CBV (1.88 versus 1.38, P = .02) and a tendency toward a larger relative vessel size index (99.67 versus 83.17, P = .15) than those with pseudoprogression. Combining arterial dominance scores and relative CBV showed significantly higher diagnostic performance (area under the curve = 0.82; 95% CI, 0.70-0.94; P = .02) than relative CBV alone (area under the curve = 0.64; 95% CI, 0.49-0.79) in distinguishing early tumor progression from pseudoprogression.

CONCLUSIONS

Vessel architectural imaging significantly improved the diagnostic performance of relative CBV by demonstrating venous dominance and a tendency toward larger vessel size in early tumor progression.

Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 - Radiological features and metric markers

After chemoradiotherapy for glioblastoma, pseudoprogression can occur and must be distinguished from true progression to correctly manage glioblastoma treatment and follow-up. Conventional treatment response assessment is evaluated via conventional MRI (contrast-enhanced T1-weighted and T2/FLAIR), which is unreliable. The emergence of advanced MRI techniques, MR spectroscopy, and PET tracers has improved pseudoprogression diagnostic accuracy. This review presents a literature review of the different imaging techniques and potential imaging biomarkers to differentiate pseudoprogression from true progression.

Inflow-based vascular-space-occupancy (iVASO) might potentially predict IDH mutation status and tumor grade in diffuse cerebral gliomas

PURPOSE

The aim of the study is to assess the diagnostic performance of inflow-based vascular-space-occupancy (iVASO) MR imaging for differentiating glioblastomas (grade IV, GBM) and lower-grade diffuse gliomas (grade II and III, LGG) and its potential to predict IDH mutation status.

METHODS

One hundred and two patients with diffuse cerebral glioma (56 males; median age, 43.5 years) underwent iVASO and dynamic susceptibility contrast (DSC) MR imaging. The iVASO-derived arteriolar cerebral blood volume (CBVa), relative CBVa (rCBVa), and the DSC-derived relative cerebral blood volume (rCBV) were obtained, and these measurements were compared between the GBM group (n = 43) and the LGG group (n = 59) and between the IDH-mutation group (n = 54) and the IDH-wild group (n = 48).

RESULTS

Significant correlation was observed between rCBV and CBVa (P < 0.001) or rCBVa (P < 0.001). Both CBVa (P < 0.001) and rCBVa (P < 0.001) were higher in the GBM group. Both CBVa (P < 0.001) and rCBVa (P < 0.001) were lower in the IDH-mutation group compared to the IDH-wild group. Receiver operating characteristic analyses showed the area under curve (AUC) of 0.95 with CBVa and 0.97 with rCBVa in differentiating GBM from LGG. The AUCs were 0.82 and 0.85 for CBVa and rCBVa in predicting IDH gene status, respectively, which were lower than that of rCBV (AUC = 0.90). Combined rCBV and rCBVa significantly improved the diagnostic performance (AUC = 0.95).

CONCLUSIONS

iVASO MR imaging has the potential to predict IDH mutation and grade in glioma.

Conventional and advanced imaging throughout the cycle of care of gliomas

Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.

Gamma distribution model of diffusion MRI for the differentiation of primary central nerve system lymphomas and glioblastomas

The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) <1.0×10-3 mm2/sec; f2, D >1.0×10-3 and <3.0×10-3 mm2/sec; f3, D >3.0 × 10-3 mm2/sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p<0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p<0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p<0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs.

A feasibility study to evaluate early treatment response of brain metastases one week after stereotactic radiosurgery using perfusion weighted imaging

Background

To explore if early perfusion-weighted magnetic resonance imaging (PWI) may be a promising imaging biomarker to predict local recurrence (LR) of brain metastases after stereotactic radiosurgery (SRS).

Methods

This is a prospective pilot study of adult brain metastasis patients who were treated with SRS and imaged with PWI before and 1 week later. Relative cerebral blood volume (rCBV) parameter maps were calculated by normalizing to the mean value of the contralateral white matter on PWI. Cox regression was conducted to explore factors associated with time to LR, with Bonferroni adjusted p<0.0006 for multiple testing correction. LR rates were estimated with the Kaplan-Meier method and compared using the log-rank test.

Results

Twenty-three patients were enrolled from 2013 through 2016, with 22 evaluable lesions from 16 patients. After a median follow-up of 13.1 months (range: 3.0–53.7), 5 lesions (21%) developed LR after a median of 3.4 months (range: 2.3–5.7). On univariable analysis, larger tumor volume (HR 1.48, 95% CI 1.02–2.15, p = 0.04), lower SRS dose (HR 0.45, 95% CI 0.21–0.97, p = 0.04), and higher rCBV at week 1 (HR 1.07, 95% CI 1.003–1.14, p = 0.04) had borderline association with shorter time to LR. Tumors >2.0cm³ had significantly higher LR than if ≤2.0cm³: 54% vs 0% at 1 year, respectively, p = 0.008. A future study to confirm the association of early PWI and LR of the high-risk cohort of lesions >2.0cm³ is estimated to require 258 patients.

Conclusions

PWI at week 1 after SRS may have borderline association with LR. Tumors <2.0cm³ have low risk of LR after SRS and may be low-yield for predictive biomarker studies. Information regarding sample size and potential challenges for future imaging biomarker studies may be gleaned from this pilot study.

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.

Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine.

Pattern recognition analysis of dynamic susceptibility contrast (DSC)‐MRI curves automatically segments tissue areas with intact blood–brain barrier in a rat stroke model: A feasibility and comparison study

Background

The manual segmentation of intact blood–brain barrier (BBB) regions in the stroke brain is cumbersome, due to the coexistence of infarction, large blood vessels, ventricles, and intact BBB regions, specifically in areas with weak signal enhancement following contrast agent injection.

Hypothesis

That from dynamic susceptibility contrast (DSC)‐MRI alone, without user intervention, regions of weak BBB damage can be segmented based on the leakage‐related parameter K 2 and the extent of intact BBB regions, needed to estimate K 2 values, determined.

Study Type

Feasibility.

Animal Model

Ten female Sprague–Dawley rats (SD, 200–250g) underwent 1‐hour middle carotid artery occlusion (MCAO) and 1‐day reperfusion. Two SD rats underwent 1‐hour MCAO with 3‐day and 5‐day reperfusion.

Field Strength/Sequence

7T; ADC and T1 maps using diffusion‐weighted echo planar imaging (EPI) and relaxation enhancement (RARE) with variable repetition time (TR), respectively. dynamic contrast‐enhanced (DCE)‐MRI using FLASH. DSC‐MRI using gradient‐echo EPI.

Assessment

Constrained nonnegative matrix factorization (cNMF) was applied to the dynamic ‐curves of DSC‐MRI (<4 min) in a BBB‐disrupted rat model. Areas of voxels with intact BBB, classified by automated cNMF analyses, were then used in estimating K 1 and K 2 values, and compared with corresponding values from manually‐derived areas.

Statistical Tests

Mean ± standard deviation of ΔT1‐differences between ischemic and healthy areas were displayed with unpaired Student's t‐tests. Scatterplots were displayed with slopes and intercepts and Pearson's r values were evaluated between K 2 maps obtained with automatic (cNMF)‐ and manually‐derived regions of interest (ROIs) of the intact BBB region.

Results

Mildly BBB‐damaged areas (indistinguishable from DCE‐MRI (10 min) parameters) were automatically segmented. Areas of voxels with intact BBB, classified by automated cNMF, matched closely the corresponding, manually‐derived areas when respective areas were used in estimating K 2 maps (Pearson's r = 0.97, 12 slices).

Data Conclusion

Automatic segmentation of short DSC‐MRI data alone successfully identified areas with intact and compromised BBB in the stroke brain and compared favorably with manual segmentation.

Level of Evidence: 3

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2020;51:1369–1381.

[Current Applications and Future Perspectives of Brain Tumor Imaging]

뇌종양의 진단 및 치료 반응 평가의 기본이 되는 영상기법은 해부학적 영상이다. 현재 임상에서 사용 가능한 영상기법들 중 확산 강조 영상 및 관류 영상이 추가적인 정보를 제공하고 있다. 최근에는 종양의 유전체 변이와 이질성 평가가 중요해지면서 라디오믹스와 딥러닝을 이용한 영상분석기법의 임상 응용이 기대되고 있다. 본 종설에서는 뇌종양 영상 임상 적용에서 여전히 중요한 해부학적 영상을 중심으로 한 자기공명영상 촬영 권고안, 최신 영상기법 중 확산 강조 영상 및 관류 영상의 기본 원리, 병태생리학적 배경 및 임상응용, 마지막으로 최근 컴퓨터 기술의 발전으로 많이 연구되고 있는 라디오믹스와 딥러닝의 뇌종양에서의 향후 활용가치에 대해 기술하고자 한다.

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.

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.

The combined role of MR spectroscopy and perfusion imaging in preoperative differentiation between high- and low-grade gliomas

Background

Brain tumors are an important health problem. The preoperative classification of gliomas by non-invasive techniques is a significant problem. Relative cerebral blood volume and spectroscopy have the ability to sample the entire lesion non-invasively. The present study aims to evaluate the combined role of dynamic susceptibility perfusion and spectroscopy in the classification of primary brain tumors. The combination of both provides overall diagnostic accuracy (100%). Relative cerebral blood volume in peritumoral region plays an important additional role in this regard.

Results

On the basis of histopathology, among 50 patients with brain tumors, high-grade gliomas accounted for 58%, while low-grade gliomas accounted for 42%. The relative cerebral blood volume in the tumor had the best sensitivity, specificity, and accuracy of 96.8%, 95.3%, and 96, respectively. The use of relative cerebral blood volume and choline/N-acetyl Aspartate increased diagnostic accuracy by 100%.

Conclusion

The combination of magnetic resonance spectroscopy and perfusion can increase sensitivity and positive predictive value to define the degree of glioma.

Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis

Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 to June 2018, 24 patients with GBM and 18 patients with sBM were included in this retrospective study. The enhancing area was first segmented on the post-contrast T1WI, then the segmentation was copied to the absolute CBV map and histogram analysis was finally performed. Unpaired t test was used to select the features that could separate two entities and receiving operating curve was used to test the diagnostic performance. Finally, a machine learning method was used to test the diagnostic performance combing all the selected features.Six of 19 features were feasible to distinguish GBM from sBM (all P < .001), among which energy had the highest diagnostic performance (area under curve, 0.84; accuracy, 88%), while a machine learning method could improve the diagnostic performance (area under curve, 0.94; accuracy, 95%).Histogram analysis of the absolute CBV in the enhancing area could help us distinguish GBM from sBM, in addition, a machine learning method with combined features is preferable. It is quite helpful in the condition that the biological nature of peritumoral edema could not separate these two entities.

Biopsy Confirmed Glioma Recurrence Predicted by Multi-Modal Neuroimaging Metrics

Histopathological verification is currently required to differentiate tumor recurrence from treatment effects related to adjuvant therapy in patients with glioma. To bypass the complications associated with collecting neural tissue samples, non-invasive classification methods are needed to alleviate the burden on patients while providing vital information to clinicians. However, uncertainty remains as to which tissue features on magnetic resonance imaging (MRI) are useful. The primary objective of this study was to quantitatively assess the reliability of combining MRI and diffusion tensor imaging metrics to discriminate between tumor recurrence and treatment effects in histopathologically identified biopsy samples. Additionally, this study investigates the noise adjuvant radiation therapy introduces when discriminating between tissue types. In a sample of 41 biopsy specimens, from a total of 10 patients, we derived region-of-interest samples from MRI data in the ipsilateral hemisphere that encompassed biopsies obtained during resective surgery. This study compares normalized intensity values across histopathology classifications and contralesional volumes reflected across the midline. Radiation makes noninvasive differentiation of abnormal-nontumor tissue to tumor recurrence much more difficult. This is because radiation exhibits opposing behavior on key MRI modalities: specifically, on post-contrast T1, FLAIR, and GFA. While radiation makes noninvasive differentiation of tumor recurrence more difficult, using a novel analysis of combined MRI metrics combined with clinical annotation and histopathological correlation, we observed that it is possible to successfully differentiate tumor tissue from other tissue types. Additional work will be required to expand upon these findings.

Association of Glioma Grading With Inflow-Based Vascular-Space-Occupancy MRI: A Preliminary Study at 3T

BACKGROUND

Inflow-based vascular-space-occupancy (iVASO) MRI is a noninvasive perfusion technique that does not require administration of exogenous contrast agents. Arteriolar cerebral blood volume (CBVa) obtained from iVASO MRI is hypothesized to be an indicator of tumor microvasculature.

PURPOSE

To assess the diagnostic performance of iVASO MRI implemented at 3T in predicting histologic grades of cerebral gliomas.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-five patients (31 males) consisting of 14 WHO grade IV glioblastoma multiformes, 14 grade III, and 17 grade II gliomas.

FIELD STRENGTH/SEQUENCE

At 3T we acquired CBVa data using an iVASO sequence.

ASSESSMENT

The maximum and mean CBVa (CBVa_max and CBVa_mean) values were calculated in the tumor and normalized to the contralateral thalamus (nCBVa_max and nCBVa_mean).

STATISTICAL TESTS

Kruskal-Wallis test, Mann-Whitney test, and receiver operating characteristics (ROC) curve were used for statistical analysis.

RESULTS

Both CBVa_max and nCBVa_max increased with tumor grade (P < 0.001). Grade II gliomas showed CBVa_max <0.78 ml / 100 ml in 10/17 cases and nCBVa_max <1.20 in 11/17 cases. Grade III gliomas showed both CBVa_max >0.78 ml / 100 ml and nCBVa_max >1.20 in 13/14 cases, and CBVa_max <2.06 ml / 100 ml in 13/14 cases and nCBVa_max <2.33 in 11/14 cases. Grade IV gliomas showed CBVa_max >2.06 ml / 100 ml in 9/14 cases and nCBVa_max >2.33 in 13/14 cases. The areas under the ROC curve, sensitivity, and specificity were 0.839 (P < 0.001), 92.9% (26/28), and 64.7% (11/17) for CBVa_max, and 0.883 (P < 0.001), 92.9% (26/28), and 70.6% (12/17) for nCBVa_max in the discrimination between grade II and high-grade (grade III and grade IV) tumors, respectively. DATA CONCLUSION: iVASO MRI might be used to help determine and predict glioma grade.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1817-1823.

MRI techniques to measure arterial and venous cerebral blood volume

The measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains.

Dural masses: meningiomas and their mimics

Meningiomas are the most common dural tumour. They are regularly being seen as an incidental finding on brain imaging and treated conservatively. However, there are many other dural masses which mimic their appearances, including primary neoplastic processes, metastases, granulomatous diseases and infection. While some of these are rare, others such as metastases and tuberculosis arise relatively frequently in practice. Although not pathognomonic, key features which increase the probability of a lesion being a meningioma include intralesional calcifications, skull hyperostosis, local dural enhancement and increased perfusion. It is important to have an awareness of these entities as well as their main imaging findings, as they have a wide range of prognoses and differing management strategies. This review outlines several of the most important mimics along with their imaging findings on both standard and advanced techniques with key features which may be used to help differentiate them from meningiomas.

Differentiation of Glioblastoma and Solitary Brain Metastasis by Gradient of Relative Cerebral Blood Volume in the Peritumoral Brain Zone Derived from Dynamic Susceptibility Contrast Perfusion Magnetic Resonance Imaging

OBJECTIVE

The purpose of our study was to evaluate the efficacy of the relative cerebral blood volume (rCBV) gradient in the peritumoral brain zone (PBZ)-the difference in the rCBV values from the area closest to the enhancing lesion to the area closest to the healthy white matter-in differentiating glioblastoma (GB) from solitary brain metastasis (MET).

METHODS

A 3.0-T magnetic resonance imaging (MRI) machine was used to perform dynamic susceptibility contrast perfusion MRI (DSC-MRI) on 43 patients with a solitary brain tumor (24 GB, 19 MET). The rCBV ratios were acquired by DSC-MRI data in 3 regions of the PBZ (near the enhancing tumor, G1; intermediate distance from the enhancing tumor, G2; far from the enhancing tumor, G3). The maximum rCBV ratios in the PBZ (rCBVp) and the enhancing tumor were also calculated, respectively. The perfusion parameters were evaluated using the nonparametric Mann-Whitney test. The sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve were identified.

RESULTS

The rCBVp ratios and rCBV gradient in the PBZ were significantly higher in GB compared with MET (P < 0.05 for both rCBVp ratios and rCBV gradient). The threshold values of 0.50 or greater for rCBVp ratios provide sensitivity and specificity of 57.69% and 79.17%, respectively, for differentiation of GB from MET. Compared with rCBVp ratios, rCBV gradient had higher sensitivity (94.44%) and specificity (91.67%) using the threshold value of greater than 0.06.

CONCLUSIONS

The parameter of rCBV gradient derived from DSC-MRI in the PBZ seems to be the most efficient parameter to differentiate GB from METs.

Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization

PURPOSE

To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV).

METHODS

We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs).

RESULTS

GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant.

CONCLUSION

GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.