Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors

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

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

Differentiation Between High-Grade Glioma and Brain Metastasis Using Cerebral Perfusion-Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta-Analysis of Perfusion-weighted MRI Techniques

BACKGROUND

Distinguishing high-grade gliomas (HGGs) from brain metastases (BMs) using perfusion-weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.

PURPOSE

To compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.

STUDY TYPE

Systematic review and meta-analysis.

POPULATION

Twenty-four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).

FIELD STRENGTH/SEQUENCE

Arterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast-enhanced (DSCE) sequences at 1.5 T and 3.0 T.

ASSESSMENT

Following the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.

STATISTICAL TESTS

Standardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P-value<0.05 was considered significant.

RESULTS

HGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05-0.69) and CBV (SMD = 0.26, 95% CI: 0.01-0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).

DATA CONCLUSION

ASL-derived CBF may be more useful than DSC-derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Growth differentiation factor-15 overexpression promotes cell proliferation and predicts poor prognosis in cerebral lower-grade gliomas correlated with hypoxia and glycolysis signature

AIMS

Growth differentiation factor-15 (GDF15) plays complex and controversial roles in cancer. In this study, the prognostic value and the exact biological function of GDF15 in cerebral lower-grade gliomas (LGGs) and its potential molecular targets were examined.

MAIN METHODS

Wilcoxon signed-rank test and logistic regression were applied to analyze associations between GDF15 expression and clinical characteristics using the Cancer Genome Atlas (TCGA) database. Overall survival was analyzed using Kaplan-Meier and Cox analyses. Gene set enrichment analysis (GSEA) and the hypoxia risk model was conducted to identify the potential molecular mechanisms underlying the effects of GDF15 on LGGs tumorigenesis. The biological function of GDF15 was examined using gain- and loss-of-function experiments, and a recombinant hGDF15 protein in LGG SW1783 cells in vitro.

KEY FINDINGS

We found that higher GDF15 expression is associated with poor clinical features in LGG patients, and an independent risk factor for overall survival among LGG patients. GSEA results showed that the poor prognostic role of GDF15 in LGGs is related to hypoxia and glycolysis signatures, which was further validated using the hypoxia risk model. Furthermore, GDF15 overexpression facilitated cell proliferation, while GDF15 siRNA inhibits cell proliferation in LGG SW1783 cells. In addition, GDF15 was upregulated upon CoCl2 treatment which induces hypoxia, correlating with the upregulation of the expressions of HIF-1α and glycolysis-related key genes in SW1783 cells.

SIGNIFICANCE

GDF15 may promote LGG tumorigenesis that is associated with the hypoxia and glycolysis pathways, and thus could serve as a promising molecular target for LGG prevention and therapy.

Multiparametric MR Imaging Features of Primary CNS Lymphomas

Objective

Primary central nervous system lymphomas (PCNS) are relatively rare tumors, accounting for about 4% of all brain tumors. On neuroimaging, they are characterized by a low MR signal in T1, isointense in T2, bright uniform contrast enhancement, and diffusion restriction. The aim of this study is to note the lack of effectiveness of the MR/CT perfusion technique in complex multiparametric imaging in the differential diagnosis of primary lymphomas of the central nervous system in comparison with highly malignant gliomas and brain metastases.

Materials and Methods

This prospective study included 80 patients with CNS tumors examined/operated at the Federal Center for Neurosurgery (Tyumen, Russia) from 2018 to 2021. The patients were divided into 4 groups: group 1 consisted of 33 cases with primary CNS lymphomas (10 cases with atypical manifestations according to perfusion parameters and 23 cases of classic CNS lymphomas), group 2 with anaplastic astrocytomas—14 cases, group 3—23 cases with glioblastomas and group 4—10 cases with solitary metastatic lesions. The study was carried out on a General Electric Discovery W750 3T magnetic resonance tomograph, a Canon Aquilion One multispiral X-ray computed tomograph (Gadovist 7.5 ml, Yomeron 400 mg−50 ml). Additionally, immunohistochemical analysis was carried out with the following markers: CD3, CD20, CD34, Ki-67, VEGF.

Results

It has been established that MR/CT perfusion is not a highly sensitive method for visualizing primary CNS lymphomas, as previously thought, but at the same time, the method has a number of undeniable advantages that make it indispensable in the algorithm of a complex multiparametric diagnostic approach for this type of tumor. Nevertheless, PLCNS is characterized by an atypical manifestation, which is an exception to the rule.

Conclusions

The possibilities of neuroimaging of primary lymphomas, even with the use of improved techniques for collecting MR/CT data, are limited and do not always allow reliable differentiation from other neoplasms.

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.

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.

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

•

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

•

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

•

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.

Advancements in Positron Emission Tomography/Magnetic Resonance Imaging and Applications to Diagnostic Challenges in Neuroradiology

Since the clinical adoption of magnetic resonance (MR) in medical imaging, MR has proven to be a workhorse in diagnostic neuroradiology, with the ability to provide superb anatomic detail as well as additional functional and physiologic data, depending on the techniques utilized. Positron emission tomography/computed tomography has also shown irreplaceable diagnostic value in certain disease processes of the central nervous system by providing molecular and metabolic information through the development of numerous disease-specific PET tracers, many of which can be utilized as a diagnostic technique in and of themselves or can provide a valuable adjunct to information derived from MR. Despite these advances, many challenges still remain in neuroradiology, particularly in malignancy, neurodegenerative disease, epilepsy, and cerebrovascular disease. Through improvements in attenuation correction, motion correction, and PET detectors, combining the 2 modalities of PET and MR through simultaneous imaging has proven feasible and allows for improved spatial and temporal resolution without compromising either of the 2 individual modalities. The complementary information offered by both technologies has provided increased diagnostic accuracy in both research and many clinical applications in neuroradiology.

Multiparametric magnetic resonance imaging features of a canine glioblastoma model

PURPOSE

To assess glioblastoma multiforme (GBM) formation with similar imaging characteristics to human GBM using multiparametric magnetic resonance imaging (MRI) in an orthotopic xenograft canine GBM model.

MATERIALS AND METHODS

The canine GBM cell line J3T1 was subcutaneously injected into 6-week-old female BALB/c nude mice to obtain tumour fragments. Tumour fragments were implanted into adult male mongrel dog brains through surgery. Multiparametric MRI was performed with conventional MRI, diffusion-weighted imaging, and dynamic susceptibility contrast-enhanced perfusion-weighted imaging at one week and two weeks after surgery in a total of 15 surgical success cases. The presence of tumour cells, the necrotic area fraction, and the microvessel density (MVD) of the tumour on the histologic specimen were assessed. Tumour volume, diffusion, and perfusion parameters were compared at each time point using Wilcoxon signed-rank tests, and the differences between tumour and normal parenchyma were compared using unpaired t-tests. Spearman correlation analysis was performed between the imaging and histologic parameters.

RESULTS

All animals showed a peripheral enhancing lesion on MRI and confirmed the presence of a tumour through histologic analysis (92.3%). The normalized perfusion values did not show significant decreases through at least 2 weeks after the surgery (P > 0.05). There was greater cerebral blood volume and flow in the GBM than in the normal-appearing white matter (1.46 ± 0.25 vs. 1.13 ± 0.16 and 1.30 ± 0.22 vs. 1.02 ± 0.14; P < 0.001 and P < 0.001, respectively). The MVD in the histologic specimens was correlated with the cerebral blood volume in the GBM tissue (r = 0.850, P = 0.004).

CONCLUSION

Our results suggest that the canine GBM model showed perfusion imaging characteristics similar to those of humans, and it might have potential as a model to assess novel technical developments for GBM treatment.

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.

Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain

Objectives

Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors.

Methods

We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve.

Results

We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10^-3 mm²/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10^-3 mm²/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors.

Conclusions

The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

OBJECTIVES

The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas.

METHODS

Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters.

RESULTS

Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10-3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92.

CONCLUSION

The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

Diagnostic role of apparent diffusion coefficient combined with intratumoral susceptibility signals in differentiating high-grade gliomas from brain metastases

OBJECTIVE

The aim of this study was to assess whether tumoral and peritumoral apparent diffusion coefficient values and intratumoral susceptibility signals on susceptibility-weighted imaging could distinguish between high-grade gliomas and brain metastases, and to investigate their associations with the Ki-67 proliferation index.

MATERIALS AND METHODS

Fifty-seven patients with pathologically confirmed diagnoses of either high-grade glioma or brain metastasis were enrolled in this study (23 with high-grade gliomas and 34 with brain metastases). The minimum and mean apparent diffusion coefficients in the enhancing tumoral region (ADC_min and ADC_mean) and the minimum apparent diffusion coefficient in the peritumoral region (ADC_edema) were measured from apparent diffusion coefficient maps, and intratumoral susceptibility signal grades acquired by susceptibility-weighted imaging were calculated. Ki-67 proliferation index values were obtained from the hospital database. These parameters were evaluated using the Mann-Whitney U test, independent-sample t-test, Spearman correlation analysis, receiver operating characteristic curve, and logistic regression analyses.

RESULTS

ADC_mean, ADC_min values, and intratumoral susceptibility signal grades in brain metastases were significantly lower than those in high-grade gliomas (all p < 0.05). Ki-67 proliferation index values showed significant correlations with ADC_mean, ADC_min, and intratumoral susceptibility signal grade in brain metastases (all p < 0.05), but no correlation was found in high-grade gliomas (all p > 0.05). According to receiver operating characteristic curve analysis, ADC_mean achieved the highest diagnostic performance for discriminating high-grade gliomas from brain metastases. Furthermore, the combination of tumoral apparent diffusion coefficient parameters with intratumoral susceptibility signal grade provided a higher area under the curve than univariate parameters.

CONCLUSION

The combination of tumoral apparent diffusion coefficient with intratumoral susceptibility signal grade can offer better diagnostic performances for differential diagnosis. Apparent diffusion coefficient and intratumoral susceptibility signal may reflect cellular proliferative activity in brain metastases, but not in high-grade gliomas.

Prediction of recurrent glioblastoma after laser interstitial thermal therapy: The role of diffusion imaging

Background

Evaluate the utility of diffusion-weighted imaging (DWI) for the assessment of local recurrence of glioblastoma (GBM) on imaging performed 24 h following MRI-guided laser interstitial thermal therapy (LITT). We hypothesize that microscopic peritumoral infiltration correlates with early subtle variations on DWI images and apparent diffusion coefficient (ADC) maps.

Methods

Of 64 patients with GBM treated with LITT, 39 had MRI scans within 24 h after undergoing LITT. Patterns on DWI images and ADC maps 24 h following LITT were correlated with areas of future GBM recurrence identified through coregistration of subsequent MRI examinations. In the areas of suspected recurrence within the periphery of post-LITT lesions, signal intensity values on ADC maps were recorded and compared with the remaining peritumoral ring.

Results

Thirty-nine patients with GBM met the inclusion criteria. For predicting recurrent GBM, areas of decreased DWI signal and increased signal on ADC maps within the expected peritumoral ring of restricted diffusion identified 24 h following LITT showed 86.1% sensitivity, 75.2% specificity, and high correlation (r = 0.53) with future areas of GBM recurrence (P < .01). Areas of future recurrence demonstrated a 37% increase in the ADC value (P < .001), compared with findings in the surrounding treated peritumoral region. A significantly greater area under the receiver operating characteristics curve was determined for ADC values (P < .01).

Conclusions

DWI obtained 24 h following LITT can help predict the location of GBM recurrence months before the development of abnormal enhancement. This may alter future treatment planning, perhaps suggesting areas that may be targeted for additional therapy.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

Objective

To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup.

Materials and Methods

Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade.

Results

The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68).

Conclusion

Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.

White Matter Integrity in Tanzanian Children With Sickle Cell Anemia: A Diffusion Tensor Imaging Study

Background and Purpose- Widespread reductions in white matter integrity are associated with cognitive dysfunction in sickle cell anemia. Silent cerebral infarction (SCI), vasculopathy (VSC), and low hemoglobin concentration (Hb) are implicated; we aimed to determine independent contributions to microstructural white matter injury and whether white matter integrity differs across arterial territories. Methods- Sixty two children with sickle cell anemia aged 6 to 19 years were prospectively studied at Muhimbili National Hospital, Tanzania. SCI± and VSC± were identified on magnetic resonance imaging (MRI)/magnetic resonance angiography (MRA) scans by 2 neuroradiologists. Tract-based spatial statistics tested for voxel-wise differences in diffusion tensor imaging metrics (ie, fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity) between SCI± and VSC± groups, with correlations between diffusion tensor imaging metrics and Hb. In tract-based spatial statistics analyses, potentially mediating factors (ie, age, sex, as well as Hb, SCI, and/or vasculopathy) were covariates. Differences in mean diffusion tensor imaging metrics across regions of interest in arterial territories were explored. Results- Compared with SCI- patients (n=45), SCI+ patients (n=17) exhibited increased radial diffusivity in multiple regions; negative relationships were observed between mean diffusivity, axial diffusivity, and Hb (P<0.005). Compared with VSC- patients (n=49), mild (n=6) or moderate (n=7) VSC+ patients exhibited reduced fractional anisotropy in widespread regions (P<0.05) including the anterior longitudinal fasciculi, corpus callosum, internal capsule, corona radiata, and corticospinal tracts. Overall, the posterior cerebral arterial territory had higher mean mean diffusivity and mean radial diffusivity than the anterior and middle cerebral arterial territories, although no patient had vasculopathy in this area. There was an interaction between territory and vasculopathy. Conclusions- SCI, vasculopathy, and Hb are independent risk factors, and thus treatment targets, for diffuse white matter injury in patients with sickle cell anemia. Exacerbation of hemodynamic stress may play a role.

Grading Gliomas Capability: Comparison between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion-Weighted Imaging (DWI)

Background:

To compare diagnostic accuracy between DWI visual scale assessment and ADC value measurement of solid portion of the tumor in grading gliomas.

Methods:

This retrospective study included 38 patients who had pathologically proven gliomas between January 2013 and August 2018 with 18 low grade and 20 high grade tumors. All patients underwent MRI and biopsy. Two readers reviewed DWI visual scale independently. Disagreement was resolved by consensus. One reviewer measured ADC value of entire solid part of the tumor in single axial slice with greatest dimension of tumor which was chosen by consensus. Two data sets of visual scale and ADC value were analyzed and comparison of diagnostic accuracy in glioma grading was done by using area under the curve (AUC) of receiver operating characteristic curve (ROC).

Results:

Visual scale and ADC value could be used to distinguish between low and high grade gliomas with a statistically significant difference. (P-value 0.002 and <0.001). Almost all high grade gliomas had visual scale 5. The sensitivity, specificity, PPV NPV and accuracy were 50%, 100%, 100% , 64.3%,73.68% respectively. The cutoff level for the ADC value was determined to be 1119.48 x10^-6 mm²/s in differentiation between low and high grade gliomas with the sensitivity, specificity, PPV, NPV, accuracy of 90%, 88.89% , 90%, 88.9% and 89.47% respectively. There was no statistically significant difference(P-value = 0.163).

Conclusion:

Both Visual scale and ADC value were capable of differentiating between low and high grade gliomas. Although visual scale may not replace ADC measurement, larger scale prospective study is needed for validate this initial result.

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

Background

The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale.

Purpose

To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67.

Material and Methods

A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors.

Results

Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve.

Conclusion

The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.

Pseudo-continuous arterial spin labelling shows high diagnostic performance in the detection of postoperative residual lesion in hyper-vascularised adult brain tumours

OBJECTIVES

Our aim was to evaluate the contribution of pseudo-continuous arterial spin labelling (pCASL) in the detection of a postoperative residual lesion in adult brain tumours.

METHODS

Seventy-five patients were prospectively included. Following the results of preoperative DSC-PWI assessment, intra-axial lesions, including high-grade gliomas (n = 43) and certain metastases (n = 14), were classified as hyper-vascular (HV+ group, n = 57); other lesions, including low-grade gliomas and certain metastases, were classified as non-hyper-vascular (HV- group, n = 18). To confirm the absence/presence of a residual lesion or disease progression, postoperative MRI including pCASL sequence and follow-up-MRI were performed within 72 h and 1-6 months after the resection, respectively. Two raters evaluated the images. Mean and maximal ASL cerebral blood flow (CBF) values were measured in the perioperative region and normalised to the contralateral tissue. The pCASL-CBF maps and post-contrast T1WI were visually assessed for residual lesion. Quantitative data were analysed with unpaired Student t and Mann-Whitney U tests and the visual diagnostic performance with the McNemar test.

RESULTS

In the HV+ group, the mean normalised CBF was 1.97 ± 0.59 and 0.97 ± 0.29 (p < 0.0001, AUC = 0.964, cut-off = 1.27) for patients with or without residual tumours, respectively. The mean normalised CBF was not discriminative for assessing residual tumours in the HV- group (p = 0.454). Visual CBF evaluation allowed 92.98% patients belonging to the HV+ group to be correctly classified (sensitivity 93.02%, specificity 92.86%, p < 0.001). Visual evaluation was correlated with contrast enhancement evaluation and with the mean normalised CBF values (r = 0.505, p < 0.0001 and 0.838, p < 0.0001, respectively).

CONCLUSION

Qualitative and quantitative ASL evaluation shows high diagnostic performance in postoperative assessment of hyper-perfused tumours. In this case, postoperative pCASL may be useful, especially if contrast injection cannot be performed or when contrast enhancement is doubtful.

KEY POINTS

• Evaluation of postoperative residual lesion in the case of brain tumours is an imaging challenge. • This prospective monocentric study showed that increased normalised cerebral blood flow assessed by pseudo-continuous arterial spin labelling (pCASL) correlates well with the presence of a residual tumour in the case of hyper-vascular tumour diagnosed on preoperative MRI. • Qualitative and quantitative pCASL is an informative sequence for hyper-vascular residual tumour, especially if acquired more than 48 h after brain tumour surgery, when contrast enhancement can give ambiguous results due to blood-brain barrier disruption.

Imaging Glioblastoma Posttreatment: Progression, Pseudoprogression, Pseudoresponse, Radiation Necrosis

Radiographic monitoring of posttreatment glioblastoma is important for clinical trials and determining next steps in management. Evaluation for tumor progression is confounded by the presence of treatment-related radiographic changes, making a definitive determination less straight-forward. The purpose of this article was to describe imaging tools available for assessing treatment response in glioblastoma, as well as to highlight the definitions, pathophysiology, and imaging features typical of true progression, pseudoprogression, pseudoresponse, and radiation necrosis.

Increased Antiangiogenic Effect by Blocking CCL2-dependent Macrophages in a Rodent Glioblastoma Model: Correlation Study with Dynamic Susceptibility Contrast Perfusion MRI

When glioblastoma multiforme (GBM) is treated with anti-vascular endothelial growth factor (VEGF) agents, it commonly exhibits tumor progression due to the development of resistance, which results in a dismal survival rate. GBM tumors contain a large number of monocytes/macrophages, which have been shown to be resistant to the effects of bevacizumab. It has been reported that tumor-associated macrophages (TAMs) promote resistance to bevacizumab treatment. Therefore, it is important to target TAMs in the GBM microenvironment. TAMs, which depend on chemokine ligand 2 (CCL2) for differentiation and survival, induce the expression of proangiogenic factors such as VEGF. Dynamic susceptibility contrast (DSC)-MR imaging is an advanced technique that provides information on tumor blood volume and can potentially predict the response to several treatments, including anti-angiogenic agents such as bevacizumab, in human GBM. In this study, we used a CCL2 inhibitor, mNOX-E36, to suppress the recruitment of TAMs in a CCL2-expressing rat GBM model and investigated the effect of combination therapy with bevacizumab using DSC-MR imaging. We demonstrated that the inhibition of CCL2 blocked macrophage recruitment and angiogenesis, which resulted in decreased tumor volume and blood volume in CCL2-expressing GBM in a rat model. Our results provide direct evidence that CCL2 expression can increase the resistance to bevacizumab, which can be assessed noninvasively with the DSC-MR imaging technique. This study shows that the suppression of CCL2 can play an important role in increasing the efficacy of anti-angiogenic treatment in GBM by inhibiting the recruitment of CCL2-dependent macrophages.

Heterogeneity of glioblastoma with gliomatosis cerebri growth pattern on diffusion and perfusion MRI

BACKGROUND AND PURPOSE

Gliomatosis cerebri (GC) is a rare growth pattern of glioblastoma whose diffuse nature is reflected by unspecific, relatively uniform findings on conventional MRI. In the present study we sought to evaluate the additional value of diffusion (DWI) and perfusion weighted (PWI) MRI for a more detailed characterization.

METHODS

We analyzed the MRI findings in patients with histologically proven glioblastoma with GC growth pattern with a specific emphasis on T2 lesion pattern, volume, relative apparent diffusion coefficient (rACD), and relative cerebral blood volume (rCBV) and compared these to age-/gender-matched patients with localized glioblastoma.

RESULTS

Overall, 16 patients (median age 59.5 years, 4 male) were included in the study. Of these, 8 patients had a glioblastoma with GC growth pattern, and 8 a classical localized growth pattern. While the median rADC (1.27 [IQR 1.12-1.41]) within the T2 lesion was significant lower in glioblastoma with GC growth pattern compared to localized glioblastoma (1.74 [IQR 1.45-1.96]; p = 0.003), the median T2 lesion volume and rCBV within the T2 lesion did not differ significantly. Furthermore, six patients with glioblastoma with GC growth pattern showed focal areas with significantly reduced rADC (p = 0.043), and/or increased rCBV (p = 0.028).

CONCLUSIONS

Lower rADC in glioblastoma with GC growth pattern might reflect the diffuse tumor cell infiltration whereas focal areas with decreased rADC and/or increased rCBV probably indicate high tumor cell density and/or abnormal tumor vessels which may be useful for biopsy guidance.

Grading and proliferation assessment of diffuse astrocytic tumors with monoexponential, biexponential, and stretched-exponential diffusion-weighted imaging and diffusion kurtosis imaging

PURPOSE

To compare the main parameters derived from monoexponential, biexponential and stretched-exponential diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) with respect to diagnostic performance for tumor grading and proliferation assessment in diffuse astrocytic tumors (DATs).

MATERIALS AND METHODS

Fifty-eight pathologically confirmed DAT patients who underwent DWI and DKI on a 3-T scanner were prospectively collected and retrospectively reviewed. Measurements including the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D^*), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), fractional anisotropy (FA), and mean kurtosis (MK) were compared between tumor grades (Ⅱ, Ⅲ, and Ⅳ) by using a Jonckheere-Terpstra test. Receiver operating characteristic (ROC) curves were used to assess the diagnostic efficacy of these parameters. Spearman's rho with the Ki-67 labeling index (LI) was calculated for each parameter.

RESULTS

MK values differed significantly between all DAT subtypes and increased with grade. The ADC, D, f, DDC, α and MD values were significantly higher in grade Ⅱ tumors than in grade Ⅲ/Ⅳ tumors. D^* values were significantly lower in grade Ⅱ tumors than in grade Ⅳ tumors (all P < 0.05). In discriminating between grade Ⅱ and Ⅲ tumors, α, MK, MD, D and f had significantly greater area under the ROC curve (AUC) values than D^* and FA (0.927, 0.901, 0.896, 0.895, and 0.889, respectively vs 0.659 and 0.598, respectively, P < 0.05). In discriminating between grade Ⅲ and Ⅳ tumors, only MK demonstrated acceptable discrimination (AUC = 0.711). MK and D showed a strong correlation with the Ki-67 LI (ρ = 0.791 and -0.789, respectively, P < 0.001). D^*, f, MD, ADC, DDC and α showed a moderate correlation (|ρ| ranged from 0.415 to 0.698, P < 0.05).

CONCLUSION

MK and D have considerable potential to predict the degree of proliferation of DATs. MK could effectively characterize microstructural changes throughout the malignant transformation of DATs and provided useful complementary information for grading.

The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies

OPINION STATEMENT

T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.

Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient

Purpose:

This study aimed to determine whether whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) and apparent diffusion coefficient (ADC) for contrast-enhancing lesions can be used to differentiate between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL).

Methods:

From 20 patients, 9 with PCNSL and 11 with GBM without any hemorrhagic lesions, underwent MRI, including diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging before surgery. Histogram analysis of nCBV and ADC from whole-tumor voxels in contrast-enhancing lesions was performed. An unpaired t-test was used to compare the mean values for each type of tumor. A multivariate logistic regression model (LRM) was performed to classify GBM and PCNSL using the best parameters of ADC and nCBV.

Results:

All nCBV histogram parameters of GBMs were larger than those of PCNSLs, but only average nCBV was statistically significant after Bonferroni correction. Meanwhile, ADC histogram parameters were also larger in GBM compared to those in PCNSL, but these differences were not statistically significant. According to receiver operating characteristic curve analysis, the nCBV average and ADC 25th percentile demonstrated the largest area under the curve with values of 0.869 and 0.838, respectively. The LRM combining these two parameters differentiated between GBM and PCNSL with a higher area under the curve value (Logit (P) = −21.12 + 10.00 × ADC 25th percentile (10⁻³ mm²/s) + 5.420 × nCBV mean, P < 0.001).

Conclusion:

Our results suggest that whole-tumor histogram analysis of nCBV and ADC combined can be a valuable objective diagnostic method for differentiating between GBM and PCNSL.

Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis

BACKGROUND

Accurate diagnosis of high-grade glioma and solitary brain metastasis is clinically important because it affects the patient's outcome and alters patient management.

PURPOSE

To evaluate the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis.

DATA SOURCES

A literature search of Ovid MEDLINE and EMBASE was conducted up to November 10, 2017.

STUDY SELECTION

Studies evaluating the diagnostic performance of DWI and DTI for differentiating high-grade glioma from solitary brain metastasis were selected.

DATA ANALYSIS

Summary sensitivity and specificity were established by hierarchic logistic regression modeling. Multiple subgroup analyses were also performed.

DATA SYNTHESIS

Fourteen studies with 1143 patients were included. The individual sensitivities and specificities of the 14 included studies showed a wide variation, ranging from 46.2% to 96.0% for sensitivity and 40.0% to 100.0% for specificity. The pooled sensitivity of both DWI and DTI was 79.8% (95% CI, 70.9%-86.4%), and the pooled specificity was 80.9% (95% CI, 75.1%-85.5%). The area under the hierarchical summary receiver operating characteristic curve was 0.87 (95% CI, 0.84-0.89). The multiple subgroup analyses also demonstrated similar diagnostic performances (sensitivities of 76.8%-84.7% and specificities of 79.7%-84.0%). There was some level of heterogeneity across the included studies (I² = 36%); however, it did not reach a level of concern.

LIMITATIONS

The included studies used various DWI and DTI parameters.

CONCLUSIONS

DWI and DTI demonstrated a moderate diagnostic performance for differentiation of high-grade glioma from solitary brain metastasis.

Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADC_min, ADC_mean) and the minimum ADC in the peritumoral region (ADC_edema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADC_min values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADC_mean values. The mean ADC_min values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10^-3mm²/s for the ADC_edema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis

OBJECTIVES

Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.

METHODS

A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.

RESULTS

Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).

CONCLUSIONS

Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.

KEY POINTS

• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

Diagnostic performance of apparent diffusion coefficient parameters for glioma grading

This study was to evaluate the diagnostic performance of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) parameters derived from diffusion tensor imaging in the differentiation between grade II and III gliomas. The records of 60 patients (30 women, 30 men; mean age, 45.4 years) suspected of having gliomas who underwent an ADC image-guided stereotactic biopsy were retrospectively reviewed. The values of FA and ADC were measured, and the sensitivity, specificity, accuracy and area under the curve (AUC) of those parameters were calculated based on the receiver operating characteristic curve analysis. A predictive diagnostic equation was also constructed and evaluated. Significant differences in minimum ADC values were found in the quantitative analysis between the grade III and II glioma groups. The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), accuracy and AUC for identifying grade III and II gliomas at the optimum cut-off value of 0.895 × 10^-3 mm²/s of minimum ADC were 81.0, 89.1, 77.3, 91.1, 86.6 and 0.87, respectively. The predictive diagnostic equation was superior to the single minimum ADC indicator with a sensitivity of 90.5%, a specificity of 84.8%, a PPV of 73.1%, an NPV of 95.1%, and an accuracy of 86.6%, respectively. The study provides evidence that minimum ADC values have a superior diagnostic performance in differentiating grade III and II gliomas, and the predictive diagnostic equation may be helpful in the differentiation.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice’s similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Assessment of bevacizumab resistance increased by expression of BCAT1 in IDH1 wild-type glioblastoma: application of DSC perfusion MR imaging

BCAT1 (branched-chain amino acid trasaminase1) expression is necessary for the progression of IDH1 wild-type (WT) glioblastoma multiforme (GBM), which is known to be associated with aggressive tumors. The purpose of our study is to investigate the bevacizumab resistance increased by the expression of BCAT1 in IDH1 WT GBM in a rat model, which was evaluated using DSC perfusion MRI. BCAT1 sh#1 inhibits cell proliferation and limits cell migration potential in vitro. In vivo MRI showed that the increase in both tumor volume and nCBV after bevacizumab treatment in IDH1 WT tumors was significantly higher compared with BCAT1 sh#1tumors. In a histological analysis, more micro-vessel reformation by bevacizumab resistance was observed in IDH1 WT tumors than BCAT1 sh#1 tumors. These findings indicate that BCAT1 expression in IDH1 WT GBM increases resistance to bevacizumab treatment, which could be assessed by DSC perfusion MRI, and that nCBV can be a surrogate imaging biomarker for the prediction of antiangiogenic treatment in GBM.

Magnetic resonance perfusion for differentiating low-grade from high-grade gliomas at first presentation

BACKGROUND

Gliomas are the most common primary brain tumour. They are graded using the WHO classification system, with Grade II-IV astrocytomas, oligodendrogliomas and oligoastrocytomas. Low-grade gliomas (LGGs) are WHO Grade II infiltrative brain tumours that typically appear solid and non-enhancing on magnetic resonance imaging (MRI) scans. People with LGG often have little or no neurologic deficit, so may opt for a watch-and-wait-approach over surgical resection, radiotherapy or both, as surgery can result in early neurologic disability. Occasionally, high-grade gliomas (HGGs, WHO Grade III and IV) may have the same MRI appearance as LGGs. Taking a watch-and-wait approach could be detrimental for the patient if the tumour progresses quickly. Advanced imaging techniques are increasingly used in clinical practice to predict the grade of the tumour and to aid clinical decision of when to intervene surgically. One such advanced imaging technique is magnetic resonance (MR) perfusion, which detects abnormal haemodynamic changes related to increased angiogenesis and vascular permeability, or "leakiness" that occur with aggressive tumour histology. These are reflected by changes in cerebral blood volume (CBV) expressed as rCBV (ratio of tumoural CBV to normal appearing white matter CBV) and permeability, measured by K^trans.

OBJECTIVES

To determine the diagnostic test accuracy of MR perfusion for identifying patients with primary solid and non-enhancing LGGs (WHO Grade II) at first presentation in children and adults. In performing the quantitative analysis for this review, patients with LGGs were considered disease positive while patients with HGGs were considered disease negative.To determine what clinical features and methodological features affect the accuracy of MR perfusion.

SEARCH METHODS

Our search strategy used two concepts: (1) glioma and the various histologies of interest, and (2) MR perfusion. We used structured search strategies appropriate for each database searched, which included: MEDLINE (Ovid SP), Embase (Ovid SP), and Web of Science Core Collection (Science Citation Index Expanded and Conference Proceedings Citation Index). The most recent search for this review was run on 9 November 2016.We also identified 'grey literature' from online records of conference proceedings from the American College of Radiology, European Society of Radiology, American Society of Neuroradiology and European Society of Neuroradiology in the last 20 years.

SELECTION CRITERIA

The titles and abstracts from the search results were screened to obtain full-text articles for inclusion or exclusion. We contacted authors to clarify or obtain missing/unpublished data.We included cross-sectional studies that performed dynamic susceptibility (DSC) or dynamic contrast-enhanced (DCE) MR perfusion or both of untreated LGGs and HGGs, and where rCBV and/or K^trans values were reported. We selected participants with solid and non-enhancing gliomas who underwent MR perfusion within two months prior to histological confirmation. We excluded studies on participants who received radiation or chemotherapy before MR perfusion, or those without histologic confirmation.

DATA COLLECTION AND ANALYSIS

Two review authors extracted information on study characteristics and data, and assessed the methodological quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We present a summary of the study characteristics and QUADAS-2 results, and rate studies as good quality when they have low risk of bias in the domains of reference standard of tissue diagnosis and flow and timing between MR perfusion and tissue diagnosis.In the quantitative analysis, LGGs were considered disease positive, while HGGs were disease negative. The sensitivity refers to the proportion of LGGs detected by MR perfusion, and specificity as the proportion of detected HGGs. We constructed two-by-two tables with true positives and false negatives as the number of correctly and incorrectly diagnosed LGG, respectively, while true negatives and false positives are the number of correctly and incorrectly diagnosed HGG, respectively.Meta-analysis was performed on studies with two-by-two tables, with further sensitivity analysis using good quality studies. Limited data precluded regression analysis to explore heterogeneity but subgroup analysis was performed on tumour histology groups.

MAIN RESULTS

Seven studies with small sample sizes (4 to 48) met our inclusion criteria. These were mostly conducted in university hospitals and mostly recruited adult patients. All studies performed DSC MR perfusion and described heterogeneous acquisition and post-processing methods. Only one study performed DCE MR perfusion, precluding quantitative analysis.Using patient-level data allowed selection of individual participants relevant to the review, with generally low risks of bias for the participant selection, reference standard and flow and timing domains. Most studies did not use a pre-specified threshold, which was considered a significant source of bias, however this did not affect quantitative analysis as we adopted a common rCBV threshold of 1.75 for the review. Concerns regarding applicability were low.From published and unpublished data, 115 participants were selected and included in the meta-analysis. Average rCBV (range) of 83 LGGs and 32 HGGs were 1.29 (0.01 to 5.10) and 1.89 (0.30 to 6.51), respectively. Using the widely accepted rCBV threshold of <1.75 to differentiate LGG from HGG, the summary sensitivity/specificity estimates were 0.83 (95% CI 0.66 to 0.93)/0.48 (95% CI 0.09 to 0.90). Sensitivity analysis using five good quality studies yielded sensitivity/specificity of 0.80 (95% CI 0.61 to 0.91)/0.67 (95% CI 0.07 to 0.98). Subgroup analysis for tumour histology showed sensitivity/specificity of 0.92 (95% CI 0.55 to 0.99)/0.42 (95% CI 0.02 to 0.95) in astrocytomas (6 studies, 55 participants) and 0.77 (95% CI 0.46 to 0.93)/0.53 (95% CI 0.14 to 0.88) in oligodendrogliomas+oligoastrocytomas (6 studies, 56 participants). Data were too sparse to investigate any differences across subgroups.

AUTHORS' CONCLUSIONS

The limited available evidence precludes reliable estimation of the performance of DSC MR perfusion-derived rCBV for the identification of grade in untreated solid and non-enhancing LGG from that of HGG. Pooled data yielded a wide range of estimates for both sensitivity (range 66% to 93% for detection of LGGs) and specificity (range 9% to 90% for detection of HGGs). Other clinical and methodological features affecting accuracy of the technique could not be determined from the limited data. A larger sample size of both LGG and HGG, preferably using a standardised scanning approach and with an updated reference standard incorporating molecular profiles, is required for a definite conclusion.

Slice-accelerated gradient-echo echo planar imaging dynamic susceptibility contrast-enhanced MRI with blipped CAIPI: effect of increasing temporal resolution

PURPOSE

To assess the influence of high temporal resolution on the perfusion measurements and image quality of perfusion maps, by applying simultaneous-multi-slice acquisition (SMS) dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

DSC-MRI data using SMS gradient-echo echo planar imaging sequences in 10 subjects with no intracranial abnormalities were retrospectively analyzed. Three additional data sets with temporal resolution of 1.0, 1.5, and 2.0 s were created from the raw data sets of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (T _max) measurements were performed, as was visual perfusion map analysis. The perfusion parameter for temporal resolution of 0.5 s (reference) was compared with each synthesized perfusion parameter.

RESULTS

CBF, MTT, and TTP values at temporal resolutions of 1.5 and 2.0 s differed significantly from the reference. The image quality of MTT, TTP, and T _max maps deteriorated with decreasing temporal resolution.

CONCLUSION

The temporal resolution of DSC-MRI influences perfusion parameters and SMS DSC-MRI provides better image quality for MTT, TTP, and T _max maps.

Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges

Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.

Preoperative grading of supratentorial gliomas using high or standard b-value diffusion-weighted MR imaging at 3T

PURPOSE

The goal of this study was to compare diffusion-weighted magnetic resonance imaging (DW-MRI) using high b-value (b=3000s/mm²) to DW-MRI using standard b-value (b=1000s/mm²) in the preoperative grading of supratentorial gliomas.

MATERIALS AND METHODS

Fifty-three patients with glioma had brain DW-MRI at 3T using two different b-values (b=1000s/mm² and b=3000s/mm²). There were 35 men and 18 women with a mean age of 40.5±17.1 years (range: 18-79 years). Mean, minimum, maximum, and range of apparent diffusion coefficient (ADC) values for solid tumor ROIs (ADC_mean, ADC_min, ADC_max, and ADC_diff), and the normalized ADC (ADC_ratio) were calculated. A Kruskal-Wallis statistic with Bonferroni correction for multiple comparisons was applied to detect significant ADC parameter differences between tumor grades by including or excluding 19 patients with an oligodendroglioma. Receiver operating characteristic curve analysis was conducted to define appropriate cutoff values for grading gliomas.

RESULTS

No differences in ADC derived parameters were found between grade II and grade III gliomas. Mean ADC values using standard b-value were 1.17±0.27×10^-3mm²/s [range: 0.63-1.61], 1.05±0.22×10^-3mm²/s [range: 0.73-1.33], and 0.86±0.23×10^-3mm²/s [range: 0.52-1.46] for grades II, III and IV gliomas, respectively. Using high b-value, mean ADC values were 0.89±0.24×10^-3mm²/s [range: 0.42-1.25], 0.82±0.20×10^-3mm²/s [range: 0.56-1.10], and 0.59±0.17×10^-3mm²/s [range: 0.40-1.01] for grades II, III and IV gliomas, respectively. ADC_mean, ADC_ratio, ADC_max, and ADC_min were different between grade II and grade IV gliomas at both standard and high b-values. Differences in ADC_mean, ADC_max, and ADC_diff were found between grade III and grade IV only using high b-value.

CONCLUSION

ADC parameters derived from DW-MRI using a high b-value allows a better differential diagnosis of gliomas, especially for differentiating grades III and IV, than those derived from DW-MRI using a standard b-value.

Predicting Glioblastoma Recurrence by Early Changes in the Apparent Diffusion Coefficient Value and Signal Intensity on FLAIR Images

OBJECTIVE

Recurrence of glioblastoma multiforme (GBM) arises from areas of microscopic tumor infiltration that have yet to disrupt the blood-brain barrier. We hypothesize that these microscopic foci of invasion cause subtle variations in the apparent diffusion coefficient (ADC) and FLAIR signal detectable with the use of computational big-data modeling.

MATERIALS AND METHODS

Twenty-six patients with native GBM were studied immediately after undergoing gross total tumor resection. Within the peritumoral region, areas of future GBM recurrence were identified through coregistration of follow-up MRI examinations. The likelihood of tumor recurrence at each individual voxel was assessed as a function of signal intensity on ADC maps and FLAIR images. Both single and combined multivariable logistic regression models were created.

RESULTS

A total of 419,473 voxels of data (105,477 voxels of data within tumor recurrence and 313,996 voxels of data on surrounding peritumoral edema) were analyzed. For future areas of recurrence, a 9.5% decrease in the ADC value (p < 0.001) and a 9.2% decrease in signal intensity on FLAIR images (p < 0.001) were shown, compared with findings for the surrounding peritumoral edema. Logistic regression revealed that the amount of signal loss on both ADC maps and FLAIR images correlated with the likelihood of tumor recurrence. A combined multiparametric logistic regression model was more specific in the prediction of tumor recurrence than was either single-variable model alone.

CONCLUSION

Areas of future GBM recurrence exhibit small but highly statistically significant differences in signal intensity on ADC maps and FLAIR images months before the development of abnormal enhancement occurs. A multiparametric logistic model calibrated to these changes can be used to estimate the burden of microscopic nonenhancing tumor and predict the location of recurrent disease. Computational big-data modeling performed at the voxel level is a powerful technique capable of discovering important but subtle patterns in imaging data.

Differentiation between Glioblastoma Multiforme and Primary Cerebral Lymphoma: Additional Benefits of Quantitative Diffusion-Weighted MR Imaging

The differentiation between glioblastoma multiforme (GBM) and primary cerebral lymphoma (PCL) is important because the treatments are substantially different. The purpose of this article is to describe the MR imaging characteristics of GBM and PCL with emphasis on the quantitative ADC analysis in the tumor necrosis, the most strongly-enhanced tumor area, and the peritumoral edema. This retrospective cohort study collected 104 GBM (WHO grade IV) patients and 22 immune-competent PCL (diffuse large B cell lymphoma) patients. All these patients had pretreatment brain MR DWI and ADC imaging. Analysis of conventional MR imaging and quantitative ADC measurement including the tumor necrosis (ADCn), the most strongly-enhanced tumor area (ADCt), and the peritumoral edema (ADCe) were done. ROC analysis with optimal cut-off values and area-under-the ROC curve (AUC) was performed. For conventional MR imaging, there are statistical differences in tumor size, tumor location, tumor margin, and the presence of tumor necrosis between GBM and PCL. Quantitative ADC analysis shows that GBM tended to have significantly (P<0.05) higher ADC in the most strongly-enhanced area (ADCt) and lower ADC in the peritumoral edema (ADCe) as compared with PCL. Excellent AUC (0.94) with optimal sensitivity of 90% and specificity of 86% for differentiating between GBM and PCL was obtained by combination of ADC in the tumor necrosis (ADCn), the most strongly-enhanced tumor area (ADCt), and the peritumoral edema (ADCe). Besides, there are positive ADC gradients in the peritumoral edema in a subset of GBMs but not in the PCLs. Quantitative ADC analysis in these three areas can thus be implemented to improve diagnostic accuracy for these two brain tumor types. The histological correlation of the ADC difference deserves further investigation.

Antiangiogenic Effect of Bevacizumab: Application of Arterial Spin-Labeling Perfusion MR Imaging in a Rat Glioblastoma Model

BACKGROUND AND PURPOSE

The usefulness of arterial spin-labeling for the evaluation of the effect of the antiangiogenic therapy has not been elucidated. Our aim was to evaluate the antiangiogenic effect of bevacizumab in a rat glioblastoma model based on arterial spin-labeling perfusion MR imaging.

MATERIALS AND METHODS

DSC and arterial spin-labeling perfusion MR imaging were performed by using a 9.4T MR imaging scanner in nude rats with glioblastoma. Rats were randomly assigned to the following 3 groups: control, 3-day treatment, and 10-day treatment after bevacizumab injection. One-way analysis of variance with a post hoc test was used to compare perfusion parameters (eg, normalized CBV and normalized CBF from DSC MR imaging and normalized CBF based on arterial spin-labeling) with microvessel area on histology. The Pearson correlations between perfusion parameters and microvessel area were also determined.

RESULTS

All of the normalized CBV from DSC, normalized CBF from DSC, normalized CBF from arterial spin-labeling, and microvessel area values showed significant decrease after treatment (P < .001, P < .001, P = .005, and P < .001, respectively). In addition, normalized CBV and normalized CBF from DSC and normalized CBF from arterial spin-labeling strongly correlated with microvessel area (correlation coefficient, r = 0.911, 0.869, and 0.860, respectively; P < .001 for all).

CONCLUSIONS

Normalized CBF based on arterial spin-labeling and normalized CBV and normalized CBF based on DSC have the potential for evaluating the effect of antiangiogenic therapy on glioblastomas treated with bevacizumab, with a strong correlation with microvessel area.

Neuronavigation-assisted trajectory planning for deep brain biopsy with susceptibility-weighted imaging

BACKGROUND

Susceptibility-weighted imaging (SWI) exploits susceptibility differences between tissues to enhance contrast in magnetic resonance imaging to enable the visualization of small blood vessels that are difficult to detect by other contrast agents. This study explored the value of SWI-based planning for neuronavigation-guided deep brain biopsies to reduce the incidence of post-surgical complications.

METHODS

The cohort of 84 patients was divided into 41 biopsies performed aided by SWI (SWI group) and 43 biopsies based on conventional T1w-Gd-based imaging (T1w-Gd group). Biopsy targets were determined using magnetic resonance spectroscopy (MRS) before the operation, and the safest trajectory was selected based on preoperative images of blood vessels.

RESULTS

Within 24 h of surgery, there was no radiographically identified bleeding, no blood extravasation and no clinical intracranial hypertension in the SWI group. Only one patient (2.5 %) with basal ganglia lymphoma developed transient hemiparesis after biopsy, who later recovered after undergoing symptomatic treatment. Complication rates in the SWI group were lower than in the T1w-Gd group, where a 7 % morbidity rate was encountered with one patient developing a permanent neurological deficit and two showing biopsy-associated hemorrhages. SWI imaging yielded a better visualization of subcortical vessels and deep-seated brain structures.

CONCLUSIONS

SWI-based imaging revealed significantly better visualization of small-caliber vasculature that was not detectable on conventional T1w-Gd imaging, minimizing damage to the brain and reducing postoperative complications. Furthermore, MRS can contribute significantly to target selection to improve the yield of image-guided biopsies.

Noninvasively detecting Isocitrate dehydrogenase 1 gene status in astrocytoma by dynamic susceptibility contrast MRI

PURPOSE

To investigate the value of dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) in the noninvasive evaluation of isocitrate dehydrogenase (IDH) 1 gene status in astrocytoma.

MATERIALS AND METHODS

The preoperative DSC MRI data of 91 lesions with pathologically confirmed astrocytoma were retrospectively analyzed. MR examination was performed on a 3T MRI scanner. The normalized maximum ratios of relative cerebral blood volume (rCBV ratio) of tumor parenchyma were measured. The enrolled astrocytoma patients were divided into six groups according to the World Health Organization (WHO) classification method and IDH1 gene status. The differences in the rCBV ratio of tumor parenchyma between the IDH1 gene mutant and wildtype groups of WHO grade II, III, and IV were compared and plotted receiver operating characteristic (ROC) curves were drawn.

RESULTS

The IDH1 gene mutant and wildtype groups of WHO grade II, III, and IV astrocytoma showed differences in the rCBV ratio (P = 0.005, 0.045, and 0.005, respectively). In WHO grade II, III, and IV astrocytoma, the area under the ROC curve was respectively 0.83, 0.86, and 0.94. The cutoff value of the rCBV ratio was respectively 2.20, 3.14, and 5.63.

CONCLUSION

The rCBV ratio value provided by DSC MRI provides a new potential imaging method for the noninvasive evaluation of the IDH1 status in astrocytoma.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:492-499.