Assessment of the pathological grade of astrocytic gliomas using an MRI score

Three-Dimensional Amide Proton Transfer-Weighted Imaging for Differentiating between Glioblastoma, IDH-Wildtype and Primary Central Nervous System Lymphoma

Distinguishing primary central nervous system lymphoma (PCNSL) from glioblastoma, isocitrate dehydrogenase (IDH)-wildtype is sometimes hard. Because the role of operation on them varies, accurate preoperative diagnosis is crucial. In this study, we evaluated whether a specific kind of chemical exchange saturation transfer imaging, i.e., amide proton transfer-weighted (APTw) imaging, was useful to distinguish PCNSL from glioblastoma, IDH-wildtype. A total of 14 PCNSL and 27 glioblastoma, IDH-wildtype cases were evaluated. There was no significant difference in the mean APTw signal values between the two groups. However, the percentile values from the 1st percentile to the 20th percentile APTw signals and the width1-100 APTw signals significantly differed. The highest area under the curve was 0.796, which was obtained from the width1-100 APTw signal values. The sensitivity and specificity values were 64.3% and 88.9%, respectively. APTw imaging was useful to distinguish PCNSL from glioblastoma, IDH-wildtype. To avoid unnecessary aggressive surgical resection, APTw imaging is recommended for cases in which PCNSL is one of the differential diagnoses.

Temporal radiographic and histological study of necrosis development in a mouse glioblastoma model

Tumor necrosis is a poor prognostic marker in glioblastoma (GBM) and a variety of other solid cancers. Accumulating evidence supports that necrosis could facilitate tumor progression and resistance to therapeutics. GBM necrosis is typically first detected by magnetic resonance imaging (MRI), after prominent necrosis has already formed. Therefore, radiological appearances of early necrosis formation and the temporal-spatial development of necrosis alongside tumor progression remain poorly understood. This knowledge gap leads to a lack of reliable radiographic diagnostic/prognostic markers in early GBM progression to detect necrosis. Recently, we reported an orthotopic xenograft GBM murine model driven by hyperactivation of the Hippo pathway transcriptional coactivator with PDZ-binding motif (TAZ) which recapitulates the extent of GBM necrosis seen among patients. In this study, we utilized this model to perform a temporal radiographic and histological study of necrosis development. We observed tumor tissue actively undergoing necrosis first appears more brightly enhancing in the early stages of progression in comparison to the rest of the tumor tissue. Later stages of tumor progression lead to loss of enhancement and unenhancing signals in the necrotic central portion of tumors on T1-weighted post-contrast MRI. This central unenhancing portion coincides with the radiographic and clinical definition of necrosis among GBM patients. Moreover, as necrosis evolves, two relatively more contrast-enhancing rims are observed in relationship to the solid enhancing tumor surrounding the central necrosis in the later stages. The outer more prominently enhancing rim at the tumor border probably represents the infiltrating tumor edge, and the inner enhancing rim at the peri-necrotic region may represent locally infiltrating immune cells. The associated inflammation at the peri-necrotic region was further confirmed by immunohistochemical study of the temporal development of tumor necrosis. Neutrophils appear to be the predominant immune cell population in this region as necrosis evolves. This study shows central, brightly enhancing areas associated with inflammation in the tumor microenvironment may represent an early indication of necrosis development in GBM.

Conventional MRI Features of Central Nervous System Embryonal Tumor, Not Otherwise Specified in Adults: Comparison with Glioblastoma

RATIONALE AND OBJECTIVES

The purpose of this study was to explore conventional MRI features that can accurately differentiate central nervous system embryonal tumor, not otherwise specified (CNS ETNOS) from glioblastoma (GBM) in adults.

MATERIALS AND METHODS

Preoperative conventional MRI images of 30 CNS ETNOS and 98 GBMs were analyzed by neuroradiologists retrospectively to identify valuable MRI features. Five blinded neuroradiologists independently reviewed all these MRI images, and scored MRI features on a five-point scale. Kendall's coefficient of concordance was used to measure inter-rater agreement. Diagnostic value was assessed by the area under the curve (AUC) of receiver operating curve, and sensitivity and specificity were also calculated.

RESULTS

Seven MRI features, including isointensity on T1WI, T2WI, and FLAIR, ill-defined margin, severe peritumoral edema, ring enhancement, and broad-based attachment sign, were helpful for the differential diagnosis of these two entities. Among these features, ring enhancement showed the highest inter-rater concordance (0.80). Ring enhancement showed the highest AUC value (0.79), followed by severe peritumoral edema (0.67). The combination of seven features showed the highest AUC value (0.86), followed by that of three features (ill-defined margin, severe peritumoral edema, and ring enhancement) (0.83).

CONCLUSION

Enhancement pattern, peritumoral edema, and margin are valuable for the discrimination between CNS ETNOS and GBM in adults.

Predictive accuracy of T2-FLAIR mismatch sign for the IDH-mutant, 1p/19q noncodeleted low-grade glioma: An updated systematic review and meta-analysis

BACKGROUND

The T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign, has been considered a highly specific imaging biomarker of IDH-mutant, 1p/19q noncodeleted low-grade glioma. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of T2-FLAIR mismatch sign for prediction of a patient with IDH-mutant, 1p/19q noncodeleted low-grade glioma, and identify the causes responsible for the heterogeneity across the included studies.

METHODS

A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before November 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Meta-regression analyses were also performed to determine factors influencing heterogeneity.

RESULTS

For all the 10 included cohorts from 8 studies, the pooled sensitivity was 40% (95% confidence interval [CI] 28-53%), and the pooled specificity was 100% (95% CI 95-100%). In the hierarchic summary receiver operating characteristic curve, the difference between the 95% confidence and prediction regions was relatively large, indicating heterogeneity among the studies. Higgins I2 statistics demonstrated considerable heterogeneity in sensitivity (I2 = 83.5%) and considerable heterogeneity in specificity (I2 = 95.83%). Among the potential covariates, it seemed that none of factors was significantly associated with study heterogeneity in the joint model. However, the specificity was increased in studies with all the factors based on the differences in the composition of the detailed tumors.

CONCLUSIONS

The T2-FLAIR mismatch sign is near-perfect specific marker of IDH mutation and 1p/19q noncodeletion.

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PURPOSE

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

MATERIALS AND METHODS

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34-75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5-T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

RESULTS

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10^-3 mm²/s and 1.21×10^-3 mm²/s cut-off values, yielding 70% sensitivity each (95% CI: 59-82% and 61-80%, respectively), 80% (95% CI: 79-98%) and 90% (95% CI: 82-97%) specificity, and 73% (95% CI: 66-91%) and 76% (95% CI: 72-90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58-83%) and 70% (95% CI: 59-84%), specificities of 90% (95% CI: 80-95%) and 70% (95% CI: 64-83%) and accuracies of 79% (95% CI: 69-89%) and 70% (95% CI: 64-77%), respectively.

CONCLUSION

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

Incidence of high grade gliomas presenting as radiographically non-enhancing lesions: experience in 111 surgically treated non-enhancing gliomas with tissue diagnosis

PURPOSE

Although non-enhancing lesions suspicious for glioma are usually assumed to be low grade glioma (LGG), some high grade glioma (HGG) do not enhance, which may lead to a delay in biopsy and/or resection, diagnosis, and treatment initiation. Thus, there is a clear need for a large-sample study that quantifies the rate of malignant, non-enhancing gliomas.

METHODS

We retrospectively reviewed our series of 561 consecutive surgically treated gliomas with tissue diagnosis, 111 of which were non-enhancing, to determine the prevalence of high-grade histology in radiographically presumed LGG. Relative expression of tumor markers were also reported for non-enhancing lesions to investigate genetic correlates.

RESULTS

We identified 561 surgically treated gliomas with tissue diagnosis from August 2012 to July 2018 and found that 111 patients (19.8%) demonstrated non-enhancing lesions suspicious for glioma on preoperative MRI. Thirty-one (27.9%) of the non-enhancing lesions were classified as HGGs (WHO Grade III or IV). Non-enhancing lesions were four times more likely to be HGG in patients older than 60 years than patients younger than 35 years (41.2% vs. 11.4%, Pearson Chi² p < 0.001). Binomial logistic regression showed a significant inverse effect of age on the presence of IDH mutation in non-enhancing HGGs (p = 0.007).

CONCLUSION

A clinically significant proportion (27.9%) of non-enhancing lesions were found to be HGG on final pathologic diagnosis. Thus, in patients with good functional and health status, especially those older than 60 years, we recommend obtaining tissue diagnosis of all lesions suspected to be glioma, even those that are non-enhancing, to guide diagnosis as well as early initiation of chemotherapy and radiation therapy.

A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma

OBJECTIVES

To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).

METHODS

We extracted tumor location and tumor volume (enhancing tumor, non-enhancing tumor, peritumor edema) features from 229 The Cancer Genome Atlas (TCGA)-LGG and TCGA-GBM cases. Through two sampling strategies, i.e., institution-based sampling and repeat random sampling (10 times, 70% training set vs 30% validation set), LASSO (least absolute shrinkage and selection operator) regression and nine-machine learning method-based models were established and evaluated.

RESULTS

Principal component analysis of 229 TCGA-LGG and TCGA-GBM cases suggested that the LRGG and GBM cases could be differentiated by extracted features. For nine machine learning methods, stack modeling and support vector machine achieved the highest performance (institution-based sampling validation set, AUC > 0.900, classifier accuracy > 0.790; repeat random sampling, average validation set AUC > 0.930, classifier accuracy > 0.850). For the LASSO method, regression model based on tumor frontal lobe percentage and enhancing and non-enhancing tumor volume achieved the highest performance (institution-based sampling validation set, AUC 0.909, classifier accuracy 0.830). The formula for the best performance of the LASSO model was established.

CONCLUSIONS

Computer-generated, clinically meaningful MRI features of tumor location and component volumes resulted in models with high performance (validation set AUC > 0.900, classifier accuracy > 0.790) to differentiate lower grade glioma and glioblastoma.

KEY POINTS

• Lower grade glioma and glioblastoma have significant different location and component volume distributions. • We built machine learning prediction models that could help accurately differentiate lower grade gliomas and GBM cases. We introduced a fast evaluation model for possible clinical differentiation and further analysis.

Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation

It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data. Using the dataset and the method ranked third in the BraTS 2018 challenge, we performed experiments to examine the impact of tumor type on segmentation performance. We propose to stratify the training dataset into high-grade glioma (HGG) and low-grade glioma (LGG) subjects and train two separate models. Although we observed only minor gains in overall mean dice scores by this stratification, examining case-wise rankings of individual subjects revealed statistically significant improvements. Compared to a baseline model trained on both HGG and LGG cases, two separately trained models led to better performance in 64.9% of cases (p < 0.0001) for the tumor core. An analysis of subjects which did not profit from stratified training revealed that cases were missegmented which had poor image quality, or which presented clinically particularly challenging cases (e.g., underrepresented subtypes such as IDH1-mutant tumors), underlining the importance of such latent variables in the context of tumor segmentation. In summary, we found that segmentation models trained on the BraTS 2018 dataset, stratified according to tumor type, lead to a significant increase in segmentation performance. Furthermore, we demonstrated that this gain in segmentation performance is evident in the case-wise ranking of individual subjects but not in summary statistics. We conclude that it may be useful to consider the segmentation of brain tumors of different types or grades as separate tasks, rather than developing one tool to segment them all. Consequently, making this information available for the test data should be considered, potentially leading to a more clinically relevant BraTS competition.

Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy for Differentiating Between Enhanced Gliomas and Malignant Lymphomas

BACKGROUND

Although the treatment strategies for malignant lymphomas and gliomas differ, it is usually difficult to preoperatively distinguish between them. Magnetic resonance spectroscopy (MRS) was recently reported to be useful for preoperative diagnoses; however, MRS data analysis using LCModel, which is a quantitative and objective method, was performed in only a few of the existing reports.

METHODS

The clinical characteristics, conventional magnetic resonance imaging findings, and MRS parameters using LCModel were evaluated to identify the factors that can help distinguish between malignant lymphomas and enhanced gliomas.

RESULTS

In total, 59 cases were evaluated, including 13 cases of malignant lymphoma, 1 case of pilocytic astrocytoma, 5 cases of grade Ⅱ glioma, 5 cases of grade Ⅲ glioma, and 35 cases of glioblastoma. There was no correlation between clinical characteristics (sex and age) and diagnosis. Neither T1- nor T2-weighted image was useful for differentiation between the 2 forms of tumors, but the apparent diffusion coefficient minimum value was useful for distinguishing malignant lymphomas from gliomas, with an area under the curve (AUC) value of 0.852. MRS analysis using LCModel revealed differences in glutamate (Glu), N-acetylaspartate (NAA) + N-acetylaspartylglutamate (NAAG), Glu + glutamine, and Lipid (Lip) 13a + Lip13b between malignant lymphomas and gliomas. The largest AUC was 0.904, which was obtained for the Glu level, followed by 0.883 and 0.866 for NAA + NAAG and Lip13a + Lip13b, respectively.

CONCLUSIONS

Quantitative analysis of proton-MRS using LCModel is considered to be a valuable method for distinguishing between gliomas and malignant lymphomas.

Sequential Apparent Diffusion Coefficient for Assessment of Tumor Progression in Patients with Low-Grade Glioma

BACKGROUND AND PURPOSE

Early and accurate identification of tumor progression in patients with low-grade gliomas is challenging. We aimed to assess the role of quantitative ADC analysis in the sequential follow-up of patients with low-grade gliomas as a potential imaging marker of tumor stability or progression.

MATERIALS AND METHODS

In this retrospective study, patients with a diagnosis of low-grade glioma with at least 12 months of imaging follow-up were retrospectively reviewed. Two neuroradiologists independently reviewed sequential MR imaging in each patient to determine tumor progression using the Response Assessment in Neuro-Oncology criteria. Normalized mean ADC (ADC_mean) and 10th percentile ADC (ADC₁₀) values from FLAIR hyperintense tumor volume were calculated for each MR image and compared between patients with stable disease versus tumor progression using univariate analysis. The interval change of ADC values between sequential scans was used to differentiate stable disease from progression using the Fisher exact test.

RESULTS

Twenty-eight of 69 patients who were evaluated met our inclusion criteria. Fifteen patients were classified as stable versus 13 patients as having progression based on consensus reads of MRIs and the Response Assessment in Neuro-Oncology criteria. The interval change of ADC values showed greater concordance with ultimate lesion disposition than quantitative ADC values at a single time point. The interval change in ADC₁₀ matched the expected pattern in 12/13 patients with tumor progression (overall diagnostic accuracy of 86%, P <.001). On average, the ADC₁₀ interval change predicted progression 8 months before conventional MR imaging.

CONCLUSIONS

The interval change of ADC₁₀ values can be used to identify progression versus stability of low-grade gliomas with a diagnostic accuracy of 86% and before apparent radiologic progression on conventional MR imaging.

T2-FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project

Purpose: Lower-grade gliomas (WHO grade II/III) have been classified into clinically relevant molecular subtypes based on IDH and 1p/19q mutation status. The purpose was to investigate whether T2/FLAIR MRI features could distinguish between lower-grade glioma molecular subtypes.Experimental Design: MRI scans from the TCGA/TCIA lower grade glioma database (n = 125) were evaluated by two independent neuroradiologists to assess (i) presence/absence of homogenous signal on T2WI; (ii) presence/absence of "T2-FLAIR mismatch" sign; (iii) sharp or indistinct lesion margins; and (iv) presence/absence of peritumoral edema. Metrics with moderate-substantial agreement underwent consensus review and were correlated with glioma molecular subtypes. Somatic mutation, DNA copy number, DNA methylation, gene expression, and protein array data from the TCGA lower-grade glioma database were analyzed for molecular-radiographic associations. A separate institutional cohort (n = 82) was analyzed to validate the T2-FLAIR mismatch sign.Results: Among TCGA/TCIA cases, interreader agreement was calculated for lesion homogeneity [κ = 0.234 (0.111-0.358)], T2-FLAIR mismatch sign [κ = 0.728 (0.538-0.918)], lesion margins [κ = 0.292 (0.135-0.449)], and peritumoral edema [κ = 0.173 (0.096-0.250)]. All 15 cases that were positive for the T2-FLAIR mismatch sign were IDH-mutant, 1p/19q non-codeleted tumors (P < 0.0001; PPV = 100%, NPV = 54%). Analysis of the validation cohort demonstrated substantial interreader agreement for the T2-FLAIR mismatch sign [κ = 0.747 (0.536-0.958)]; all 10 cases positive for the T2-FLAIR mismatch sign were IDH-mutant, 1p/19q non-codeleted tumors (P < 0.00001; PPV = 100%, NPV = 76%).Conclusions: Among lower-grade gliomas, T2-FLAIR mismatch sign represents a highly specific imaging biomarker for the IDH-mutant, 1p/19q non-codeleted molecular subtype. Clin Cancer Res; 23(20); 6078-85. ©2017 AACR.

Multiparametric MRI-based differentiation of WHO grade II/III glioma and WHO grade IV glioblastoma

Non-invasive, imaging-based examination of glioma biology has received increasing attention in the past couple of years. To this end, the development and refinement of novel MRI techniques, reflecting underlying oncogenic processes such as hypoxia or angiogenesis, has greatly benefitted this research area. We have recently established a novel BOLD (blood oxygenation level dependent) based MRI method for the measurement of relative oxygen extraction fraction (rOEF) in glioma patients. In a set of 37 patients with newly diagnosed glioma, we assessed the performance of a machine learning model based on multiple MRI modalities including rOEF and perfusion imaging to predict WHO grade. An oblique random forest machine learning classifier using the entire feature vector as input yielded a five-fold cross-validated area under the curve of 0.944, with 34/37 patients correctly classified (accuracy 91.8%). The most important features in this classifier as per bootstrapped feature importance scores consisted of standard deviation of T1-weighted contrast enhanced signal, maximum rOEF value and cerebral blood volume (CBV) standard deviation. This study suggests that multimodal MRI information reflects underlying tumor biology, which is non-invasively detectable through integrative data analysis, and thus highlights the potential of such integrative approaches in the field of radiogenomics.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas.

MATERIALS AND METHODS

This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis.

RESULTS

Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters.

CONCLUSIONS

In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV.

Diffusely infiltrating astrocytomas: pathology, molecular mechanisms and markers

Diffusely infiltrating astrocytomas include diffuse astrocytomas WHO grade II and anaplastic astrocytomas WHO grade III and are classified under astrocytic tumours according to the current WHO Classification. Although the patients generally have longer survival as compared to those with glioblastoma, the timing of inevitable malignant progression ultimately determines the prognosis. Recent advances in molecular genetics have uncovered that histopathologically diagnosed astrocytomas may consist of two genetically different groups of tumours. The majority of diffusely infiltrating astrocytomas regardless of WHO grade have concurrent mutations of IDH1 or IDH2, TP53 and ATRX. Among these astrocytomas, no other genetic markers that may distinguish grade II and grade III tumours have been identified. Those astrocytomas without IDH mutation tend to have a distinct genotype and a poor prognosis comparable to that of glioblastomas. On the other hand, diffuse astrocytomas that arise in children do not harbour IDH/TP53 mutations, but instead display mutations of BRAF or structural alterations involving MYB/MYBL1 or FGFR1. A molecular classification may thus help delineate diffusely infiltrating astrocytomas into distinct pathogenic and prognostic groups, which could aid in determining individualised therapeutic strategies.

MRI grading versus histology: predicting survival of World Health Organization grade II-IV astrocytomas

BACKGROUND AND PURPOSE

Histologic grading of intracranial astrocytomas is affected by sampling error and substantial inter- and intraobserver variability. We proposed that incorporating MR imaging into grading will predict patient survival more accurately than histopathology alone.

MATERIALS AND METHODS

Patients with a new diagnosis of World Health Organization grades II-IV astrocytoma or mixed oligoastrocytoma diagnosed between September 2007 and December 2010 were identified. Two hundred forty-five patients met the inclusion criteria. Preoperative MRIs were independently reviewed by 2 readers blinded to the histologic grade, and an MR imaging grade was given. The MR imaging and histopathologic grades were compared with patient survival.

RESULTS

Patients with grade II or III astrocytomas on histology but evidence of necrosis on MR imaging (consistent with a grade IV tumor) had significantly worse survival than patients with the same histology but no evidence of necrosis on MR imaging (P = .002 for grade II histology and P = .029 for grade III). Their survival was not significantly different from that in patients with grade IV tumors on histology (P = .164 and P = .385, respectively); this outcome suggests that all or most are likely to have truly been grade IV tumors. MR imaging evidence of necrosis was less frequent in grade II and III oligoastrocytomas, preventing adequate subgroup analysis.

CONCLUSIONS

MR imaging can improve grading of intracranial astrocytomas by identifying patients suspected of being undergraded by histology, with high interobserver agreement. This finding has the potential to optimize patient management, for example, by encouraging more aggressive treatment earlier in the patient's course.

Papillary meningioma with vital abnormal vessels

Papillary meningioma is an uncommon meningioma subtype of World Health Organization grade III. It could show some radiologic profiles pointing to malignant behavior, such as a cystic change, a heterogeneous enhancement, and an ill- defined border. However, to date, the radiologic profile described in this article has not been reported in previous literatures, and it is just the characteristic being considered as the major cause for patients’ death. A 16-year-old adolescent boy with a 6-month history of headache was admitted to our department on June 28, 2012. Magnetic resonance imaging showed a giant well-defined mass in the left temporal region, with a severe flow void on T2-weighted image and an abundant stripelike enhancement on T1-weighted contrast-enhanced scan. In view of its middle cranial fossa location (one predilection site for meningioma), meningioma was suspected preoperatively. A regular left frontotemporal craniotomy was performed. Unexpectedly, extreme hemorrhage happened intraoperatively, and it was difficult to stop the bleeding. After identification of no hemorrhage in the operative cavity through intraoperative magnetic resonance imaging, the operation was finished, with an overall blood loss of 15,000 mL. The patient died of brain stem dysfunction the second day after the operation.

Contrast-enhanced MR imaging in neuroimaging

MR imaging without and with gadolinium-based contrast agents (GBCAs) is an important imaging tool for defining normal anatomy and characteristics of lesions. GBCAs have been used in contrast-enhanced MR imaging in defining and characterizing lesions of the central nervous system for more than 20 years. The combination of unenhanced and GBCA-enhanced MR imaging is the clinical gold standard for the noninvasive detection and delineation of most intracranial and spinal lesions. MR imaging has a high predictive value that rules out neoplasm and most inflammatory and demyelinating processes of the central nervous system.

Conventional MRI evaluation of gliomas

MRI using T(1) weighted, T(2) weighted and gadolinium-enhanced sequences plays a central clinical role in diagnosis, characterisation, surveillance and therapeutic monitoring of gliomas. Such conventional MRI protocols provide high resolution multiplanar structural information, and substantially improved tissue characterisation compared with CT. However, the MRI signal lacks biological specificity, e.g. T(2) weighted dependent signal abnormality is dominated by tissue water content, and contrast enhancement reflects a non-specific increase in blood-brain barrier permeability. This limits non-invasive glioma diagnosis, characterisation and therapeutic planning and assessment of active tumour load may be confounded by treatment-related effects. The complex features of glioma morphology and often subtle changes between MRI examinations are also frequently difficult to detect reliably by visual inspection of the images, even by an experienced radiologist. Moreover, the most widely used response criteria in clinical practice and therapeutic trials rely on linear measurements of enhancing tumour and are further challenged by the irregular shape and heterogeneous composition of gliomas. This contributes to the poor correlation of these criteria with hard clinical endpoints. While conventional MRI is widely available and provides essential anatomical information, the lack of pathology-specific biomarkers available from standard MRI sequences and methods of image analysis used limit overall diagnostic and prognostic efficacy of the examination.

[Intra-axial brain tumors]

Neuroradiology plays a key role in the diagnosis of brain tumors. Computed tomography (CT) and specially magnetic resonance imaging (MRI) allow accurate anatomic depiction of intracerebral lesions. The implementation of native and contrast studies allows the characterization of the various lesions encountered in the majority of cases. In this review the imaging aspects on CT and MRI of the most common primary intra-axial brain tumors will be discussed.

Clinical Application of MR Spectroscopy and Imaging of Brain Tumor

Advanced imaging techniques, including diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS) can provide more information than that regarding anatomy. These techniques have been commonly used in the clinical field and recently been shown useful in diagnosing brain tumors, especially in cases difficult to specify using conventional imaging. Differentiation requires more than attention to each advanced image. Diagnostic accuracy improves by combining information from MRS with that from other sequences, such as maps of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) generated from DTI and of cerebral blood volume (CBV) generated from PWI. We show clinical applications of advanced imaging techniques, combined MRS, for brain tumor.

Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas

PURPOSE

To evaluate if the relative tumor blood volume (rTBV) using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) can aid in distinguishing low- from high-grade nonenhancing astrocytomas.

MATERIALS AND METHODS

Seventeen patients with histologically proven astrocytomas underwent MRI including DSC-MRI. Maximum TBV regions of interest were recorded from each neoplasm and normalized to contralateral normal white matter. Demographic features, diagnostic MRI findings including tumor volumes, and the normalized rTBV ratios were compared between low-grade (I and II, LGA, n = 6) and high-grade (III) astrocytomas (HGA, n = 11) using Mann-Whitney's U-test and receiver operating characteristic (ROC) analysis.

RESULTS

Maximum rTBV ratios were statistically higher for HGA (1.11 ± 0.13) than LGA (0.66 ± 0.17, P < 0.005) with the best cutoff threshold at 0.94 (sensitivity of 90.9%, specificity of 100%). Differences in mean age and tumor volume on fluid-attenuated inversion recovery (FLAIR) imaging between the two groups did not reach statistical difference (P = 0.22, 0.36).

CONCLUSION

The addition of DSC-MRI can aid in accurate grading of nonenhancing astrocytomas with high sensitivity and specificity.

FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading

PURPOSE

Treatment and prognosis of gliomas depend on their histological tumour grade. The aim of the study was to evaluate the potential of [(18)F]fluoroethyltyrosine (FET) PET for non-invasive tumour grading in untreated patients.

METHODS

Dynamic FET PET studies were performed in 54 patients who, based on MRI, were estimated to have low grade (LG; n = 20), intermediate (WHO II-III; n = 4) or high grade (HG; n = 30) tumours. For standard evaluation, tumour SUV(max) and the ratio to background (SUV(max)/BG) were calculated (sum image: 20-40 min). For dynamic evaluation, mean SUV values within a 90% isocontour ROI (SUV90) and the SUV90/BG ratios were determined for each time frame to evaluate the course of FET uptake. Results were correlated with histopathological findings from PET-guided stereotactic biopsies.

RESULTS

Histology revealed gliomas in all patients. Using the standard method a statistically significant difference (p = 0.001) was found between LG (n = 20; SUV(max)/BG: 2.16 +/- 0.98) and HG (n = 34; SUV(max)/BG: 3.29 +/- 1.06) gliomas (opt. threshold 2.58: SN71%/SP85%/area under ROC curve [AUC]:0.798), however, with a marked overlap between WHO II to IV tumours. Time activity curves showed slight increase in LG, whereas HG tumours presented with an early peak (10-20 min) followed by a decrease. Dynamic evaluation successfully separated LG from HG gliomas with higher diagnostic accuracy (SN94%/SP100%/AUC:0.967).

CONCLUSIONS

Based on the ratio-based method, a statistically significant difference was found between LG and HG gliomas. Due to the interindividual variability, however, no reliable individual grading was possible. In contrast, dynamic evaluation allowed LG and HG gliomas to be differentiated with high diagnostic power and, thus, should supplement the conventional method.

Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging

INTRODUCTION

Diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) and MR spectroscopy (MRS) provide useful data for tumor evaluation. To assess the contribution of these multimodal techniques in grading glial neoplasms, we compared the value of DWI, PWI and MRS in the evaluation of histologically proven high- and low-grade gliomas in a population of 105 patients.

METHODS

Independently for each modality, the following variables were used to compare the tumors: minimum apparent diffusion coefficient (ADC) and maximum relative cerebral blood volume (rCBV) normalized values between tumor and healthy tissue, maximum Cho/Cr ratio and minimum NAA/Cr ratio in tumor, and scored lactate and lipid values in tumor. The Mann-Whitney and Wilcoxon tests were employed to compare DWI, PWI and MRS between tumor types. Logistic regression analysis was used to determine which parameters best increased the diagnostic accuracy in terms of sensitivity, specificity, and positive and negative predictive values. ROC curves were determined for parameters with high sensitivity and specificity to identify threshold values to separate high- from low-grade lesions.

RESULTS

Statistically significant differences were found for rCBV tumor/normal tissue ratio, and NAA/Cr ratio in tumor and Cho/Cr ratio in tumor between low- and high-grade tumors. The best performing single parameter for group classification was the normalized rCBV value; including all parameters, statistical significance was reached by rCBV tumor/normal tissue ratio, NAA/Cr tumor ratio and lactate. From the ROC curves, a high probability for a neoplasm to be a high-grade lesion was associated with a rCBV tumor/normal tissue ratio of >1.16 and NAA/Cr tumor ratio of <0.44.

CONCLUSION

Combining PWI and MRS with conventional MR imaging increases the accuracy of the attribution of malignancy to glial neoplasms. The best performing parameter was found to be the perfusion level.

Neuroimaging characteristics in subgroup of GBMs with p53 overexpression

Glioblastoma multiforme (GBM) is a heterogeneous group of tumors, and neuroimaging characteristics have not been well-defined in molecular subgroups. Eighty-five patients with GBM were analyzed regarding imaging characteristics and correlation to p53 expression. The p53 positivity was graded according to percentage of positive cells (Grade 0, for < 10%; Grade 1, for <25%; Grade 2, for 26-50%; Grade 3, for >50% labeled cells). Imaging characteristics evaluated in the preoperative MRI were location and number of lesions, dimensions of enhancing lesion and of surrounding edema, mass effect, tumor borders, enhancement pattern after intravenous contrast administration, and tumor necrosis. Eighteen tumors had p53 expression >50% in immunohistochemical staining. Preoperative MRI of patients harboring those tumors with high p53 positivity revealed typical lesions with ring enhancement pattern and well-defined borders in T1-weighted images with contrast, and they were significantly different from other groups of p53 expression. There was no difference in terms of location and number of the lesions, dimensions of enhancing lesion and surrounding edema, mass effect, and the tumor necrosis between four different groups of p53 expression. A special subgroup of GBMs with p53 overexpression has ring enhancement pattern and well-defined border on MRI that may be influential in preoperative planning and postoperative management of adjunct therapy.

Anaplastic Astrocytoma Presenting as Reversible Posterior Leukoencephalopathy Syndrome

We report a 60-year-old man with grade III astrocytoma, who presented with status epilepticus. The initial MRI did not demonstrate typical findings of an astrocytoma but rather showed reversible posterior leukoencephalopathy syndrome (RPLS). N-Isopropil-p-[I] iodoamphetamine single photon emission computed tomography (SPECT) demonstrated hyperperfusion in this area. A brain tumor should be considered and the patient carefully followed by MRI, even if the MRI white matter lesion pattern suggests RPLS. This is especially relevant in the presence of atypical findings for RPLS on SPECT.

Comparison Between Contrast-enhanced Magnetic Resonance Imaging and Technetium 99m Glucohepatonic Acid Single Photon Emission Computed Tomography With Histopathologic Correlation in Gliomas

OBJECTIVE

: To determine and compare the accuracy of contrast-enhanced magnetic resonance imaging (MRI) and Technetium 99m glucohepatonic acid single photon emission computed tomography (Tc-GHA SPECT) in grading of gliomas, compared with neuropathologic findings.

MATERIALS AND METHODS

: The study included 20 adult patients (13 men and 7 women) with clinical/radiological suspicion of brain tumor (glial tumor) who were subjected to magnetic resonance examination and Tc-GHA brain SPECT.The lesions were evaluated by using MRI imaging score, based on 9 MRI criteria. Based on the discrimination threshold of 0.9 for mean MRI score, the gliomas were graded as low- or high-grade glioma. The Tc-GHA SPECT retention index was calculated as the ratio between delayed and early uptake ratios. Based on the discrimination threshold of 1 for Tc-GHA SPECT retention index, the gliomas were graded as low- or high-grade glioma.The diagnosis was verified by means of histopathologic examination in all patients (open surgery in 19 patients and stereotactic biopsy in 1 patient). Correlation between MRI findings/scores, SPECT scores, and histopathologic grades was done in all the patients, and comparison between MRI and Tc-GHA SPECT was made using paired Student t test and correlation coefficient.

RESULTS

: The study revealed significant difference between the mean MRI scores and early uptake ratio, delayed uptake ratio, and retention index of low-grade (grades I-II) and high-grade (grades III-IV) gliomas. No statistically significant difference could be demonstrated between the abilities of contrast-enhanced MRI and Tc-GHA SPECT to allow differentiation between high- and low-grade gliomas. The accuracy of MRI (78.4%), however, was slightly higher than that of Tc-GHA SPECT (73.68%). However, Tc-GHA SPECT allowed differentiation between high-grade gliomas (between grades III and IV gliomas).

CONCLUSIONS

: The accuracy of contrast-enhanced MRI in the distinction of high- and low-grade malignancy was higher than that of Tc-GHA SPECT. The performance of Tc-GHA SPECT adds little in determining tumor grade when MRI is performed. However, it may act as a useful adjunct to differentiate between grades III and IV gliomas.

Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study

OBJECT

Biopsy targeting based on MR imaging alone may fail to identify malignant areas in brain gliomas. Considering the differences in relative Cerebral Blood Volume (rCBV) ratios reported among tumour grades, we evaluated whether perfusion-weighted MR imaging (PWI) could usefully implement the routine preoperative imaging by detecting those areas bearing a higher yield for malignancy to guide the stereotactic biopsy or the surgical removal.

CLINICAL MATERIAL AND METHODS

We studied a series of 55 consecutive patients with newly diagnosed brain glioma using both conventional MR imaging and PWI in the preoperative assessment. The pathological diagnosis was established by stereotactic biopsy in 29 cases and by craniotomy in 24 cases. We evaluated the patient survival to detect undergrading.

DISCUSSION

Independent from contrast-enhancement, perfusion-weighted MR imaging improved the target selection in stereotactic biopsy guidance and the removal of malignant areas in tumours amenable to surgery. Particularly sensitive to the perfused part of the tumour as to small regional changes, rCBV maps allowed a better detection of malignant areas. The rCBV ratios correlated significantly to the tumour grade and the final outcome (p < 0.01).

CONCLUSIONS

We found PWI valuable in the preoperative assessment of brain gliomas, discriminating high from low-grade gliomas. PWI can easily be performed on widely available MR imaging systems as part of the routine imaging of gliomas.

Noninvasive diagnostic assessment of brain tumors using combined in vivo MR imaging and spectroscopy

To determine the potential value of multimodal MRI for the presurgical management of patients with brain tumors, we performed combined magnetic resonance imaging (MRI) and proton MR spectroscopy (MRS) in 164 patients who presented with tumors of various histological subtypes confirmed by surgical biopsy. Univariate statistical analysis of metabolic ratios carried out on the first 121 patients demonstrated significant differences in between-group comparisons, but failed to provide sufficiently robust classification of individual cases. However, a multivariate statistical approach correctly classified the tumors using linear discriminant analysis (LDA) of combined MRI and MRS data. After initial separation of contrast-enhancing and non-contrast-enhancing lesions, 91% of the former and 87% of the latter were correctly classified. The results were stable when this diagnostic strategy was tested on the additional 43 patients included for validation after the initial statistical analysis, with over 90% of correct classification. Combined MRI and MRS had superior diagnostic value compared to MRS alone, especially in the contrast-enhancing group. This study shows the clinical value of a multivariate statistical analysis based on multimodal MRI and MRS for the noninvasive evaluation of intracranial tumors.

Magnetic Resonance Imaging Characteristics Predict Epidermal Growth Factor Receptor Amplification Status in Glioblastoma

PURPOSE

Two clinical-molecular glioblastoma subtypes have been described: "primary" glioblastomas arise de novo in older patients and often overexpress epidermal growth factor receptor (EGFR); "secondary" glioblastomas progress from lower-grade tumors in younger patients and commonly have TP53 mutations. EGFR overexpression correlates in experimental gliomas with increased angiogenesis, edema, and invasion. No radiographic predictors of molecular glioblastoma subtype are known.

EXPERIMENTAL DESIGN

We retrospectively reviewed 75 glioblastomas, classified as TP53-mutated (n=11), EGFR-amplified (n=31), or neither (non-TP53/non-EGFR; n=33). Four variables were derived from preoperative magnetic resonance imaging: (a) T2/T1, the ratio of T2-bright volume to enclosed T1-enhancing volume; (b) percentage of tumor volume that was necrosis; and (c and d) T1 and T2 border sharpness coefficients (BSC), the rates of change in grayscale intensity of adjacent 0.02-cm2 voxels traversing the anterior, posterior, and lateral borders on T1-enhanced and T2 images.

RESULTS AND CONCLUSIONS

Mean T2/T1 was 4.7 for EGFR-amplified glioblastomas, greater than that of TP53-mutated glioblastomas (2.3) or non-TP53/non-EGFR glioblastomas (2.6; P<0.00005). All four tumors with T2/T1>7.2 were EGFR-amplified; 0 of 15 with T2/T1<4.7 underwent gross total resection. The mean T2 BSC of EGFR-amplified glioblastomas was 33.7, less sharp (P<0.0000005) than TP53-mutated (72.2) and non-TP53/non-EGFR glioblastomas (81.2). All 15 glioblastomas with T2 BSC<30.8 were EGFR-amplified. Percentage necrosis and T1 BSC did not differ between glioblastoma subtypes. The increased T2/T1 ratio and decreased T2 BSC in EGFR-overexpressing tumors are the first radiographic distinctions described between glioblastoma molecular subtypes. These findings may reflect increased angiogenesis, edema, and/or invasion in EGFR-overexpressing tumors.

Use of fractional anisotropy value by diffusion tensor MRI for preoperative diagnosis of astrocytic tumors: case report

The fractional anisotropy (FA) value calculated by diffusion tensor MRI can indicate the degree of directionality of water diffusion in astrocytic tumors. Here, we report a case of anaplastic astrocytoma in which FA proved invaluable for the preoperative differential diagnosis. A 60-year-old man complained of headache, and underwent routine neuroimaging and DTI. The routine images suggested a low-grade glioma in the left temporal lobe, based on lack of enhancement on MRI with contrast medium and lack of tumor staining on angiograms, whereas FA value was very high. Based on these findings, a preoperative diagnosis of high-grade glioma was suspected. The surgical specimen exhibited the histological features of anaplastic astrocytoma with a high density of spindle shaped cells and low vascularity. In this report, we discuss the relationship between FA and other characteristics of the present tumor, and discuss the utility of FA measurement in astrocytic tumors.

Intra-axial brain tumours

The radiological diagnosis and differential diagnosis of intra-axial tumours no longer relies on CT scan and routine MR sequences alone. Standard multiplanar imaging has to be combined with fMRI to allow the exact anatomic location of the lesion and precise determination of the extension of the tumour. Perfusion and diffusion MR is becoming more and more important in the differential diagnosis of cerebral mass lesions and in the grading and typing of gliomas. More sophisticated techniques such as diffusion tensor imaging and spectroscopy will further enhance the value of the radiological studies.

Development of a new reporter gene system--dsRed/xanthine phosphoribosyltransferase-xanthine for molecular imaging of processes behind the intact blood-brain barrier

We report the development of a novel dual-modality fusion reporter gene system consisting of Escherichia coli xanthine phosphoribosyltransferase (XPRT) for nuclear imaging with radiolabeled xanthine and Discosoma red fluorescent protein for optical fluorescent imaging applications. The dsRed/XPRT fusion gene was successfully created and stably transduced into RG2 glioma cells, and both reporters were shown to be functional. The level of dsRed fluorescence directly correlated with XPRT enzymatic activity as measured by ribophosphorylation of [14C]-xanthine was in vitro (Ki = 0.124 +/- 0.008 vs. 0.00031 +/- 0.00005 mL/min/g in parental cell line), and [*]-xanthine octanol/water partition coefficient was 0.20 at pH = 7.4 (logP = -0.69), meeting requirements for the blood-brain barrier (BBB) penetrating tracer. In the in vivo experiment, the concentration of [14C]-xanthine in the normal brain varied from 0.20 to 0.16 + 0.05% dose/g under 0.87 + 0.24% dose/g plasma radiotracer concentration. The accumulation in vivo in the transfected flank tumor was to 2.4 +/- 0.3% dose/g, compared to 0.78 +/- 0.02% dose/g and 0.64 +/- 0.05% dose/g in the control flank tumors and intact muscle, respectively. [14C]-Xanthine appeared to be capable of specific accumulation in the transfected infiltrative brain tumor (RG2-dsRed/XPRT), which corresponded to the 585 nm fluorescent signal obtained from the adjacent cryosections. The images of endogenous gene expression with the "sensory system" have to be normalized for the transfection efficiency based on the "beacon system" image data. Such an approach requires two different "reporter genes" and two different "reporter substrates." Therefore, the novel dsRed/XPRT fusion gene can be used as a multimodality reporter system in the biological applications requiring two independent reporter genes, including the cells located behind the BBB.

Noninvasive grading of untreated gliomas: a comparative study of MR imaging and 3-(iodine 123)-L-alpha-methyltyrosine SPECT

PURPOSE

To compare the accuracy of magnetic resonance (MR) imaging scores with that of 3-(iodine 123)-L-alpha-methyltyrosine ((123)I-IMT) single photon emission computed tomography (SPECT) in the noninvasive grading of untreated gliomas.

MATERIALS AND METHODS

The study comprised 15 patients with low-grade gliomas (grades I-II, according to World Health Organization criteria) and 33 patients with high-grade gliomas (grades III-IV). The lesions were evaluated by using an MR imaging score based on nine criteria. The (123)I-IMT uptake was quantified as the ratio between the amino acid uptake in the tumor and that in the contralateral hemisphere. To test for potentially significant differences in diagnostic performance between contrast material-enhanced MR imaging and (123)I-IMT SPECT, binormal receiver operating characteristic curves were fitted to the data and compared by using the area test.

RESULTS

The accuracy of MR imaging in the noninvasive grading of untreated gliomas was higher than that of (123)I-IMT SPECT (88% vs 79%). However, the difference in diagnostic performance was not significant on the basis of findings at receiver operating characteristic analysis (P >.2). Neither MR imaging nor (123)I-IMT SPECT allowed differentiation between high-grade gliomas (grades III and IV).

CONCLUSION

Although (123)I-IMT uptake is significantly higher in high-grade gliomas than in low-grade gliomas, the performance of (123)I-IMT SPECT adds little to the accuracy of determining tumor grade when MR imaging is performed.

MRI enhancement and microvascular density in gliomas. Correlation with tumor cell proliferation

RATIONALE AND OBJECTIVES

Angiogenesis and proliferation activity are important indicators of tumor behavior in human gliomas. The authors studied how tumor enhancement in MR imaging and intratumoral vascular density were correlated with cell proliferation in cerebral gliomas.

METHODS

The authors studied retrospectively 62 cerebral gliomas. Patients were examined before surgery with contrast-enhanced MR imaging. Microvessel density and the cell proliferation rate of tumor specimens were measured immunohistochemically using factor VIII and MIB-1 antibodies. Contrast enhancement of the tumors was evaluated by two radiologists.

RESULTS

Contrast enhancement was observed in 45 tumors and was correlated with histologic cell proliferation (P = 0.0007) and microvessel density (P = 0.01). There was also a correlation between tumor vascular density and the cell proliferation rate (r = 0.51, P < 0.0001). Histologic tumor grade was associated with vascular density (P = 0.001).

CONCLUSIONS

Lesion enhancement on preoperative contrast-enhanced MR imaging correlates with vascularity and proliferation activity of gliomas. The additional correlation between tumor vascularity and proliferation suggests that intratumoral microvessel density could be useful in estimating tumor proliferation.

Magnetic resonance characteristics of meningiomas arising from the falcotentorial junction

We report the magnetic resonance features of meningiomas at the falcotentorial junction in six patients. Three tumors showed several characteristics on magnetic resonance images. They demonstrated hypo- or isointensity on T1-weighted images and iso- or hyperintensity on T2-weighted images. The degree of contrast between tumor and brain parenchyma, however, was less marked. The tumors often had a mottled appearance on T2-weighted images owing to their hypervascularity. All of the tumors had a peritumoral rim, and no edema was seen in adjacent brain parenchyma. With the injection of gadolinium, the tumors in all patients markedly enhanced; the adjacent dura was enhanced in four patients.