Advances in imaging low-grade gliomas

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.

The Role of the T2-FLAIR Mismatch Sign as an Imaging Marker of IDH Status in a Mixed Population of Low- and High-Grade Gliomas

Our study evaluated the role of the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign in detecting isocitrate dehydrogenase (IDH) mutations based on a mixed sample of 24 patients with low- and high- grade gliomas. The association between the two was realized using univariate and multivariate logistic regression analysis. There was a substantial agreement between the two raters for the detection of the T2-FLAIR mismatch sign (Cohen's kappa coefficient was 0.647). The T2-FLAIR mismatch sign when co-registered with the degree of tumor homogeneity were significant predictors of the IDH status (OR 29.642; 95% CI 1.73-509.15, p = 0.019). The probability of being IDH mutant in the presence of T2-FLAIR mismatch sign was as high as 92.9% (95% CI 63-99%). The sensitivity and specificity of T2-FLAIR mismatch sign in the detection of the IDH mutation was 88.9% and 86.7%, respectively. The T2-FLAIR mismatch sign may be an easy to use and helpful tool in recognizing IDH mutant patients, particularly if formal IDH testing is not available. We suggest that the adoption of a protocol based on imaging and histological data for optimal glioma characterization could be very helpful.

PI3K/mTOR inhibition of IDH1 mutant glioma leads to reduced 2HG production that is associated with increased survival

70-90% of low-grade gliomas and secondary glioblastomas are characterized by mutations in isocitrate dehydrogenase 1 (IDHmut). IDHmut produces the oncometabolite 2-hydroxyglutarate (2HG), which drives tumorigenesis in these tumors. The phosphoinositide-3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathway represents an attractive therapeutic target for IDHmut gliomas, but noninvasive indicators of drug target modulation are lacking. The goal of this study was therefore to identify magnetic resonance spectroscopy (MRS)-detectable metabolic biomarkers associated with IDHmut glioma response to the dual PI3K/(mTOR) inhibitor XL765. ¹H-MRS of two cell lines genetically modified to express IDHmut showed that XL765 induced a significant reduction in several intracellular metabolites including 2HG. Importantly, examination of an orthotopic IDHmut tumor model showed that enhanced animal survival following XL765 treatment was associated with a significant in vivo ¹H-MRS detectable reduction in 2HG but not with significant inhibition in tumor growth. Further validation is required, but our results indicate that 2HG could serve as a potential noninvasive MRS-detectable metabolic biomarker of IDHmut glioma response to PI3K/mTOR inhibition.

Advanced neuroimaging in the clinic: critical appraisal of the evidence base

The shortage of high-quality systematic reviews in the field of radiology limits evidence-based integration of imaging methods into clinical practice and may perpetuate misconceptions regarding the efficacy and appropriateness of imaging techniques for specific applications. Diffusion tensor imaging for patients with mild traumatic brain injury (DTI-mTBI) and dynamic susceptibility contrast MRI for patients with glioma (DSC-glioma) are applications of quantitative neuroimaging, which similarly detect manifestations of disease where conventional neuroimaging techniques cannot. We performed a critical appraisal of reviews, based on the current evidence-based medicine methodology, addressing the ability of DTI-mTBI and DSC-glioma to (a) detect brain abnormalities and/or (b) predict clinical outcomes. 23 reviews of DTI-mTBI and 26 reviews of DSC-glioma met criteria for inclusion. All reviews addressed detection of brain abnormalities, whereas 12 DTI-mTBI reviews and 22 DSC-glioma reviews addressed prediction of a clinical outcome. All reviews were assessed using a critical appraisal worksheet consisting of 19 yes/no questions. Reviews were graded according to the total number of positive responses and the 2011 Oxford Centre for evidence-based medicine levels of evidence criteria. Reviews addressing DTI-mTBI detection had moderate quality, while those addressing DSC-glioma were of low quality. Reviews addressing prediction of outcomes for both applications were of low quality. Five DTI-mTBI reviews, but only one review of DSC-glioma met criteria for classification as a meta-analysis/systematic/quantitative review.

Characterization of passive permeability at the blood-tumor barrier in five preclinical models of brain metastases of breast cancer

The blood-brain barrier (BBB) is compromised in brain metastases, allowing for enhanced drug permeation into brain. The extent and heterogeneity of BBB permeability in metastatic lesions is important when considering the administration of chemotherapeutics. Since permeability characteristics have been described in limited experimental models of brain metastases, we sought to define these changes in five brain-tropic breast cancer cell lines: MDA-MB-231BR (triple negative), MDA-MB-231BR-HER2, JIMT-1-BR3, 4T1-BR5 (murine), and SUM190 (inflammatory HER2 expressing). Permeability was assessed using quantitative autoradiography and fluorescence microscopy by co-administration of the tracers (14)C-aminoisobutyric acid (AIB) and Texas red conjugated dextran prior to euthanasia. Each experimental brain metastases model produced variably increased permeability to both tracers; additionally, the magnitude of heterogeneity was different among each model with the highest ranges observed in the SUM190 (up to 45-fold increase in AIB) and MDA-MB-231BR-HER2 (up to 33-fold in AIB) models while the lowest range was observed in the JIMT-1-BR3 (up to 5.5-fold in AIB) model. There was no strong correlation observed between lesion size and permeability in any of these preclinical models of brain metastases. Interestingly, the experimental models resulting in smaller mean metastases size resulted in shorter median survival while models producing larger lesions had longer median survival. These findings strengthen the evidence of heterogeneity in brain metastases of breast cancer by utilizing five unique experimental models and simultaneously emphasize the challenges of chemotherapeutic approaches to treat brain metastases.

The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline

QUESTION

What is the optimal imaging technique to be used in the diagnosis of a suspected low grade glioma, specifically: which anatomic imaging sequences are critical for most accurately identifying or diagnosing a low grade glioma (LGG) and do non-anatomic imaging methods and/or sequences add to the diagnostic specificity of suspected low grade gliomas?

TARGET POPULATION

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

RECOMMENDATION

LEVEL II

In patients with a suspected brain tumor, the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging. CRITICAL IMAGING FOR THE IDENTIFICATION AND DIAGNOSIS OF LOW GRADE GLIOMA:

LEVEL II

In patients with a suspected brain tumor, anatomic imaging sequences should include T1 and T2 weighted and Fluid Attenuation Inversion Recovery (FLAIR) MR sequences and will include T1 weighted imaging after the administration of gadolinium based contrast. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage, which may narrow the differential diagnosis. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention. IMPROVEMENT OF DIAGNOSTIC SPECIFICITY WITH THE ADDITION OF NON-ANATOMIC (PHYSIOLOGIC AND ADVANCED IMAGING) TO ANATOMIC IMAGING:

LEVEL II

Class II evidence from multiple studies and a significant number of Class III series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected LGGs, for the purposes of discriminating the potential for tumor subtypes and identification of suspicion of higher grade diagnoses.

LEVEL III

Multiple series offer Class III evidence to support the potential for magnetic resonance spectroscopy (MRS) and nuclear medicine methods including positron emission tomography and single-photon emission computed tomography imaging to offer additional diagnostic specificity although these are less well defined and their roles in clinical practice are still being defined.

QUESTION

Which imaging sequences or parameters best predict the biological behavior or prognosis for patients with LGG?

TARGET POPULATION

These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG.

RECOMMENDATION

Anatomic and advanced imaging methods and prognostic stratification

LEVEL III

Multiple series suggest a role for anatomic and advanced sequences to suggest prognostic stratification among low grade gliomas. Perfusion weighted imaging, particularly when obtained as a part of diagnostic evaluation (as recommended above) can play a role in consideration of prognosis. Other imaging sequences remain investigational in terms of their role in consideration of tumor prognosis as there is insufficient evidence to support more formal recommendations as to their use at this time.

QUESTION

What is the optimal imaging technique to be used in the follow-up of a suspected (or biopsy proven) LGG?

TARGET POPULATION

This recommendation applies to adults with a newly diagnosed low grade glioma.

RECOMMENDATIONS

LEVEL II

In patients with a diagnosis of LGG, anatomic imaging sequences should include T2/FLAIR MR sequences and T1 weighted imaging before and after the administration of gadolinium based contrast. Serial imaging should be performed to identify new areas of contrast enhancement or significant change in tumor size, which may signify transformation to a higher grade.

LEVEL III

Advanced imaging utility may depend on tumor subtype. Multicenter clinical trials with larger cohorts are needed. For astrocytic tumors, baseline and longitudinal elevations in tumor perfusion as assessed by dynamic susceptibility contrast perfusion MRI are associated with shorter time to tumor progression, but can be difficult to standardize in clinical practice. For oligodendrogliomas and mixed gliomas, MRS may be helpful for identification of progression.

Prognostic Value of MRS Metabolites in Postoperative Irradiated High Grade Gliomas

Purpose. We studied the prognostic significance of Magnetic Resonance Spectroscopy (MRS) in operated high grade gliomas. Materials and Methods. Twelve patients were treated with radiotherapy and Temozolomide. The MRS data were taken four weeks after operation (before radiotherapy) and every six months after the completion of RT. The N-acetyl aspartate, choline, creatine, and myo-inositol parameters were quantified, analyzed, and correlated to recurrence-free survival (RFS). Results. The median RFS was 26.06 months. RFS was significantly worse in elderly patients (P = 0.001) along with the higher choline/creatine ratios at either baseline (P = 0.003) or six months post Radiotherapy (P = 0.042). Median RFS was 23 months in high choline/creatine levels ≥2 at 6 months after radiotherapy and 11 months for those with <2 choline/creatine levels. There was a significant correlation of maximum difference of choline/creatine ratio with RFS (rho = 0.64, P = 0.045). Conclusion. Age and choline/creatine ratio are strong independent prognostic factors in high grade gliomas.

Resting state fMRI feature-based cerebral glioma grading by support vector machine

PURPOSE : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependent functional magnetic resonance imaging (RS-fMRI) in the region of glioma and use the extracted features for tumor grading. METHODS : Tumor segmentation was performed with both conventional MRI and RS-fMRI. Four typical parameters, signal intensity difference ratio, signal intensity correlation (SIC), fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), were defined to analyze tumor regions. Mann-Whitney [Formula: see text] test was employed to identify statistical difference of these four parameters between low-grade glioma (LGG) and high-grade glioma (HGG), respectively. Support vector machine (SVM) was employed to assess the diagnostic contributions of these parameters. RESULTS : Compared with LGG, HGG had more complex anatomical morphology and BOLD-fMRI features in the tumor region. SIC [Formula: see text], fALFF ([Formula: see text]) and ReHo ([Formula: see text]) were selected as features for classification according to the test [Formula: see text] value. The accuracy, sensitivity and specificity of SVM classification were better than 80, where SIC had the best classification accuracy (89). CONCLUSION : Parameters of RS-fMRI are effective to classify the tumor grade in glioma cases. The results indicate that this technique has clinical potential to serve as a complementary diagnostic tool.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data

INTRODUCTION

A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification.

METHODS

MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data.

RESULTS

The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible.

CONCLUSIONS

FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

Oedema-based model for diffuse low-grade gliomas: application to clinical cases under radiotherapy

OBJECTIVES

Diffuse low-grade gliomas are characterized by slow growth. Despite appropriate treatment, they change inexorably into more aggressive forms, jeopardizing the patient's life. Optimizing treatments, for example with the use of mathematical modelling, could help to prevent tumour regrowth and anaplastic transformation. Here, we present a model of the effect of radiotherapy on such tumours. Our objective is to explain observed delay of tumour regrowth following radiotherapy and to predict its duration.

MATERIALS AND METHODS

We have used a migration-proliferation model complemented by an equation describing appearance and draining of oedema. The model has been applied to clinical data of tumour radius over time, for a population of 28 patients.

RESULTS

We were able to show that draining of oedema accounts for regrowth delay after radiotherapy and have been able to fit the clinical data in a robust way. The model predicts strong correlation between high proliferation coefficient and low progression-free gain of lifetime, due to radiotherapy among the patients, in agreement with clinical studies. We argue that, with reasonable assumptions, it is possible to predict (precision ~20%) regrowth delay after radiotherapy and the gain of lifetime due to radiotherapy.

CONCLUSIONS

Our oedema-based model provides an early estimation of individual duration of tumour response to radiotherapy and thus, opens the door to the possibility of personalized medicine.

The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

Manganese-enhanced MRI of minimally gadolinium-enhancing breast tumors

PURPOSE

To investigate the potential of manganese (Mn)-enhanced MRI for sensitive detection and delineation of tumors that demonstrate little enhancement on Gd-DTPA.

MATERIALS AND METHODS

Eighteen nude rats bearing 1 to 2 cm in diameter orthotopic breast tumors (ZR75 and LM2) were imaged on a 3 Tesla (T) clinical scanner. Gd-DTPA was administered intravenously and MnCl2 subcutaneously, both at 0.05 mmol/kg. T1 -weighted imaging and T1 measurements were performed precontrast, 10 min post-Gd-DTPA, and 24 h post-MnCl2 . Tumors were excised and histologically assessed using H&E (composition and necrosis) and CD34 (vascularity).

RESULTS

Most tumors (78%) demonstrated little enhancement (< 20% change in R1 ) on Gd-DTPA. MnCl2 administration achieved greater and more uniform enhancement throughout the tumor mass (i.e., not restricted to the tumor periphery), with R1 changing over 20% in 72% of tumors. MnCl2 -induced R1 changes compared with Gd-induced changes were significantly greater in both ZR75 (P < 0.01) and LM2 tumors (P < 0.05). Histology confirmed very low vascularity in both tumor models, and necrotic areas were well delineated only on Mn-enhanced MRI.

CONCLUSION

Mn-enhanced MRI is a promising approach for detection of low-Gd-enhancing tumors.

MRI in the diagnosis and management of epileptomas

High-resolution magnetic resonance imaging (MRI) is invaluable for identifying cerebral tumors that cause epilepsy. Serial voxel-based automated quantitative analyses are more sensitive than visual reading for detecting change in a lesion. Eloquent cortex can be identified with functional MRI (fMRI), with cautions about the precise location and extent of critical cortex. Tractography is useful for delineating critical white matter tracks as are MR venography and computerized tomography (CT) angiography for displaying veins and arteries. These data may be combined into a three-dimensional (3D) multimodal MR data presentation and displayed interoperatively to increase the precision and minimize the risk of neurosurgical treatment, and for the illustrations.

Correlation between cerebral blood volume measurements by perfusion-weighted magnetic resonance imaging and two-year progression-free survival in gliomas

Our goal was to determine whether relative cerebral blood volume (rCBV) can serve as an adjunct to histopathologic grading in the assessment of gliomas, with the hypothesis that rCBV can predict two-year survival. We evaluated 29 newly diagnosed gliomas (13 WHO grade II, seven grade III, nine grade IV; 17 astrocytomas, 12 oligodendroglial tumors). Dynamic susceptibility-weighted contrast-enhanced perfusion MR images and CBV maps were obtained. rCBVmax measurements (maximum tumor CBV/contralateral normal tissue CBV) and progression-free survival (PFS) were recorded. Receiver operating characteristic curves and Kaplan-Meier survival curves were calculated for rCBVmax and histologic grade. rCBVmax measurements differed between gliomas without (2.38 +/- 1.22) and with progression (5.57 +/- 2.84) over two years. The optimal rCBVmax cut-off value to predict progression was 2.95. rCBVmax < 2.95 was a significant predictor of two-year PFS, almost as accurate as WHO grade II. In the pure astrocytoma subgroup, the optimal rCBVmax cut-off value to predict progression was 2.85. In this group rCBVmax < 2.85 was a significant predictor of two-year PFS, an even better predictor of two-year PFS than WHO grade II. rCBVmax can be used to predict two-year PFS in patients with gliomas, independent of pathologic findings, especially in tumors without oligodendroglial components.

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

The aim of this study was to evaluate the contribution of diffusion and perfusion MR metrics in the discrimination of intracranial brain lesions at 3T MRI, and to investigate the potential diagnostic and predictive value that pattern recognition techniques may provide in tumor characterization using these metrics as classification features. Conventional MRI, diffusion weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic-susceptibility contrast imaging (DSCI) were performed on 115 patients with newly diagnosed intracranial tumors (low-and- high grade gliomas, meningiomas, solitary metastases). The Mann-Whitney U test was employed in order to identify statistical differences of the diffusion and perfusion parameters for different tumor comparisons in the intra-and peritumoral region. To assess the diagnostic contribution of these parameters, two different methods were used; the commonly used receiver operating characteristic (ROC) analysis and the more sophisticated SVM classification, and accuracy, sensitivity and specificity levels were obtained for both cases. The combination of all metrics provided the optimum diagnostic outcome. The highest predictive outcome was obtained using the SVM classification, although ROC analysis yielded high accuracies as well. It is evident that DWI/DTI and DSCI are useful techniques for tumor grading. Nevertheless, cellularity and vascularity are factors closely correlated in a non-linear way and thus difficult to evaluate and interpret through conventional methods of analysis. Hence, the combination of diffusion and perfusion metrics into a sophisticated classification scheme may provide the optimum diagnostic outcome. In conclusion, machine learning techniques may be used as an adjunctive diagnostic tool, which can be implemented into the clinical routine to optimize decision making.

Evaluation of quantitative criteria for glioma grading with static and dynamic 18F-FDopa PET/CT

PURPOSE

The aim of this study was to compare various acquisition and processing protocols for noninvasive glioma grading using either static or dynamic (18)F-FDopa PET.

METHODS

Dynamic studies were performed in 33 patients. Based on histopathological analysis, 18 patients had a high-grade (HG) tumor and 15 patients had a low-grade (LG) tumor. For static imaging, SUV(mean) and SUV(max) were calculated for different acquisition time ranges after injection. For dynamic imaging, the transport rate constant k1 was calculated according to a compartmental kinetic analysis using an image-derived input function.

RESULTS

With the use of a 5-minute static imaging protocol starting at 38 minutes after injection, newly diagnosed HG tumors could be distinguished from LG tumors with a sensitivity of 70% and a specificity of 90% with a threshold of SUV(mean) of 2.5. In recurrent tumors, a sensitivity of 100% and a specificity of 80% for identifying HG tumors were obtained with a threshold set to 1.8. Dynamic imaging only slightly, but nonsignificantly, improved differential diagnosis.

CONCLUSIONS

Static and dynamic imaging without blood sampling can discriminate between LG and HG for both newly diagnosed and recurrent gliomas. In dynamic imaging, excellent discrimination was obtained by considering the transport rate constant k1 of tumors. In static imaging, the best discrimination based on SUV was obtained for SUV(mean) calculated from a 5-minute acquisition started at 38 minutes after injection.

A Molecular Predictor Reassesses Classification of Human Grade II/III Gliomas

Diffuse gliomas are incurable brain tumors divided in 3 WHO grades (II; III; IV) based on histological criteria. Grade II/III gliomas are clinically very heterogeneous and their prognosis somewhat unpredictable, preventing definition of appropriate treatment. On a cohort of 65 grade II/III glioma patients, a QPCR-based approach allowed selection of a biologically relevant gene list from which a gene signature significantly correlated to overall survival was extracted. This signature clustered the training cohort into two classes of low and high risk of progression and death, and similarly clustered two external independent test cohorts of 104 and 73 grade II/III patients. A 22-gene class predictor of the training clusters optimally distinguished poor from good prognosis patients (median survival of 13–20 months versus over 6 years) in the validation cohorts. This classification was stronger at predicting outcome than the WHO grade II/III classification (P≤2.8E-10 versus 0.018). When compared to other prognosis factors (histological subtype and genetic abnormalities) in a multivariate analysis, the 22-gene predictor remained significantly associated with overall survival. Early prediction of high risk patients (3% of WHO grade II), and low risk patients (29% of WHO grade III) in clinical routine will allow the development of more appropriate follow-up and treatments.

Quantitative characterization of the imaging limits of diffuse low-grade oligodendrogliomas

BACKGROUND

Supratentorial diffuse low-grade gliomas in adults extend beyond maximal visible MRI-defined abnormalities, and a gap exists between the imaging signal changes and the actual tumor margins. Direct quantitative comparisons between imaging and histological analyses are lacking to date. However, they are of the utmost importance if one wishes to develop realistic models for diffuse glioma growth.

METHODS

In this study, we quantitatively compared the cell concentration and the edema fraction from human histological biopsy samples (BSs) performed inside and outside imaging abnormalities during serial imaging-based stereotactic biopsy of diffuse low-grade gliomas.

RESULTS

The cell concentration was significantly higher in BSs located inside (1189 ± 378 cell/mm(2)) than outside (740 ± 124 cell/mm(2)) MRI-defined abnormalities (P = .0003). The edema fraction was significantly higher in BSs located inside (mean, 45% ± 23%) than outside (mean, 5 %± 9%) MRI-defined abnormalities (P < .0001). At borders of the MRI-defined abnormalities, 20% of the tissue surface area was occupied by edema and only 3% by tumor cells. The cycling cell concentration was significantly higher in BSs located inside (10 ± 12 cell/mm(2)), compared with outside (0.5 ± 0.9 cell/mm(2)), MRI-defined abnormalities (P = .0001).

CONCLUSIONS

We showed that the margins of T2-weighted signal changes are mainly correlated with the edema fraction. In 62.5% of patients, the cycling tumor cell fraction (defined as the ratio of the cycling tumor cell concentration to the total number of tumor cells) was higher at the limits of the MRI-defined abnormalities than closer to the center of the tumor. In the remaining patients, the cycling tumor cell fraction increased towards the center of the tumor.

Usefulness of FDG, MET and FLT-PET studies for the management of human gliomas

The use of positron imaging agents such as FDG, MET, and FLT is expected to lead the way for novel applications toward efficient malignancy grading and treatment of gliomas. In this study, the usefulness of FDG, MET and FLT-PET images was retrospectively reviewed by comparing their histopathological findings. FDG, MET, and FLT-PET were performed in 27 patients with WHO grade IV, 15 patients with WHO grade III, and 12 patients with WHO grade II during 5.5 years. The resulting PET images were compared by measuring SUVs and T/N ratios (tumor to normal tissue ratios). Although there were no significant differences in FDG-PET, there were significant differences in the T/N ratios in the MET-PET between WHO grades II and IV and in the FLT-PET between the WHO grades III and IV. In glioblastoma patients, the SUVs of the areas depicted by MRI in the MET-PET were different from those SUVs in the FLT-PET. Importantly, the areas with high SUVs in both MET-PET and FLT-PET were also high in Ki-67 index and were histologically highly malignant. PET imaging is a noninvasive modality that is useful in determining a tumor area for removal as well as improving preoperative diagnosis for gliomas.

Potential role for magnetoencephalography in distinguishing low- and high-grade gliomas: a preliminary study with histopathological confirmation

Gliomas are the most common form of tumor in the CNS and are exceptionally heterogeneous. Accurately characterizing gliomas, in terms of grade and type, is essential for predicting the rate of tumor progression. Histopathological grading and analysis based on biopsied tissue remains the gold standard, but non- and semi-invasive neuroimaging also plays a key role. Neuroimaging has been used to guide and optimize biopsies for several decades, but more recently molecular imaging and variants of MRI have shown promise in independently predicting glioma grade. Here we evaluated whether magnetoencephalographic (MEG) measurements of population-level physiology within the glioma space were predictive of the inherent grade of the tissue, based on definitive histopathological analyses. High-density MEG data were recorded from 11 patients who were undergoing functional mapping in preparation for resective surgery. The primary results indicated that glioma grade was positively correlated with the local amplitude of activity within the glioma space in the theta (4-7 Hz), alpha (8-14 Hz), and beta bands (14-30 Hz). Additionally, activity within the glioma was significantly elevated relative to the nonaffected homologue area in the same frequency bands. These results indicate that pathological levels of synchronization exist within the tumor space and that MEG may be a viable tool for noninvasively differentiating gliomas by their grade. Although these results should be considered preliminary and are only correlative in nature, these data suggest that MEG can potentially detect neurophysiological signatures or markers that predict the inherent grade of a glial tumor.