Differentiation Of High-grade Gliomas Research Articles

A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors

Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for “cancer of unknown primary”, where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.

A glutamatergic biomarker panel enables differentiating Grade 4 gliomas/astrocytomas from brain metastases.

The differentiation of high-grade glioma and brain tumors of an extracranial origin is eminent for the decision on subsequent treatment regimens. While in high-grade glioma, a surgical resection of the tumor mass is a fundamental part of current standard regimens, in brain metastasis, the burden of the primary tumor must be considered. However, without a cancer history, the differentiation remains challenging in the imaging. Hence, biopsies are common that may help to identify the tumor origin. An additional tool to support the differentiation may be of great help. For this purpose, we aimed to identify a biomarker panel based on the expression analysis of a small sample of tissue to support the pathological analysis of surgery resection specimens. Given that an aberrant glutamate signaling was identified to drive glioblastoma progression, we focused on glutamate receptors and key players of glutamate homeostasis. Based on surgically resected samples from 55 brain tumors, the expression of ionotropic and metabotropic glutamate receptors and key players of glutamate homeostasis were analyzed by RT-PCR. Subsequently, a receiver operating characteristic (ROC) analysis was performed to identify genes whose expression levels may be associated with either glioblastoma or brain metastasis. Out of a total of 29 glutamatergic genes analyzed, nine genes presented a significantly different expression level between high-grade gliomas and brain metastases. Of those, seven were identified as potential biomarker candidates including genes encoding for AMPA receptors GRIA1, GRIA2, kainate receptors GRIK1 and GRIK4, metabotropic receptor GRM3, transaminase BCAT1 and the glutamine synthetase (encoded by GLUL). Overall, the biomarker panel achieved an accuracy of 88% (95% CI: 87.1, 90.8) in predicting the tumor entity. Gene expression data, however, could not discriminate between patients with seizures from those without. We have identified a panel of seven genes whose expression may serve as a biomarker panel to discriminate glioblastomas and brain metastases at the molecular level. After further validation, our biomarker signatures could be of great use in the decision making on subsequent treatment regimens after diagnosis.

Diagnostic Value/Accuracy of Diffusion-weighted Magnetic Resonance Imaging in Low-grade Glioma: Systematic Review

Tumor grading plays a decisive role in glioma classification, which impacts the treatment strategy and prognosis of the patient. Diffusion weighted imaging (DWI) technique is a noninvasive technique used in tumor grading. This study explores whether DWI can be effectively used in tumor grading and the identification of low-grade glioma (LGG) by analyzing apparent diffusion coefficient (ADC) values. Publicly available English databases, PubMed and Google Scholar, were queried from 2015 to 2021. We mined a total of 465 research documents; of which nine articles were selected based on the inclusion and exclusion criteria. The overall findings show cutoff threshold min ADC values with higher b value playing a significant role in the differentiation of high-grade gliomas and LGGs, tumor grading, and determining tumor progression and stability. This systematic review suggests the development of unified DWI imaging parameters with better sensitivity and specificity for grading LGG tumors.

Differentiation of high grade glioma and solitary brain metastases by measuring relative cerebral blood volume and fractional anisotropy: a systematic review and meta-analysis of MRI diagnostic test accuracy studies.

This study aims to research the efficacy of MRI (I) for differentiating high-grade glioma (HGG) (P) with solitary brain metastasis (SBM) (C) by creating a combination of relative cerebral blood volume (rCBV) (O) and fractional anisotropy (FA) (O) in patients with intracerebral tumors. Searches were conducted on September 2021 with no publication date restriction, using an electronic search for related articles published in English, from PubMed (1994 to September 2021), Scopus (1977 to September 2021), Web of Science (1985 to September 2021), and Cochrane (1997 to September 2021). A total of 1056 studies were found, with 23 used for qualitative and quantitative data synthesis. Inclusion criteria were: patients diagnosed with HGG and SBM without age, sex, or race restriction; MRI examination of rCBV and FA; reliable histopathological diagnostic method as the gold-standard for all conditions of interest; observational and clinical studies. Newcastle-Ottawa quality assessment Scale (NOS) and Cochrane risk of bias tool (ROB) for observational and clinical trial studies were managed to appraise the quality of individual studies included. Data extraction results were managed using Mendeley and Excel, pooling data synthesis was completed using the Review Manager 5.4 software with random effect model to discriminate HGG and SBM, and divided into four subgroups. There were 23 studies included with a total sample size of 597 HGG patients and 373 control groups/SBM. The analysis was categorized into four subgroups: (1) the subgroup with rCBV values in the central area of the tumor/intratumoral (399 HGG and 232 SBM) shows that HGG patients are not significantly different from SBM/controls group (SMD [95% CI] = -0.27 [-0.66, 0.13]), 2) the subgroup with rCBV values in the peritumoral area (452 HGG and 274 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = -1.23 [-1.45 to -1.01]), (3) the subgroup with FA values in the central area of the tumor (249 HGG and 156 SBM) shows that HGG patients are significantly higher than SBM (SMD [95% CI] = - 0.44 [-0.84,-0.04]), furthermore (4) the subgroup with FA values in the peritumoral area (261 HGG and 168 SBM) shows that the HGG patients are significantly higher than the SBM (SMD [95% CI] = -0.59 [-1.02,-0.16]). Combining rCBV and FA measurements in the peritumoral region and FA in the intratumoral region increase the accuracy of MRI examination to differentiate between HGG and SBM patients effectively. Confidence in the accuracy of our results may be influenced by major interstudy heterogeneity. Whereas the I2 for the rCBV in the intratumoral subgroup was 80%, I2 for the rCBV in the peritumoral subgroup was 39%, and I2 for the FA in the intratumoral subgroup was 69%, and I2 for the FA in the peritumoral subgroup was 74%. The predefined accurate search criteria, and precise selection and evaluation of methodological quality for included studies, strengthen this studyOur study has no funder, no conflict of interest, and followed an established PROSPERO protocol (ID: CRD42021279106). The combination of rCBV and FA measurements' results is promising in differentiating HGG and SBM.

Differentiation of high-grade glioma and primary central nervous system lymphoma: Multiparametric imaging of the enhancing tumor and peritumoral regions based on hybrid 18F-FDG PET/MRI

ObjectivesTo investigate the value of the 18F-FDG PET/MRI multiparametric model in the differentiation of high-grade glioma (HGG) and primary central nervous system lymphoma (PCNSL), with emphasis on the quantitative analysis of the enhancing tumor (ET) and non-enhancing peritumoral region (PTR). MethodsForty-five patients with HGG and 20 patients with PCNSL who underwent simultaneous 18F-FDG PET, arterial spin labelling perfusion-weighted imaging and diffusion-weighted imaging with hybrid PET/MRI before treatment were retrospectively enrolled. The relative maximum standardized uptake value (rSUVmax), relative maximum cerebral blood flow (rCBFmax) and relative minimum apparent diffusion coefficient (rADCmin) in both the ET and NPR were calculated and compared between HGG and PCNSL. Multivariate logistic regression was used to determine the best logistic regression model (LRM) for classification. Receiver operating curve analysis was used to assess diagnostic performance. ResultsIn the ET, HGG showed significantly lower rSUVmax values but higher rCBFmax and rADCmin than PCNSL (all P < 0.05). In the PTR, HGG demonstrated significantly higher rSUVmax and rCBFmax but lower rADCmin than PCNSL (all P < 0.05). Multivariate logistic regression based on quantitative parameters revealed that the LRM consisting of rSUVmax_ET, rADCmin_ET and rCBFmax_PTR had significantly improved diagnostic performance in differentiating HGG from PCNSL than single parameter alone, with an AUC of 0.980 and an accuracy of 95.4%. Multivariate logistic regression incorporating quantitative parameters and conventional MRI features revealed that the LRM consisting of rSUVmax_ET, rCBFmax_PTR and enhancement pattern yielded a slightly higher AUC of 0.989 and an identical accuracy of 95.4%. No significant difference in AUCs was detected between the two LRMs (P = 0.233). ConclusionsMultiparametric 18F-FDG PET/MRI diagnostic model based on conventional MRI features and quantitative analysis of the enhancing tumors and peritumoral regions is superior to single parameter in the differentiation of HGG and PCNSL, which should be considered in the clinical practice.

Multiparametric differentiation of intracranial central nervous system lymphoma and high-grade glioma using diffusion-, perfusion-, susceptibility-weighted magnetic resonance imaging, and spectroscopy

Aims and Objectives: To observe the characteristics of primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) in diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), susceptibility-weighted imaging (SWI) and spectroscopy, and differentiate them based on these parameters. Materials and Methods: A total of 45 patients diagnosed with the central nervous system (CNS) neoplasm on magnetic resonance imaging (MRI) using 1.5 Tesla MRI Siemens Magnetom Avanto (Siemens, Germany) and with subsequent histopathological evidence as glioblastoma or CNS lymphoma were included. The study was completed over a period of 2 years. Results: It was found that DWI is effective in the differentiation of HGGs and PCNSLs. A total of 20 (57.1%) HGGs showed diffusion restriction, whereas 9 (90%) of the PCNSLs showed diffusion restriction. The mean apparent diffusion coefficient (ADC) (×10–6 mm2/s) in PCNSLs was 646 whereas, in HGGs, it was found to be 824.3. Thirty-one (88.6%) of the HGGs showed increased perfusion. The mean value of rCBVmean in HGG was found to be 4.06 and the mean value of rCBVmax was 3.63. None of the PCNSLs showed increased perfusion. The mean value of rCBVmean in PCNSLs was 0.097 and rCBVmax was 0.133. 30 (85.7%) of HGGs showed significant areas of blooming on SWI (in the form of intratumoral susceptibility signals [ITSS]). None of the PCNSLs showed blooming. All HGGs and PCNSLs showed increased choline and decreased N acetyl aspartate (NAA) on spectroscopy. Mean Choline/Creatine (Cho/Cr) in HGGs was found to be 3.06, whereas in PCNSLs, it was 1.84. Conclusion: It is important to make a distinction between HGG and PCNSL as the treatment modalities are different for both. Multiparametric evaluation of ADC, ITSS, and rCBVmean allows the differentiation of PCNSLs and solid glioblastoma which supports the integration of advanced MR imaging techniques including DSC-PWI, DWI, and SWI for the routine diagnostic workup of these tumors.

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

With conventional MRI, it is often difficult to effectively differentiate between contrast-enhancing brain tumors, including primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and metastasis. This study aimed to assess the discrimination ability of the parameters obtained from DWI and the percentage signal recovery- (PSR-) optimized protocol of DSC-MRI between these three tumor types at an initial step. DSC-MRI using a PSR-optimized protocol (TR/TE = 1500/30 ms, flip angle = 90°, no preload) and DWI of 99 solitary enhancing tumors (60 HGGs, 24 metastases, 15 PCNSLs) were retrospectively assessed before treatment. rCBV, PSR, ADC in the tumor core and rCBV, and ADC in peritumoral edema were measured. The differences were evaluated using one-way ANOVA, and the diagnostic performance was evaluated using ROC curve analysis. PSR in the tumor core showed the best discriminating performance in differentiating these three tumor types with AUC values of 0.979 for PCNSL vs. others and 0.947 for HGG vs. metastasis. The ADC was only helpful in the tumor core and distinguishing PCNSLs from others (AUC = 0.897). Different from CBV-optimized protocols (preload, intermediate FA), PSR derived from the PSR-optimized protocol seems to be the most important parameter in the differentiation of HGGs, metastases, and PCNSLs at initial diagnosis. This property makes PSR remarkable and carries the need for comprehensive DSC-MRI protocols, which provides PSR sensitivity and CBV accuracy together, such as the preload use of the PSR-optimized protocol before the CBV-optimized protocol.

Multiparametric MRI in differentiating solitary brain metastasis from high-grade glioma: diagnostic value of the combined use of diffusion-weighted imaging, dynamic susceptibility contrast imaging, and magnetic resonance spectroscopy parameters.

The purpose of this study was to determine whether the combined use of diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic susceptibility contrast imaging (DSCI) parameters could provide a more accurate diagnosis in the differentiation of high-grade glioma (HGG) from solitary brain metastasis (SBM) in the enhancing tumour and in the peritumoural region. Fifty-six patients who received DWI, DSCI, and MRS before surgery were assessed. In differentiating SBM from HGG, the cutoff values of the DWI-apparent diffusion coefficient (ADCmin, ADCmax, and ADCmean), DSCI-relative cerebral blood volume (rCBV), and MRS-Cho/Cr, Cho/NAA, and NAA/Cr parameters for the peritumoural region were determined with ROC. The combined ROC curve was used for the different combinations of the peritumoural region DWI, DSCI, and MRS parameters in differentiating between the two tumours, and the best model combination was formed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. This study was approved by the Institutional Review Board at our institutes. In the enhancing tumour, all the parameters except NAA/Cr (P = 0.024) exhibited no statistical difference in differentiating between these two groups (P > 0.05). AUC values for ADCmin, ADCmax, ADCmean, rADCmin, rADCmax, rADCmean, rCBV, Cho/Cr, Cho/NAA, and NAA/Cr parameters in the peritumoural region in differentiating SBM from HGG were 0.860, 0.822, 0.848, 0.822, 0.801, 0.822, 0.906, 0.851, 0.903, and 0.784, respectively. In differentiating HGG from SBM, the best model consisted of the combination of peritumoural ADCmin, rCBV, and Cho/NAA parameters. AUC values were 0.970. The combination of peritumoural region ADCmin, rCBV, and Cho/NAA parameters can help in differentiating SBM from HGG, with a diagnostic accuracy of 97%.

Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography

A reliable, noninvasive method to differentiate high-grade glioma (HGG) and intracranial metastasis (IM) has remained elusive. The aim of this study was to differentiate between HGG and IM using tumoral and peritumoral diffusion tensor imaging characteristics. A semiautomated script generated volumetric regions of interest (ROIs) for the tumor and a peritumoral shell at a predetermined voxel thickness. ROI differences in diffusion tensor imaging-related metrics between HGG and IM groups were estimated, including fractional anisotropy, mean diffusivity, total fiber tract counts, and tract density. The HGG group (n= 46) had a significantly higher tumor-to-brain volume ratio than the IM group (n=35) (P < 0.001). The HGG group exhibited significantly higher mean fractional anisotropy and significantly lower mean diffusivity within peritumoral ROI than the IM group (P < 0.05). The HGG group exhibited significantly higher total tract count and higher tract density in tumoral and peritumoral ROIs than the IM group (P < 0.05). Tumoral tract count and peritumoral tract density were the most optimal metrics to differentiate the groups based on receiver operating characteristic curve analysis. Predictive analysis using receiver operating characteristic curve thresholds was performed on 13 additional participants. Compared with correct clinical diagnoses, the 2 thresholds exhibited equal specificities (66.7%), but the tumoral tract count (85.7%) seemed more sensitive in differentiating the 2 groups. Tract count and tract density were significantly different in tumoral and peritumoral regions between HGG and IM. Differences in microenvironmental interactions between the tumor types may cause these tract differences.

Differentiation of High-grade Gliomas from Brain Metastases Using Tissue Similarity Maps (TSMs) Based Relative Cerebral Blood Volume Values

Background: Perfusion Weighted Imaging (PWI) has been used to differentiate the solitary metastases from high-grade glioma. However, the CBV value obtained from classical algorithm can be affected by a number of factors. Tissue Similarity Map (TSM) is a new algorithm depending solely on the signal intensity time course but does not require the use of Concentration Time Curve (CTC), which is different from the classical rCBV algorithm. Objective: The purpose of this study is to investigate the diagnostic utility of TSM based rCBV values in differentiating between high-grade gliomas and solitary metastases. Methods: The preoperative MR PWI studies of 30 patients with a solitary cerebral neoplasm were retrospectively analyzed. All PWI data were calculated for TSM algorithm with MATLAB 7.6 (Math Works, Natick, MA). The TSM based rCBV (rCBVTSM) were measured in the parenchyma of tumor and peritumoral regions. All specimens were histopathologically categorized as high-grade gliomas or metastases and were correlated with corresponding rCBVTSM values. Results: All PWI data could be post-processed by using the TSM post-processing software. The differences in the rCBVTSM-T between high-grade gliomas and metastases were not statistically significant (p=0.299). While the rCBVTSM-P of high-grade gliomas was higher than that of metastases (p=0.040). ROC curve analysis showed significant AUC of rCBVTSM-P for differentiating highgrade gliomas from metastases (p=0.020). The rCBVTSM-P threshold of 1.725 was found to be a significant cutoff value for high-grade glioma/metastases prediction with 0.619 sensitivity and 0.889 specificity. Conclusion: The rCBVTSM-P derived from MR PWI may be helpful in differentiating high-grade gliomas from solitary metastases. Keywords: TSM, perfusion imaging, cerebral blood volume, glioma, neoplasms metastasis, differentiation diagnosis.

Correlation of 18F-fluoroethyl tyrosine positron-emission tomography uptake values and histomorphological findings by stereotactic serial biopsy in newly diagnosed brain tumors using a refined software tool

BackgroundMagnetic resonance imaging (MRI) is the standard neuroimaging method to diagnose neoplastic brain lesions, as well as to perform stereotactic biopsy surgical planning. MRI has the advantage of providing structural anatomical details with high sensitivity, though histological specificity is limited. Although combining MRI with other imaging modalities, such as positron-emission tomography (PET), has proven to increment specificity, exact correlation between PET threshold uptake ratios (URs) and histological diagnosis and grading has not yet been described.ObjectivesThe aim of this study was to correlate exactly the histopathological criteria of the biopsy site to its PET uptake value with high spatial resolution (mm3), and to analyze the diagnostic value of PET using the amino acid O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) PET in patients with newly diagnosed brain lesions in comparison to histological findings obtained from stereotactic serial biopsy.Patients and methodsA total of 23 adult patients with newly diagnosed brain tumors on MRI were enrolled in this study. Subsequently to diagnoses, all patients underwent a 18F-FET PET-guided stereotactic biopsy, using an original newly developed software module, which is presented here. Conventional MRI, stereotactic computed tomography series, and 18F-FET PET images were semiautomatically fused, and hot-spot detection was performed for target planning. UR was determined using the uptake value from the biopsy sites in relation to the contralateral frontal white matter. UR values ≥1.6 were considered positive for glioma. High-grade glioma (HGG) was suspected with URs ≥3.0, while low-grade glioma (LGG) was suspected with URs between 1.6 and 3.0. Stereotactic serial biopsies along the trajectory at multiple sites were performed in millimeter steps, and the FET URs for each site were correlated exactly with a panel of 27 different histopathological markers. Comparisons between FET URs along the biopsy trajectories and the histological diagnoses were made with Pearson product-moment correlation coefficients. Analysis of variance was performed to test for significant differences in maximum UR between different tumor grades.ResultsA total of 363 biopsy specimens were taken from 23 patients by stereotactic serial biopsies. Histological examination revealed eight patients (35%) with an LGG: one with a World Health Organization (WHO)-I lesion and seven with a WHO-II lesion. Thirteen (57%) patients revealed an HGG (two with a WHO-III and three with a WHO-IV tumor), and two patients (9%) showed a process that was neither HGG nor LGG (group X or no-grade group). The correlation matrix between histological findings and the UR revealed five strong correlations. Low cell density in tissue samples was found to have a significant negative correlation with the measured cortical uptake rate (r=−0.43, P=0.02), as well as moderate cell density (r=−0.48, P=0.02). Pathological patterns of proliferation (r=0.37, P=0.04), GFAP (r=0.37, P=0.04), and Olig2 (r=0.36, P=0.05) showed a significant positive correlation with cortical URs. Analysis of variance tests showed a significant difference between the LGG and the HGG groups (F=8.27, P<0.002), but no significant differences when differentiating between the X group and the HGG (P=0.2)/LGG (P=0.8) groups, nor between the no-grade group and the WHO-I group.Conclusion18F-FET PET is a valuable tool, as it allows the differentiation of HGGs from LGGs. Its use is not limited to preoperative evaluation; it may also refine biopsy targeting and improve tumor delimitation for radiotherapy. Histology is still necessary, and remains the gold standard for definitive diagnosis of brain lesions.

Dynamic CT perfusion imaging of intra-axial brain tumours: differentiation of high-grade gliomas from primary CNS lymphomas

Introduction:Perfusion computed tomography (PCT) allows to quantitatively assess haemodynamic characteristics of brain tissue. We investigated if different brain tumor types can be distinguished from each other using Patlak analysis of PCT data.Methods:PCT data from 43 patients with brain tumours were analysed with a commercial implementation of the Patlak method. Four patients had low-grade glioma (WHO II), 31 patients had glioblastoma (WHO IV) and eight patients had intracerebral lymphoma. Tumour regions of interest (ROIs) were drawn in a morphological image and automatically transferred to maps of cerebral blood flow (CBF), cerebral blood volume (CBV) and permeability (K Trans). Mean values were calculated, group differences were tested using Wilcoxon and Mann Whitney U-tests.Results:In comparison with normal parenchyma, low-grade gliomas showed no significant difference of perfusion parameters (p > 0.05) , whereas high-grade gliomas demonstrated significantly higher values (p < 0.0001 for K Trans, p < 0.0001 for CBV and p = 0.0002 for CBF). Lymphomas displayed significantly increased mean KTrans values compared with unaffected cerebral parenchyma (p = 0.0078) but no elevation of CBV. High-grade gliomas show significant higher CBV values than lymphomas (p = 0.0078).Discussion:PCT allows to reliably classify gliomas and lymphomas based on quantitative measurements of CBV and K Trans.

Bmi1 deficient neural stem cells have increased Integrin dependent adhesion to self-secreted matrix

Background Neural cells deficient for Polycomb group (PcG) protein Bmi1 are impaired in the formation and differentiation of high grade glioma, an incurable cancer of the brain. It was shown that mechanisms involved in cell adhesion and migration were specifically affected in these tumors. Methods Using biochemical and cell biological approaches, we investigated the adhesive capacities of Bmi1;Ink4a/Arf deficient primary neural stem cells (NSCs). Results Bmi1;Ink4a/Arf deficient NSCs have altered expression of Collagen-related genes, secrete increased amounts of extracellular matrix, and exhibit enhanced cell–matrix binding through the Beta-1 Integrin receptor. These traits are independent from the well described role of Bmi1 as repressor of the Ink4a/Arf tumor suppressor locus. Conclusion In addition to proliferative processes, Bmi1 controls the adhesive capacities of primary NSCs by modulating extracellular matrix secretion. General significance Since PcG protein Bmi1 is important for both normal development and tumorigenesis, it is vital to understand the complete network in which this protein acts. Whereas it is clear that control of Ink4a/Arf is a major Bmi1 function, there is evidence that other downstream mechanisms exist. Hence, our novel finding that Bmi1 also governs cell adhesion significantly contributes to our understanding of the PcG proteins.

In vivo single-voxel proton MR spectroscopy in the differentiation of high-grade gliomas and solitary metastases

AIM: To determine whether single-voxel proton magnetic resonance spectroscopy (1HMRS) could be used to differentiate gliomas from metastases on the basis of differences in metabolite levels in the different involved regions. MATERIALS AND METHODS: Twenty-two patients (age range from 32 to 62 years, with a median age of 46.7 years) with a solitary brain tumour (14 gliomas, eight metastases) underwent conventional, gadolinium-DTPA enhanced T1-weighted images, and 1HMRS before surgical resection. Spectra from the enhancing tumour, the peritumoural region, and normal brain were obtained from 1HMRS. A point resolved spectroscopy sequence was required for 1HMRS. The metabolites in the spectra include: N-acetylaspartate (NAA), choline (CHO), creatine compounds (CR), myo-inositol (MI), lactate (LAC), glutamate and glutamine (Glu-n). Relative concentrations of metabolites were related to the peak area, and expressed with reference to CR. Student's t-test was used to determine whether there was a statistically significant difference in relative metabolic ratios between high-grade gliomas and metastases. Meanwhile, 16 of all 22 patients were re-examined using magnetic resonance imaging (MRI) within 6 months of surgical resection. Recurrence was present in three patients (two gliomas, one metastasis). RESULTS: Of the 14 patients with gliomas, the peaks of NAA were reduced in three cases; the peaks of LAC, which were elevated, appeared as typical double-peaks in the peritumoural region in nine cases; the peaks of Glu-n, which were also elevated, had a zigzag appearance in seven cases. The peaks of MI were increased in the tumoural region in eight cases, and CHO levels were elevated in all 14 cases. Of the eight patients with metastases, Glu-n peaks in the tumoural region in three cases and CHO peaks in the tumoural region in four cases were elevated, respectively, while the peaks of CR were reduced in three cases, and the peaks of NAA were markedly reduced in four cases within the enhancing tumoural region. Elevated CHO levels (CHO-to-CR ratio was 4.98:1.46, 2.65:0.32) in both the tumoural and peritumoural regions of gliomas but not in the metastases (CHO-to-CR ratio was 1.37:0.92, 1.22:0.38), and elevated MI levels were present (MI-to-CR ratio was 1.67:0.35) within the enhancing foci of gliomas but not in the metastases (MI-to-CR ratio was 0.89:0.31). The difference was statistically significant ( p<0.01). Elevated Glu-n and lipid levels were present in all three patients with recurrences. CONCLUSION: 1HMRS is a useful method in the distinction of these two kinds of tumours. It may also may provide useful prognostic information.