Proton magnetic resonance spectroscopy in differentiating glioblastomas from primary cerebral lymphomas and brain metastases

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods

PURPOSE

To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

METHODOLOGY

Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR). Model performance was assessed using several ML pipelines and 3D-convolutional neural networks (3D-CNN) using sequence specific masks, as well as combination of masks. All pipelines were trained and evaluated with 5-fold nested cross-validation on internal data followed by external validation using multi-class AUC.

RESULTS

Two ML models achieved similar performance on test set, one using T2-ET and T2-PTR masks (AUC: 0.885, 95% CI: [0.816, 0.935] and another using T1-CE-ET and FLAIR-PTR mask (AUC: 0.878, CI: [0.804, 0.930]). The best performing DL models achieved an AUC of 0.854, (CI [0.774, 0.914]) on external data using T1-CE-ET and T2-PTR masks, followed by model derived from T1-CE-ET, ADC-ET and FLAIR-PTR masks (AUC: 0.851, CI [0.772, 0.909]).

CONCLUSION

Both ML and DL derived pipelines achieved similar performance. T1-CE mask was used in three of the top four overall models. Additionally, all four models had some mask derived from PTR, either T2WI or FLAIR.

Challenges in radiological evaluation of brain metastases, beyond progression

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: "the inflammatory cloud" and "incomplete ring enhancement", in order to adopt a conservative management with close follow-up.

Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data

Background

To investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key pathophysiological processes involved in the hierarchization of the decision-making algorithms of the models.

Methods

From 2013 to 2020, 180 consecutive patients with histopathologically proved lymphomas (n = 77), glioblastomas (n = 45), and metastases (n = 58) were included in machine learning analysis after undergoing MRI. The perfusion parameters (rCBVmax, PSRmax) and spectroscopic concentration ratios (lac/Cr, Cho/NAA, Cho/Cr, and lip/Cr) were applied to construct Classification and Regression Tree (CART) models for multiclass classification of these brain tumors. A 5-fold random cross validation was performed on the dataset.

Results

The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, 0.98 for GBM and 1.00 for METs. The model accuracy was 0.96 with a RSquare of 0.887. Five rules of classifier combinations were extracted with a predicted probability from 0.907 to 0.989 for that end nodes of the decision tree for tumor multiclass classification. In hierarchical order of importance, the root node (Cho/NAA) in the decision tree algorithm was primarily based on the proliferative, infiltrative, and neuronal destructive characteristics of the tumor, the internal node (PSRmax), on tumor tissue capillary permeability characteristics, and the end node (Lac/Cr or Cho/Cr), on tumor energy glycolytic (Warburg effect), or on membrane lipid tumor metabolism.

Conclusion

Our study shows potential implementation of machine learning decision tree model algorithms based on a hierarchical, convenient, and personalized use of perfusion and spectroscopy MRI data for multiclass classification of these brain tumors.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

Differentiating glioblastoma from primary central nervous system lymphoma of atypical manifestation using multiparametric magnetic resonance imaging: A comparative study

Background

The aim of this study is to evaluate the diagnostic efficiency of magnetic resonance imaging (MRI) of single parameters, unimodality, and bimodality in distinguishing glioblastoma (GBM) from atypical primary central nervous system lymphoma (PCNSL) based on diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC) enhancement, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (¹H-MRS) findings.

Methods

The cohort included 108 patients pathologically diagnosed with GBM and 54 patients pathologically diagnosed with PCNSL. Pretreatment morphological MRI, DWI, DSC, DTI and MRS were all performed on each patient. The quantitative parameters of multimodal MRI were measured and compared between the patients in the GBM and atypical PCNSL groups, and those parameters showing a significant difference (p < 0.05) between patients in the GBM and atypical PCNSL groups were used to develop one-parameters, unimodality, and bimodality models. We evaluated the efficiency of different models in distinguishing GBM from atypical PCNSL by performing receiver operating characteristic analysis (ROC).

Results

Atypical PCNSL had lower minimum apparent diffusion coefficient (ADC_min), mean ADC (ADC_mean), relative ADC (rADC), mean relative cerebral blood volume (rCBV_mean), maximum rCBV (rCBV_max), fractional anisotropy (FA), axial diffusion coefficient (DA) and radial diffusion coefficient (DR) values and higher choline/creatine (Cho/Cr) and lipid/creatine (Lip/Cr) ratios than GBM (all p < 0.05). The rCBV_max, DTI and DSC + DTI data were optimal models of single-parameter, unimodality and bimodality for differentiation of GBM from atypical PCNSL, yielding areas under the curves (AUCs) of 0.905, 0.954, and 0.992, respectively.

Conclusions

Models of single-parameter, unimodality and bimodality based on muti multiparameter functional MRI may help to discriminate GBM from atypical PCNSL.

Imaging of White Matter Injury Correlates with Plasma and Tissue Biomarkers in Pediatric Porcine Model of Traumatic Brain Injury

Traumatic brain injury (TBI) causes significant white matter injury, which has been characterized by various rodent and human clinical studies. The exact time course of imaging changes in a pediatric brain after TBI and its relation to biomarkers of injury and cellular function, however, is unknown. To study the changes in major white matter structures using a valid model of TBI that is comparable to a human pediatric brain in terms of size and anatomical features, we utilized a four-week-old pediatric porcine model of injury with controlled cortical impact (CCI). Using diffusion tensor imaging differential tractography, we show progressive anisotropy changes at major white matter tracts such as the corona radiata and inferior fronto-occipital fasciculus between day 1 and day 30 after injury. Moreover, correlational tractography shows a large part of bilateral corona radiata having positive correlation with the markers of cellular respiration. In contrast, bilateral corona radiata has a negative correlation with the plasma biomarkers of injury such as neurofilament light or glial fibrillary acidic protein. These are expected correlational findings given that higher integrity of white matter would be expected to correlate with lower injury biomarkers. We then studied the magnetic resonance spectroscopy findings and report decrease in a N-acetylaspartate/creatinine (NAA/Cr) ratio at the pericontusional cortex, subcortical white matter, corona radiata, thalamus, genu, and splenium of corpus callosum at 30 days indicating injury. There was also an increase in choline/creatinine ratio in these regions indicating rapid membrane turnover. Given the need for a pediatric TBI model that is comparable to human pediatric TBI, these data support the use of a pediatric pig model with CCI in future investigations of therapeutic agents. This model will allow future TBI researchers to rapidly translate our pre-clinical study findings into clinical trials for pediatric TBI.

High-grade glioma and solitary metastasis: differentiation by spectroscopy and advanced magnetic resonance techniques

Background

The differentiation by means of magnetic resonance between high-grade gliomas and intracranial solitary single metastasis is of the utmost importance since they condition both surgical and complementary treatment.

Results

Retrospective study that analyzes the parameters of advanced magnetic resonance imaging: spectroscopy, diffusion and perfusion, specifically focused on the differences in the coefficients of the metabolites Cho/Cr, Cho/NAA and NAA/Cr in peritumoral edema between high-grade gliomas and metastases. The data have been statistically analyzed using ROC (receiver operating characteristic) curves, and cutoff values were obtained.

A total of 79 patients with histologically analyzed tumors were analyzed: 49 high-grade gliomas (40 multiform glioblastomas and 9 anaplastic astrocytomas) and 30 metastases. A statistically significant mean difference was obtained in the three metabolite ratios. The area under the curve for the Cho/NAA ratio was 0.958 (CI: 0.903–1), for Cho/Cr 0.922 (CI: 0.859–0.985) and for NAA/Cr 0.163 (CI: 0.068–0.258; p < 0.001). The cutoff values were 1.115 for Cho/NAA (sensitivity 93.87%, specificity 93.33%, global precision 93.67%); 1.18 for the Cho/Cr ratio (sensitivity 89.79%, specificity 93.33% and precision 91.13%) and 1.155 for the NAA/Cr ratio (sensitivity 67.34%, specificity 93.33%, global precision 44.30%).

Conclusion

The results of the study support the premise that spectroscopy at the level of peritumoral edema is able to differentiate between high-grade gliomas and metastases by showing tumor infiltration in peritumoral edema.

Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma

Pseudoprogression (PsP) refers to treatment-related clinico-radiologic changes mimicking true progression (TP) that occurs in patients with glioblastoma (GBM), predominantly within the first 6 months after the completion of surgery and concurrent chemoradiation therapy (CCRT) with temozolomide. Accurate differentiation of TP from PsP is essential for making informed decisions on appropriate therapeutic intervention as well as for prognostication of these patients. Conventional neuroimaging findings are often equivocal in distinguishing between TP and PsP and present a considerable diagnostic dilemma to oncologists and radiologists. These challenges have emphasized the need for developing alternative imaging techniques that may aid in the accurate diagnosis of TP and PsP. In this review, we encapsulate the current state of knowledge in the clinical applications of commonly used metabolic and physiologic magnetic resonance (MR) imaging techniques such as diffusion and perfusion imaging and proton spectroscopy in distinguishing TP from PsP. We also showcase the potential of promising imaging techniques, such as amide proton transfer and amino acid-based positron emission tomography, in providing useful information about the treatment response. Additionally, we highlight the role of "radiomics", which is an emerging field of radiology that has the potential to change the way in which advanced MR techniques are utilized in assessing treatment response in GBM patients. Finally, we present our institutional experiences and discuss future perspectives on the role of multiparametric MR imaging in identifying PsP in GBM patients treated with "standard-of-care" CCRT as well as novel/targeted therapies.

Emerging MR Imaging and Spectroscopic Methods to Study Brain Tumor Metabolism

Proton magnetic resonance spectroscopy (1H-MRS) provides a non-invasive biochemical profile of brain tumors. The conventional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent and development of more sophisticated metabolic imaging and spectroscopic sequences have revolutionized the field of neuro-oncologic metabolomics. In this review article, we will briefly describe the scientific premises of three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) MRI techniques. Several published studies have shown how these emerging techniques can significantly impact the management of patients with glioma by determining histologic grades, molecular profiles, planning treatment strategies, and assessing the therapeutic responses. The purpose of this review article is to summarize the potential clinical applications of these techniques in studying brain tumor metabolism.

Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review

This review addresses the challenges of primary central nervous system (CNS) lymphoma diagnosis, assessment of treatment response, and detection of recurrence. Primary CNS lymphoma is a rare form of extra-nodal non-Hodgkin lymphoma that can involve brain, spinal cord, leptomeninges, and eyes. Primary CNS lymphoma lesions are most commonly confined to the white matter or deep cerebral structures such as basal ganglia and deep periventricular regions. Contrast-enhanced magnetic resonance imaging (MRI) is the standard diagnostic modality employed by neuro-oncologists. MRI often shows common morphological features such as a single or multiple uniformly well-enhancing lesions without necrosis but with moderate surrounding edema. Other brain tumors or inflammatory processes can show similar radiological patterns, making differential diagnosis difficult. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) has selected utility in cerebral lymphoma, especially in diagnosis. Primary CNS lymphoma can sometimes present with atypical findings on MRI and FDG PET, such as disseminated disease, non-enhancing or ring-like enhancing lesions. The complementary strengths of PET and MRI have led to the development of combined PET-MR systems, which in some cases may improve lesion characterization and detection. By highlighting active developments in this field, including advanced MRI sequences, novel radiotracers, and potential imaging biomarkers, we aim to spur interest in sophisticated imaging approaches.

Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311-0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.

Radiomics-Based Differentiation between Glioblastoma, CNS Lymphoma, and Brain Metastases: Comparing Performance across MRI Sequences and Machine Learning Models

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.

Physiological Imaging Methods for Evaluating Response to Immunotherapies in Glioblastomas

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive multi-modal therapy. Immunotherapy is currently being evaluated as an alternate treatment modality for recurrent GBMs in clinical trials. These immunotherapeutic approaches harness the patient's immune response to fight and eliminate tumor cells. Standard MR imaging is not adequate for response assessment to immunotherapy in GBM patients even after using refined response assessment criteria secondary to amplified immune response. Thus, there is an urgent need for the development of effective and alternative neuroimaging techniques for accurate response assessment. To this end, some groups have reported the potential of diffusion and perfusion MR imaging and amino acid-based positron emission tomography techniques in evaluating treatment response to different immunotherapeutic regimens in GBMs. The main goal of these techniques is to provide definitive metrics of treatment response at earlier time points for making informed decisions on future therapeutic interventions. This review provides an overview of available immunotherapeutic approaches used to treat GBMs. It discusses the limitations of conventional imaging and potential utilities of physiologic imaging techniques in the response assessment to immunotherapies. It also describes challenges associated with these imaging methods and potential solutions to avoid them.

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

PURPOSE OF REVIEW

This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET).

RECENT FINDINGS

Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.

Differential imaging of atypical demyelinating lesions of the central nervous system

The detection of atypical and sometimes aggressive or tumefactive demyelinating lesions of the central nervous system often poses difficulties in the differential diagnosis. The clinical presentation is generally aspecific, related to the location and similar to a number of different lesions, including neoplasms and other intracranial lesions with mass effect. CSF analysis may also be inconclusive, especially for lesions presenting as a single mass at onset. As a consequence, a brain biopsy is frequently performed for characterization. Advanced MRI imaging plays an important role in directing the diagnosis, reducing the rate of unnecessary biopsies and allowing a prompt start of therapy that is often crucial, especially in the case of infratentorial lesions. In this review, the main pattern of presentation of atypical inflammatory demyelinating diseases is discussed, with particular attention on the differential diagnosis and how to adequately define the correct etiology.

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂ -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Differentiation among Glioblastomas, Primary Cerebral Lymphomas, and Solitary Brain Metastases Using Diffusion-Weighted Imaging and Diffusion Tensor Imaging: A PRISMA-Compliant Meta-analysis

Previous studies showed a high diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation among glioblastomas, primary cerebral lymphomas (PCLs), and solitary brain metastases, whereas other studies reported a low or no diagnostic value of DWI and DTI in differentiation among the three types of brain malignant tumors. In order to enhance the strength of evidence, meta-analysis was conducted to summarize results of studies evaluating the diagnostic values of DWI or DTI in differentiation among the three types of brain malignant tumors. Articles evaluating the diagnostic values of DWI or DTI in differentiation among the three types of tumors and published before December 2019 were searched in databases (PubMed, Medline, Web of Science, EMBASE, and Google Scholar). A summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were calculated from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of each study using STATA 12.0 software and Meta-Disc Version 1.4. In addition, the summary receive-operating characteristic (SROC) curve was constructed. Ultimately, we included 19 diagnostic studies (including 735 glioblastomas patients, 31 PCLs patients, and 792 patients with solitary brain metastases). Regarding differentiation between glioblastomas and solitary brain metastases using DWI or DTI, the calculated pooled parameters were as follows: sensitivity, 0.84 [95% confidence interval (CI): 0.78-0.89]; specificity, 0.88 (95% CI: 0.83-0.92); PLR, 7.2 (95% CI: 4.6-11.3); NLR, 0.18 (95% CI: 0.12-0.27); and DOR, 41 (95% CI: 18-93). The analysis showed a significant heterogeneity (sensitivity, I² = 91.31%, p < 0.01; specificity, I² = 89.24%, p < 0.01). In conclusion, DWI and DTI showed a moderate diagnostic value for differentiating glioblastomas from solitary brain metastasis. Additionally, large-scale prospective studies are essential to explore differentiation between PCLs and solitary brain metastases using DWI or DTI.

MR Perfusion and MR Spectroscopy of Brain Neoplasms

Advances in imaging techniques, such as MR perfusion and spectroscopy, are increasingly indispensable in the management and treatment plans of brain neoplasms: from diagnosing, molecular/genetic typing and grading neoplasms, augmenting biopsy results and improving accuracy, to ultimately directing and monitoring treatment and response. New developments in treatment methods have resulted in new diagnostic challenges for conventional MR imaging, such as pseudoprogression, where MR perfusion has the widest current application. MR spectroscopy is showing increasing promise in noninvasively determining genetic subtypes and, potentially, susceptibility to molecular targeted therapies.

Lack of choline elevation on proton magnetic resonance spectroscopy in grade I-III gliomas

Elevated levels of choline are generally emphasized as marker of increased cellularity and cell membrane turnover in gliomas. In this study, we investigated the incidence rate of lack of choline/creatine and choline/water elevation in a population of grade I-III gliomas. A cohort of 41 patients with histopathologically confirmed gliomas underwent multi-voxel proton magnetic resonance spectroscopy on a 3 T magnetic resonance system prior to treatment. Peak areas for choline and myoinositol were measured from all voxels that exhibited hyperintensity on fluid-attenuated inversion recovery images and were normalized to creatine and unsuppressed water from each voxel. The average metabolite/creatine and metabolite/water ratios from these voxels were then computed. Similarly, average metabolite ratios were computed from normal brain parenchyma. Gliomas were considered for lack of choline elevation when choline/creatine and choline/water ratios from neoplastic regions were less than those from normal brain parenchyma regions. Six of 41 (14.6%) grade I-III gliomas showed lack of elevation for choline/creatine and choline/water ratios compared to normal brain parenchyma. Four of these six gliomas also demonstrated elevated levels of myoinositol/creatine ratio. All other gliomas (n = 35) had elevated choline levels from neoplastic regions relative to normal parenchyma. The sensitivity of choline/creatine or choline/water in determining a grade I-III glioma was 85.4%. These findings suggest that a lack of choline/creatine or choline/water elevation may be seen in some gliomas and low choline levels should not prevent us from considering the possibility of a grade I-III glioma.

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.

Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma

Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three-dimensional echo planar spectroscopic imaging (3D-EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo-radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow-up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N-acetylaspartate (Cho/NAA) were computed and co-registered with post-contrast T₁ -weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D-EPSI in differentiating TP from PsP with high sensitivity and specificity.

Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis

OBJECTIVE

The purpose of this meta-analysis was to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM).

MATERIALS AND METHODS

A thorough search of the databases including PubMed, EMBASE, and Cochrane Library was carried out and the data acquired were up to November 1, 2017. The quality of the studies involved was evaluated using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies, revised version). Multiple analytic values including sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the summary receiver operating characteristic (SROC) curve were calculated and pooled for the statistical analysis. The subgroup analysis was also performed to explore the heterogeneity.

RESULTS

Eight retrospective studies (461 patients with 461 lesions) were included. The pooled SEN, SPE, PLR, NLR, and DOR with 95% confidence interval (CI) were 0.82 [95% CI 0.70-0.90], 0.84 [95% CI 0.75-0.90], 4.96 [95% CI 3.20-7.69], 0.22 [95% CI 0.13-0.37], and 22.85 [95% CI 10.42-50.11], respectively. The area under the curve (AUC) given by SROC curve was 0.90 [95% CI 0.87-0.92]. The subgroup analysis indicated the slice thickness of the images (> 3 mm versus ≤ 3 mm) was a significant factor affecting the heterogeneity. No existence of significant publication bias was confirmed with Deeks' test.

CONCLUSIONS

DWI showed moderate diagnostic performance for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM). Moreover, it is of clinical significance using DWI combined with conventional MRI to differentiate PCNSL from GBM.

MRI as a diagnostic biomarker for differentiating primary central nervous system lymphoma from glioblastoma: A systematic review and meta-analysis

BACKGROUND

Accurate preoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioblastoma is clinically crucial because the treatment strategies differ substantially.

PURPOSE

To evaluate the diagnostic performance of MRI for differentiating PCNSL from glioblastoma.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

Ovid-MEDLINE and EMBASE databases were searched to find relevant original articles up to November 25, 2018. The search term combined synonyms for "lymphoma," "glioblastoma," and "MRI."

FIELD STRENGTH/SEQUENCE

Patients underwent at least one MRI sequence including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced imaging (DSC), dynamic contrast-enhanced imaging (DCE), arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), intravoxel incoherent motion (IVIM), and magnetic resonance spectroscopy (MRS) using 1.5 or 3 T.

ASSESSMENT

Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

STATISTICAL TESTS

Hierarchical logistic regression modeling was used to obtain pooled sensitivity and specificity. Meta-regression was performed.

RESULTS

Twenty-two studies with 1182 patients were included. MRI sequences demonstrated high overall diagnostic performance with pooled sensitivity of 91% (95% confidence interval [CI], 87-93%) and specificity of 89% (95% CI, 85-93%). The area under the hierarchical summary receiver operating characteristic curve was 0.92 (95% CI, 0.90-0.94). Studies using DSC or ASL showed high diagnostic performance (sensitivity of 93% [95% CI, 89-97%] and specificity of 91% [95% CI, 86-96%]). Heterogeneity was only detected in specificity (I² = 66.84%) and magnetic field strength was revealed to be a significant factor affecting study heterogeneity.

DATA CONCLUSION

MRI showed overall high diagnostic performance for differentiating PCNSL from glioblastoma, with studies using DSC or ASL showing high diagnostic performance. Therefore, MRI sequences including DSC or ASL is a potential diagnostic tool for differentiating PCNSL from glioblastoma.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:560-572.

Differentiation of Brain Metastases and Gliomas Based on Color Map of Phase Difference Enhanced Imaging

Background and objective: Phase difference enhanced imaging (PADRE), a new phase-related MRI technique, can enhance both paramagnetic and diamagnetic substances, and select which phases to be enhanced. Utilizing these characteristics, we developed color map of PADRE (Color PADRE), which enables simultaneous visualization of myelin-rich structures and veins. Our aim was to determine whether Color PADRE is sufficient to delineate the characteristics of non-gadolinium-enhancing T2-hyperintense regions related with metastatic tumors (MTs), diffuse astrocytomas (DAs) and glioblastomas (GBs), and whether it can contribute to the differentiation of MTs from GBs.

Methods: Color PADRE images of 11 patients with MTs, nine with DAs and 17 with GBs were created by combining tissue-enhanced, vessel-enhanced and magnitude images of PADRE, and then retrospectively reviewed. First, predominant visibility of superficial white matter and deep medullary veins within non-gadolinium-enhancing T2-hyperintense regions were compared among the three groups. Then, the discriminatory power to differentiate MTs from GBs was assessed using receiver operating characteristic analysis.

Results: The degree of visibility of superficial white matter was significantly better in MTs than in GBs (p = 0.017), better in GBs than in DAs (p = 0.014), and better in MTs than in DAs (p = 0.0021). On the contrary, the difference in the visibility of deep medullary veins was not significant (p = 0.065). The area under the receiver operating characteristic curve to discriminate MTs from GBs was 0.76 with a sensitivity of 80% and specificity of 64%.

Conclusion: Visibility of superficial white matter on Color PADRE reflects inferred differences in the proportion of vasogenic edema and tumoral infiltration within non-gadolinium-enhancing T2-hyperintense regions of MTs, DAs and GBs. Evaluation of peritumoral areas on Color PADRE can help to distinguish MTs from GBs.

Added Value of Spectroscopy to Perfusion MRI in the Differential Diagnostic Performance of Common Malignant Brain Tumors

BACKGROUND AND PURPOSE

Perfusion and spectroscopic MR imaging provide noninvasive physiologic and metabolic characterization of tissues, which can help in differentiating brain tumors. We investigated the diagnostic role of perfusion and spectroscopic MR imaging using individual and combined classifiers of these modalities and assessed the added performance value that spectroscopy can provide to perfusion using optimal combined classifiers that have the highest differential diagnostic performance to discriminate lymphomas, glioblastomas, and metastases.

MATERIALS AND METHODS

From January 2013 to January 2016, fifty-five consecutive patients with histopathologically proved lymphomas, glioblastomas, and metastases were included after undergoing MR imaging. The perfusion parameters (maximum relative CBV, maximum percentage of signal intensity recovery) and spectroscopic concentration ratios (lactate/Cr, Cho/NAA, Cho/Cr, and lipids/Cr) were analyzed individually and in optimal combinations. Differences among tumor groups, differential diagnostic performance, and differences in discriminatory performance of models with quantification of the added performance value of spectroscopy to perfusion were tested using 1-way ANOVA models, receiver operating characteristic analysis, and comparisons between receiver operating characteristic analysis curves using a bivariate χ², respectively.

RESULTS

The highest differential diagnostic performance was obtained with the following combined classifiers: maximum percentage of signal intensity recovery-Cho/NAA to discriminate lymphomas from glioblastomas and metastases, significantly increasing the sensitivity from 82.1% to 95.7%; relative CBV-Cho/NAA to discriminate glioblastomas from lymphomas and metastases, significantly increasing the specificity from 92.7% to 100%; and maximum percentage of signal intensity recovery-lactate/Cr and maximum percentage of signal intensity recovery-Cho/Cr to discriminate metastases from lymphomas and glioblastomas, significantly increasing the specificity from 83.3% to 97.0% and 100%, respectively.

CONCLUSIONS

Spectroscopy yielded an added performance value to perfusion using optimal combined classifiers of these modalities, significantly increasing the differential diagnostic performances for these common brain tumors.

Imaging in neuro-oncology

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

Role of magnetic resonance spectroscopy to differentiate high-grade gliomas from metastases

This study is to measure the diagnostic examination quality of magnetic resonance spectroscopy in differentiating high-grade gliomas from metastases. PubMed, Embase, and Chinese Biomedical databases were systematically searched for relevant studies published through 10 July 2016. Based on the data from eligible studies, heterogeneity and threshold effect tests were performed; pooled sensitivity, specificity, and areas under summary receiver-operating characteristic curve of magnetic resonance spectroscopy were calculated. Finally, seven studies with a total of 261 patients were included. Quantitative synthesis of studies showed that pooled sensitivity/specificity of Cho/NAA and Cho/Cr ratio in peritumoral region was 0.85 (95% confidence interval: 0.79-0.90)/0.93 (95% confidence interval: 0.80-0.99) and 0.86 (95% confidence interval: 0.76-0.92)/0.86 (95% confidence interval: 0.73-0.94). The area under the curve of the summary receiver-operating characteristic curve was 0.95 and 0.90. Pooled sensitivity, specificity, and area under the curve of magnetic resonance spectroscopy to identify high-grade gliomas from metastases were 0.85 (95% confidence interval: 0.79-0.90), 0.84 (95% confidence interval: 0.75-0.90), and 0.90, respectively. We concluded that magnetic resonance spectroscopy demonstrated moderate diagnostic performance in distinguishing high-grade gliomas from metastases. Furthermore, Cho/NAA ratio showed higher specificity and higher value of area under the curve than Cho/Cr ratio in peritumoral region. We suggest that Cho/NAA ratio of peritumoral region should be used to improve diagnostic accuracy of magnetic resonance spectroscopy for differentiating high-grade gliomas from metastases.

Proton MRS imaging in pediatric brain tumors

Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors.

Basic Principles and Clinical Applications of Magnetic Resonance Spectroscopy in Neuroradiology

Magnetic resonance spectroscopy is a powerful tool to assist daily clinical diagnostics. This review is intended to give an overview on basic principles of the technology, discuss some of its technical aspects, and present typical applications in daily clinical routine in neuroradiology.

Magnetic resonance spectroscopy - Revisiting the biochemical and molecular milieu of brain tumors

Background

Magnetic resonance spectroscopy (MRS) is an established tool for in-vivo evaluation of the biochemical basis of human diseases. On one hand, such lucid depiction of ‘live biochemistry’ helps one to decipher the true nature of the pathology while on the other hand one can track the response to therapy at sub-cellular level. Brain tumors have been an area of continuous interrogation and instigation for mankind. Evaluation of these lesions by MRS plays a crucial role in the two aspects of disease management described above.

Scope of review

Presented is an overview of the window provided by MRS into the biochemical aspects of brain tumors. We systematically visit each metabolite deciphered by MRS and discuss the role of deconvoluting the biochemical aspects of pathologies (here in context of brain tumors) in the disease management cycle. We further try to unify a radiologist's perspective of disease with that of a biochemist to prove the point that preclinical work is the mother of the treatment we provide at bedside as clinicians. Furthermore, an integrated approach by various scientific experts help resolve a query encountered in everyday practice.

Major conclusions

MR spectroscopy is an integral tool for evaluation and systematic follow-up of brain tumors. A deeper understanding of this technology by a biochemist would help in a swift and more logical development of the technique while a close collaboration with radiologist would enable definitive application of the same.

General significance

The review aims at inciting closer ties between the two specialists enabling a deeper understanding of this valuable technology.

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Magnetic resonance spectroscopy is an established technology for non-invasive assessment of pathological tissue.

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Good understanding of the physical principles of the technique can help one exploit it maximally.

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An array of information from the technique is available and needs deep understanding of the results.

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Newer variations of this technology are being invented to evaluate different aspects of pathologies in a more refined manner.

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We also discuss the limitations of this technology and possible solutions there-off.

Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas

PURPOSE

To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions.

MATERIALS AND METHODS

In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI.

RESULTS

Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain.

CONCLUSION

Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions.

The Diagnostic Ability of Follow-Up Imaging Biomarkers after Treatment of Glioblastoma in the Temozolomide Era: Implications from Proton MR Spectroscopy and Apparent Diffusion Coefficient Mapping

Objective. To prospectively determine institutional cut-off values of apparent diffusion coefficients (ADCs) and concentration of tissue metabolites measured by MR spectroscopy (MRS) for early differentiation between glioblastoma (GBM) relapse and treatment-related changes after standard treatment. Materials and Methods. Twenty-four GBM patients who received gross total resection and standard adjuvant therapy underwent MRI examination focusing on the enhancing region suspected of tumor recurrence. ADC maps, concentrations of N-acetylaspartate, choline, creatine, lipids, and lactate, and metabolite ratios were determined. Final diagnosis as determined by biopsy or follow-up imaging was correlated to the results of advanced MRI findings. Results. Eighteen (75%) and 6 (25%) patients developed tumor recurrence and pseudoprogression, respectively. Mean time to radiographic progression from the end of chemoradiotherapy was 5.8 ± 5.6 months. Significant differences in ADC and MRS data were observed between those with progression and pseudoprogression. Recurrence was characterized by N-acetylaspartate ≤ 1.5 mM, choline/N-acetylaspartate ≥ 1.4 (sensitivity 100%, specificity 91.7%), N-acetylaspartate/creatine ≤ 0.7, and ADC ≤ 1300 × 10⁻⁶ mm²/s (sensitivity 100%, specificity 100%). Conclusion. Institutional validation of cut-off values obtained from advanced MRI methods is warranted not only for diagnosis of GBM recurrence, but also as enrollment criteria in salvage clinical trials and for reporting of outcomes of initial treatment.

Case-Based Review: newly diagnosed glioblastoma

Glioblastoma (WHO grade IV astrocytoma) is the most common and most aggressive primary brain tumor in adults. Optimal treatment of a patient with glioblastoma requires collaborative care across numerous specialties. The diagnosis of glioblastoma may be suggested by the symptomatic presentation and imaging, but it must be pathologically confirmed via surgery, which can have dual diagnostic and therapeutic roles. Standard of care postsurgical treatment for newly diagnosed patients involves radiation therapy and oral temozolomide chemotherapy. Despite numerous recent trials of novel therapeutic approaches, this standard of care has not changed in over a decade. Treatment options under active investigation include molecularly targeted therapies, immunotherapeutic approaches, and the use of alternating electrical field to disrupt tumor cell division. These trials may be aided by new insights into glioblastoma heterogeneity, allowing for focused evaluation of new treatments in the patient subpopulations most likely to benefit from them. Because glioblastoma is incurable by current therapies, frequent clinical and radiographic assessment is needed after initial treatment to allow for early intervention upon progressive tumor when it occurs.

Magnetic resonance spectroscopy detection of high lipid levels in intraaxial tumors without central necrosis: a characteristic of malignant lymphoma

OBJECT

The differentiation of malignant lymphomas from gliomas or malignant gliomas by conventional MRI can be difficult. The authors studied Gd-enhanced MR images to obtain a differential diagnosis between malignant lymphomas and gliomas without central necrosis or cystic changes and investigated the diagnostic value of single-voxel proton MR spectroscopy ((1)H-MRS) using different parameters, including lipid levels.

METHODS

This was a retrospective study of patients with primary malignant CNS lymphoma (n = 17) and glioma (n = 122 [Grades I, II, III, and IV in 10, 30, 33, and 49 patients, respectively]) who were treated between 2007 and 2013. The authors focused on 15 patients with homogeneously enhanced primary malignant CNS lymphomas and 7 homogeneously enhanced gliomas. Images of all the included tumors were acquired with (1)H-MRS at 3 T, and the diagnoses were histologically confirmed.

RESULTS

Using a short echo time (1)H-MRS, large lipid peaks were observed in all 17 patients with a malignant lymphoma, in 39 patients (79.6%) with a Grade IV glioma, and in 10 patients (30.3%) with a Grade III glioma. A focus on homogeneously enhanced tumors revealed large lipid peaks in 15 malignant lymphomas that were free of central necrosis on Gd-enhanced T1-weighted images. Conversely, in the 7 homogeneously enhanced gliomas (glioblastoma and anaplastic astrocytoma, n = 2 each; anaplastic oligodendroglioma, diffuse astrocytoma, and pilomyxoid astrocytoma, n = 1 each), lipid peaks were small or absent.

CONCLUSIONS

Large lipid peaks on (1)H-MRS images of tumors without central necrosis were characteristic of malignant lymphomas. Conversely, small or absent lipid peaks in intraaxial tumors without central necrosis were strongly suggestive of glioma.

Clinical proton MR spectroscopy in central nervous system disorders

A large body of published work shows that proton (hydrogen 1 [(1)H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of (1)H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of (1)H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which (1)H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.

Magnetic resonance spectroscopy in intracranial tumours of glial origin

BACKGROUND AND PURPOSE

To determine in vivo magnetic resonance spectroscopy (MRS) characteristics of intracranial glial tumours and to assess MRS reliability in glioma grading and discrimination between different histopathological types of tumours.

MATERIAL AND METHODS

Analysis of spectra of 26 patients with glioblastomas, 6 with fibrillary astrocytomas, 4 with anaplastic astrocytomas, 2 with pilocytic astrocytoma, 3 with oligodendrogliomas, 3 with anaplastic oligodendrogliomas and 17 control spectra taken from healthy hemispheres.

RESULTS

All tumours' metabolite ratios, except for Cho/Cr in fibrillary astrocytomas (p = 0.06), were statistically significantly different from the control. The tumours showed decreased Naa and Cr contents and a high Cho signal. The Lac-Lip signal was high in grade III astrocytomas and glioblastomas. Reports that Cho/Cr ratio increases with glioma's grade whereas Naa/Cr decreases were not confirmed. Anaplastic astrocytomas compared to grade II astrocytomas had a statistically significantly greater mI/Cr ratio (p = 0.02). In pilocytic astrocytomas the Naa/Cr value (2.58 ± 0.39) was greater, whilst the Cho/Naa ratio was lower (2.14 ± 0.64) than in the other astrocytomas. The specific feature of oligodendrogliomas was the presence of glutamate/glutamine peak Glx. However, this peak was absent in two out of three anaplastic oligodendrogliomas. Characteristically, the latter tumours had a high Lac-Lip signal.

CONCLUSIONS

MRS in vivo cannot be used as a reliable method for glioma grading. The method is useful in discrimination between WHO grade I and WHO grade II astrocytomas as well as oligodendrogliomas from other gliomas.

Lipid and macromolecules quantitation in differentiating glioblastoma from solitary metastasis: a short-echo time single-voxel magnetic resonance spectroscopy study at 3 T

OBJECTIVE

The differentiation between solitary metastasis (MET) and glioblastoma (GBM) is difficult using only magnetic resonance imaging techniques. Magnetic resonance spectroscopy (MRS) lipid signal indicates cellular necrosis both in GBMs and METs. The purpose of this prospective study was to determine whether a class of lipids and/or macromolecules (MMs), able to efficiently discriminate between these two types of lesions, exists.

METHODS

Forty-one patients with solitary brain tumor (23 GBMs and 18 METs) underwent magnetic resonance imaging and single-voxel MRS. Short-echo time point resolved spectroscopy sequence acquisition with water suppression technique was used. Spectra were analyzed using LCModel. Absolute quantification was performed with "water-scaling" procedure. The analysis was focused on sums of lipid and macromolecular (LM) components at 0.9 and 1.3 ppm.

RESULTS

The LM13 absolute concentration was statistically different (P < 0.0001) between GBMs and METs. With a cutoff of 81 mM in LM13 absolute concentration, METs and GBMs can be distinguished with a 78% of specificity and an 81% of sensitivity. The presence of the MM12 peak, related to the fucose II complex, in tumors harboring a K-ras gene mutation has been investigated.

CONCLUSIONS

We exploited the performance of a clinically easily implementable method, such as short-echo time single-voxel MRS, for the differentiation between brain metastasis and primary brain tumors. The study showed that MRS absolute lipid and macromolecular signals could be helpful in differentiating GBM from metastasis. LM13 class was found to be a discriminant parameter with an accuracy of 85%. Detection of the MM12-fucose peak may also have a role in understanding molecular biology of brain metastasis and should be further investigated to address specific metabolic phenotypes.

Linfoma do sistema nervoso central: ensaio iconográfico

Ilustramos este ensaio iconográfico de linfoma do sistema nervoso central com imagens de ressonância magnética obtidas em nosso serviço nos últimos 13 anos e discutimos algumas das principais características radiológicas deste tipo de linfoma, primário e secundário. O linfoma sistema nervoso central é um tumor relativamente infrequente, mas alguns achados na ressonância magnética podem sugerir este diagnóstico.

3 Tesla magnetic resonance spectroscopy: cerebral gliomas vs. metastatic brain tumors. Our experience and review of the literature

The aim of the present study is to report about the value of magnetic resonance spectroscopy (MRS) in differentiating brain metastases, primary high-grade gliomas (HGG) and low-grade gliomas (LGG). MRI (magnetic resonance imaging) and MRS were performed in 60 patients with histologically verified brain tumors: 32 patients with HGG (28 glioblastomas multiforme [GBM] and 4 anaplastic astrocytomas), 14 patients with LGG (9 astrocytomas and 5 oligodendrogliomas) and 14 patients with metastatic brain tumors. The Cho/Cr (choline-containing compounds/creatine-phosphocreatine complex), Cho/NAA (N-acetyl aspartate) and NAA/Cr ratios were assessed from spectral maps in the tumoral core and peritumoral edema. The differences in the metabolite ratios between LGG, HGG and metastases were analyzed statistically. Lipids/lactate contents were also analyzed. Significant differences were noted in the tumoral and peritumoral Cho/Cr, Cho/NAA and NAA/Cr ratios between LGG, HGG and metastases. Lipids and lactate content revealed to be useful for discriminating gliomas and metastases. The results of this study demonstrate that MRS can differentiate LGG, HGG and metastases, therefore diagnosis could be allowed even in those patients who cannot undergo biopsy.

Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy

BACKGROUND AND PURPOSE

Oligodendrogliomas with 1p/19q chromosome LOH are more sensitive to chemoradiation therapy than those with intact alleles. The usefulness of dynamic susceptibility contrast-PWI-guided ¹H-MRS in differentiating these 2 genotypes was tested in this study.

MATERIALS AND METHODS

Forty patients with oligodendrogliomas, 1p/19q LOH (n = 23) and intact alleles (n = 17), underwent MR imaging and 2D-¹H-MRS. ¹H-MRS VOI was overlaid on FLAIR images to encompass the hyperintense abnormality on the largest cross-section of the neoplasm and then overlaid on CBV maps to coregister CBV maps with ¹H-MRS VOI. rCBVmax values were obtained by measuring the CBV from each of the selected ¹H-MRS voxels in the neoplasm and were normalized with respect to contralateral white matter. Metabolite ratios with respect to ipsilateral Cr were computed from the voxel corresponding to the rCBVmax value. Logistic regression and receiver operating characteristic analyses were performed to ascertain the best model to discriminate the 2 genotypes of oligodendrogliomas. Qualitative evaluation of conventional MR imaging characteristics (patterns of tumor border, signal intensity, contrast enhancement, and paramagnetic susceptibility effect) was also performed to distinguish the 2 groups of oligodendrogliomas.

RESULTS

The incorporation of rCBVmax value and metabolite ratios (NAA/Cr, Cho/Cr, Glx/Cr, myo-inositol/Cr, and lipid + lactate/Cr) into the multivariate logistic regression model provided the best discriminatory classification with sensitivity (82.6%), specificity (64.7%), and accuracy (72%) in distinguishing 2 oligodendroglioma genotypes. Oligodendrogliomas with 1p/19q LOH were also more associated with paramagnetic susceptibility effect (P < .05).

CONCLUSIONS

Our preliminary results indicate the potential of combing PWI and ¹H-MRS to distinguish oligodendroglial genotypes.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

PURPOSE

Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data.

MATERIALS AND METHODS

Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics.

RESULTS

Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology.

CONCLUSION

The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T

Purpose: To assess the contribution of ¹H-magnetic resonance spectroscopy (¹H-MRS), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and dynamic susceptibility contrast-enhanced (DSCE) imaging metrics in the differentiation of glioblastomas from solitary metastasis, and particularly to clarify the controversial reports regarding the hypothesis that there should be a significant differentiation between the intratumoral and peritumoral areas. Methods: Conventional MR imaging, ¹H-MRS, DWI, DTI and DSCE MRI was performed on 49 patients (35 glioblastomas multiforme, 14 metastases) using a 3.0-T MR unit. Metabolite ratios, apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV) were measured in the intratumoral and peritumoral regions of the lesions. Receiver-operating characteristic analysis was used to obtain the cut-off values for the parameters presenting a statistical difference between the two tumor groups. Furthermore, we investigated the potential effect of the region of interest (ROI) size on the quantification of diffusion properties in the intratumoral region of the lesions, by applying two different ROI methods. Results: Peritumoral N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, Cho/NAA and rCBV significantly differentiated glioblastomas from intracranial metastases. ADC and FA presented no significant difference between the two tumor groups. Conclusions:¹H-MRS and dynamic susceptibility measurements in the peritumoral regions may definitely aid in the differentiation of glioblastomas and solitary metastases. The quantification of the diffusion properties in the intratumoral region is independent of the ROI size placed.

Advanced magnetic resonance imaging biomarkers of cerebral metastases

There are a number of magnetic resonance imaging techniques available for use in the diagnosis and management of patients with cerebral metastases. This article reviews these techniques, in particular, the advanced imaging methodologies from which quantitative parameters can be derived, the role of these imaging biomarkers have in distinguishing metastases from primary central nervous system tumours and tumour mimics, and metrics that may be of value in predicting the origin of the primary tumour.

Quantitative short echo time 1H MRSI of the peripheral edematous region of human brain tumors in the differentiation between glioblastoma, metastasis, and meningioma

PURPOSE

To assess metabolite levels in peritumoral edematous (PO) and surrounding apparently normal (SAN) brain regions of glioblastoma, metastasis, and meningioma in humans with (1)H-MRSI to find biomarkers that can discriminate between tumors and characterize infiltrative tumor growth.

MATERIALS AND METHODS

Magnetic resonance (MR) spectra (semi-LASER MRSI, 30 msec echo time, 3T) were selected from regions of interest (ROIs) under MRI guidance, and after quality control of MR spectra. Statistical testing between patient groups was performed for mean metabolite ratios of an entire ROI and for the highest value within that ROI.

RESULTS

The highest ratios of the level of choline compounds and the sum of myo-inositol and glycine over N-acetylaspartate and creatine compounds were significantly increased in PO regions of glioblastoma versus that of metastasis and meningioma. In the SAN region of glioblastoma some of these ratios were increased. Differences were less prominent for metabolite levels averaged over entire ROIs.

CONCLUSION

Specific metabolite ratios in PO and SAN regions can be used to discriminate glioblastoma from metastasis and meningioma. An analysis of these ratios averaged over entire ROIs and those with most abnormal values indicates that infiltrative tumor growth in glioblastoma is inhomogeneous and extends into the SAN region.