Presurgical diagnosis of diffuse gliomas in adults: Post-WHO 2021 practical perspectives from radiologists in neuro-oncology units.

The 2021 World Health Organization classification of CNS tumours was greeted with enthusiasm as well as an initial potential overwhelm. However, with time and experience, our understanding of its key aspects has notably improved. Using our collective expertise gained in neuro-oncology units in hospitals in different countries, we have compiled a practical guide for radiologists that clarifies the classification criteria for diffuse gliomas in adults. Its format is clear and concise to facilitate its incorporation into everyday clinical practice. The document includes a historical overview of the classifications and highlights the most important recent additions. It describes the main types in detail with an emphasis on their appearance on imaging. The authors also address the most debated issues in recent years. It will better prepare radiologists to conduct accurate presurgical diagnoses and collaborate effectively in clinical decision making, thus impacting decisions on treatment, prognosis, and overall patient care.

Imaging of Lymphomas Involving the CNS: An Update-Review of the Full Spectrum of Disease with an Emphasis on the World Health Organization Classifications of CNS Tumors 2021 and Hematolymphoid Tumors 2022.

Lymphomas of the CNS are the second most frequent primary brain malignancy in adults after gliomas. Presurgical suspicion of lymphoma greatly impacts patient management. The radiologic features of this tumor have been widely covered in the literature for decades, but under current classifications, mainly corresponding to the most common presentations of the most frequent type: primary diffuse large B-cell lymphoma of the CNS. Nevertheless, rarer presentations of this specific lymphoma and of other World Health Organization lymphoma subtypes with different imaging features are rarely treated. Moreover, important advances in imaging techniques, changing epidemiologic factors with relevant impact on these tumors (eg, immunodeficiency/dysregulation), and recent updates of the World Health Organization Classification of CNS Tumors 2021 and Hematolymphoid Tumors 2022 may have rendered some accepted concepts outdated. In this article, the authors aim to fulfill a critical need by providing a complete update-review, emphasizing the latest clinical-radiologic features of the full spectrum of lymphomas involving the CNS.

Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics.

BACKGROUND AND PURPOSE: Immunodeficiency-associated CNS lymphoma may occur in different clinical scenarios beyond AIDS. This subtype of CNS lymphoma is diffuse large B-cell and Epstein-Barr virus-positive. Its accurate presurgical diagnosis is often unfeasible because it appears as ring-enhancing lesions mimicking glioblastoma or metastasis. In this article, we describe clinicoradiologic features and test the performance of DSC-PWI metrics for presurgical identification. MATERIALS AND METHODS: Patients without AIDS with histologically confirmed diffuse large B-cell Epstein-Barr virus-positive primary CNS lymphoma (December 2010 to January 2022) and diagnostic MR imaging without onco-specific treatment were retrospectively studied. Clinical, demographic, and conventional imaging data were reviewed. Previously published DSC-PWI time-intensity curve analysis methodology, to presurgically identify primary CNS lymphoma, was used in this particular lymphoma subtype and compared with a prior cohort of 33 patients with Epstein-Barr virus-negative CNS lymphoma, 35 with glioblastoma, and 36 with metastasis data. Normalized curves were analyzed and compared on a point-by-point basis, and previously published classifiers were tested. The standard percentage of signal recovery and CBV values were also evaluated. RESULTS: Seven patients with Epstein-Barr virus-positive primary CNS lymphoma were included in the study. DSC-PWI normalized time-intensity curve analysis performed the best for presurgical identification of Epstein-Barr virus-positive CNS lymphoma (area under the receiver operating characteristic curve of 0.984 for glioblastoma and 0.898 for metastasis), followed by the percentage of signal recovery (0.833 and 0.873) and CBV (0.855 and 0.687). CONCLUSIONS: When a necrotic tumor is found in a potentially immunocompromised host, neuroradiologists should consider Epstein-Barr virus-positive CNS lymphoma. DSC-PWI could be very useful for presurgical characterization, with especially strong performance of normalized time-intensity curves.

Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI.

BACKGROUND AND PURPOSE: DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma. MATERIALS AND METHODS: Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset. RESULTS: A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%). CONCLUSIONS: The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.

Neurologic Involvement in COVID-19: Cause or Coincidence? A Neuroimaging Perspective.

The rapid spread of the coronavirus disease 2019 (COVID-19) pandemic has shaken hospitals worldwide. Some authors suggest that neurologic involvement could further complicate the disease. This descriptive study is a cross-sectional review of 103 patients diagnosed with COVID-19 who underwent neuroimaging (of a total of 2249 patients with COVID-19 in our center). Analyzed variables were neurologic symptoms and acute imaging findings. The most frequent symptoms that motivated neuroimaging examinations were mild nonfocal neurologic symptoms, code stroke (refers to patients presenting with signs and symptoms of stroke whose hyperacute assessment and care is prioritized), focal neurologic symptoms, postsedation encephalopathy, and seizures. No cases of encephalitis or direct central nervous system involvement were detected. Thirteen patients presented with acute ischemic events, and 7, with hemorrhagic events; however, most reported multiple vascular risk factors. Despite the large cohort of patients with COVID-19, we found a large number of symptomatic patients with negative neuroimaging findings, and no conclusions can be drawn concerning concrete associations between neuroimaging and COVID-19.

Seizure-susceptible brain regions in glioblastoma: identification of patients at risk.

BACKGROUND AND PURPOSE: The main aim of this study was to identify which patients with glioblastoma multiforme (GBM) have a higher risk of presenting seizures during follow-up. METHODS: Patients with newly diagnosed GBM were reviewed (n = 306) and classified as patients with (Group 1) and without (Group 2) seizures at onset. Group 2 was split into patients with seizures during follow-up (Group 2A) and patients who never had seizures (Group 2B). The anatomical location of GBM was identified and compared by voxel-based lesion symptom mapping (discovery set). Seizure-susceptible brain regions obtained were assessed visually and automatically in external GBM validation series (n = 85). RESULTS: In patients with GBM who had no seizures at onset, an increased risk of presenting seizures during follow-up was identified in the superior frontal and inferior occipital lobe, as well as in inferoposterior regions of the temporal lobe. Conversely, those patients with GBM located in medial and inferoanterior temporal areas had a significantly lower risk of suffering from seizures during follow-up. Additionally, the seizure-susceptible brain region maps obtained classified patients in the validation set with high positive and negative predictive values. CONCLUSIONS: Tumor location is a useful marker to identify patients with GBM who are at risk of suffering from seizures during follow-up. These results may help to support the use of antiepileptic prophylaxis in a selected GBM population and to improve stratification in antiepileptic clinical trials.

Preradiotherapy MR Imaging: A Prospective Pilot Study of the Usefulness of Performing an MR Examination Shortly before Radiation Therapy in Patients with Glioblastoma.

BACKGROUND AND PURPOSE: Current protocols in patients with glioblastoma include performing an MR examination shortly after surgery and then 2-6 weeks after ending concomitant chemoradiotherapy. The assessment of this first postradiotherapy examination is challenging because the pseudoprogression phenomenon may appear. The aim of this study was to explore if performing an MR examination shortly before radiation therapy (preradiotherapy MR imaging) could improve the radiologic assessment of patients with glioblastoma. MATERIALS AND METHODS: A preradiotherapy MR imaging examination was prospectively performed before the start of radiation therapy in 28 consecutive patients with glioblastoma who had undergone surgical resection. Tumor response to chemoradiotherapy was assessed twice: with the early postoperative MR examination as baseline and with the preradiotherapy MR imaging examination as baseline. In addition, tumor growth in the preradiotherapy MR imaging examination was evaluated, and its correlation with patient survival was assessed with Kaplan-Meier analysis and Cox regression. RESULTS: Tumor progression after radiation therapy was found in 16 patients, corresponding to pseudoprogression in 7 of them (44%). Four assessments of pseudoprogression switched to partial response or stable disease when preradiotherapy MR imaging was the baseline examination, and the ratio of pseudoprogression was reduced to 25% (3 of 12). Significant differences in survival were found when patients were stratified according to the pattern of tumor growth on preradiotherapy MR imaging (median overall survival "no-growth," 837 days; "focal-growth," 582 days; "global-growth," 344 days; P = .001). CONCLUSIONS: Performing a preradiotherapy MR imaging examination may improve the clinical management of patients with glioblastoma by reducing the ratio of pseudoprogression assessments and providing prognostic information.

Development of robust discriminant equations for assessing subtypes of glioblastoma biopsies.

BACKGROUND: In the preceding decade, various studies on glioblastoma (Gb) demonstrated that signatures obtained from gene expression microarrays correlate better with survival than with histopathological classification. However, there is not a universal consensus formula to predict patient survival. METHODS: We developed a gene signature using the expression profile of 47 Gbs through an unsupervised procedure and two groups were obtained. Subsequent to a training procedure through leave-one-out cross-validation, we fitted a discriminant (linear discriminant analysis (LDA)) equation using the four most discriminant probesets. This was repeated for two other published signatures and the performance of LDA equations was evaluated on an independent test set, which contained status of IDH1 mutation, EGFR amplification, MGMT methylation and gene VEGF expression, among other clinical and molecular information. RESULTS: The unsupervised local signature was composed of 69 probesets and clearly defined two Gb groups, which would agree with primary and secondary Gbs. This hypothesis was confirmed by predicting cases from the independent data set using the equations developed by us. The high survival group predicted by equations based on our local and one of the published signatures contained a significantly higher percentage of cases displaying IDH1 mutation and non-amplification of EGFR. In contrast, only the equation based on the published signature showed in the poor survival group a significant high percentage of cases displaying a hypothesised methylation of MGMT gene promoter and overexpression of gene VEGF. CONCLUSION: We have produced a robust equation to confidently discriminate Gb subtypes based in the normalised expression level of only four genes.

Robust discrimination of glioblastomas from metastatic brain tumors on the basis of single-voxel (1)H MRS.

This article investigates methods for the accurate and robust differentiation of metastases from glioblastomas on the basis of single-voxel (1)H MRS information. Single-voxel (1)H MR spectra from a total of 109 patients (78 glioblastomas and 31 metastases) from the multicenter, international INTERPRET database, plus a test set of 40 patients (30 glioblastomas and 10 metastases) from three different centers in the Barcelona (Spain) metropolitan area, were analyzed using a robust method for feature (spectral frequency) selection coupled with a linear-in-the-parameters single-layer perceptron classifier. For the test set, a parsimonious selection of five frequencies yielded an area under the receiver operating characteristic curve of 0.86, and an area under the convex hull of the receiver operating characteristic curve of 0.91. Moreover, these accurate results for the discrimination between glioblastomas and metastases were obtained using a small number of frequencies that are amenable to metabolic interpretation, which should ease their use as diagnostic markers. Importantly, the prediction can be expressed as a simple formula based on a linear combination of these frequencies. As a result, new cases could be straightforwardly predicted by integrating this formula into a computer-based medical decision support system. This work also shows that the combination of spectra acquired at different TEs (short TE, 20-32 ms; long TE, 135-144 ms) is key to the successful discrimination between glioblastomas and metastases from single-voxel (1)H MRS.

Proton MR spectroscopy provides relevant prognostic information in high-grade astrocytomas.

BACKGROUND AND PURPOSE: There is a large range of survival times in patients with HGA that can only be partially explained by histologic grade and clinical aspects. This study aims to retrospectively assess the predictive value of single-voxel (1)H-MRS regarding survival in HGA. MATERIALS AND METHODS: Pretreatment (1)H-MRS in 187 patients with HGA produced 180 spectra at STE (30 ms) and 182 at LTE (136 ms). Patients were dichotomized into 2 groups according to survival better or worse than the median. The spectra of the 2 groups were compared using the Mann-Whitney U test. The points on the spectrum with the most significant differences were selected for discriminating patients with good and poor prognosis. Thresholds were defined with ROC curves, and survival was analyzed by using the Kaplan-Meier method and the Cox proportional hazards model. RESULTS: Four points on the spectrum showed the most significant differences: 0.98 and 3.67 ppm at STE; and 0.98 and 1.25 ppm at LTE (P between <.001 and .011). These points were useful for stratifying 2 prognostic groups (P between <.001 and .003, Kaplan-Meier). The Cox forward stepwise model selected 3 spectroscopic variables: the intensity values of the points 3.67 ppm at STE (hazard ratio, 2.132; 95% CI, 1.504-3.023), 0.98 ppm at LTE (hazard ratio, 0.499; 95% CI, 0.339-0.736), and 1.25 ppm at LTE (hazard ratio, 0.574; 95% CI, 0.368-0.897). CONCLUSIONS: (1)H-MRS is of value in predicting the length of survival in patients with HGA and could be used to stratify prognostic groups.

Non-invasive grading of astrocytic tumours from the relative contents of myo-inositol and glycine measured by in vivo MRS.

MRI and MRS are established methodologies for evaluating intracranial lesions. One MR spectral feature suggested for in vivo grading of astrocytic tumours is the apparent myo-lnositol (ml) intensity (ca 3.55 ppm) at short echo times, although glycine (gly) may also contribute in vivo to this resonance. The purpose of this study was to quantitatively evaluate the ml + gly contribution to the recorded spectral pattern in vivo and correlate it with in vitro data obtained from perchloric acid extraction of tumour biopsies. Patient spectra (n = 95) at 1.5T at short (20-31 ms) and long (135-136 ms) echo times were obtained from the INTERPRET MRS database (http://gabrmn.uab.eslinterpretvalidateddbl). Phantom spectra were acquired with a comparable protocol. Spectra were automatically processed and the ratios of the (ml + gly) to Cr peak heights ((ml + gly)/Cr) calculated. Perchloric acid extracts of brain tumour biopsies were analysed by high-resolution NMR at 9.4T. The ratio (ml + gly)/Cr decreased significantly with astrocytic grade in vivo between low-grade astrocytoma (A2) and glioblastoma multiforme (GBM). In vitro results displayed a somewhat different tendency, with anaplastic astrocytomas having significantly higher (ml + gly)/Cr than A2 and GBM. The discrepancy between in vivo and in vitro data suggests that the NMR visibility of glycine in glial brain tumours is restricted in vivo.

Reversible acute leukoencephalopathy as a form of presentation in cerebral amyloid angiopathy.

BACKGROUND: Cerebral amyloid angiopathy (CAA) may present as cerebral haemorrhage, cerebral infarction and periventricular white matter lesions. Reversible leukoencephalopathy is a rare manifestation of CAA. AIMS OF THE STUDY: To describe two patients with reversible acute leukoencephalopathy as the first manifestation of CAA. PATIENTS: Two consecutive patients were admitted to our neurology department with transient focal neurological symptoms. They showed reversible focal leukoencephalopathy on magnetic resonance imaging (MRI). CAA was finally diagnosed in both, and pathologically confirmed in one. The latter patient showed multiple foci of petechial bleeding in the cortex and subcortex in T2-weighted GRE sequences, suggestive of CAA. CONCLUSION: Reversible acute focal leukoencephalopathy may be an infrequent clinical and radiological pattern of CAA.

Intracranial meningiomatosis causing foster kennedy syndrome by unilateral optic nerve compression and blockage of the superior sagittal sinus.

The original description of the Foster Kennedy syndrome included the clinical triad of optic disc pallor in one eye, optic disc edema in the other eye, and reduced olfaction caused by space-occupying anterior fossa masses. The optic disc pallor was attributed to direct compression of the intracranial optic nerve, the optic disc edema to increased intracranial pressure from mass effect, and the reduced olfaction to direct compression of the olfactory nerve. We report a patient with the ophthalmic features of the Foster Kennedy syndrome from meningiomatosis. A meningioma compressed one optic nerve to cause impaired visual function. Convexity meningiomas compressed the superior sagittal sinus to impair cerebral venous drainage, increased intracranial pressure, and papilledema in the other eye. This is the first report of the Foster Kennedy syndrome caused by this mechanism.

Proton MR spectroscopy improves discrimination between tumor and pseudotumoral lesion in solid brain masses.

BACKGROUND AND PURPOSE: Differentiating between tumors and pseudotumoral lesions by conventional MR imaging may be a challenging question. This study aims to evaluate the potential usefulness and the added value that single-voxel proton MR spectroscopy could provide on this discrimination. MATERIALS AND METHODS: A total of 84 solid brain lesions were retrospectively included in the study (68 glial tumors and 16 pseudotumoral lesions). Single-voxel spectra at TE 30 ms (short TE) and 136 ms (long TE) were available in all cases. Two groups were defined: "training-set" (56 cases) and "test-set" (28 cases). Tumors and pseudotumors were compared in the training-set with the Mann-Whitney U test. Ratios between resonances were defined as classifiers for new cases, and thresholds were selected with receiver operating characteristic (ROC) curves. The added value of spectroscopy was evaluated by 5 neuroradiologists and assessed with the Wilcoxon signed-rank test. RESULTS: Differences between tumors and pseudotumors were found in myo-inositol (mIns); P < .01) at short TE, and N-acetylaspartate (NAA; P < .001), glutamine (Glx; P < .01), and choline (CHO; P < .05) at long TE. Classifiers suggested tumor when mIns/NAA ratio was more than 0.9 at short TE and also when CHO/NAA ratio was more than 1.9 at long TE. Classifier accuracy was tested in the test-set with the following results: short TE, 82% (23/28); long TE, 79% (22/28). The neuroradiologists' confidence rating of the test-cases on a 5-point scale (0-4) improved between 5% (from 2.86-3) and 27% (from 2.25-2.86) with spectroscopy (mean, 17%; P < .01). CONCLUSIONS: The proposed ratios of mIns/NAA at short TE and CHO/NAA at long TE provide valuable information to discriminate between brain tumor and pseudotumor by improving neuroradiologists' accuracy and confidence.

Assignment of the 2.03 ppm resonance in in vivo 1H MRS of human brain tumour cystic fluid: contribution of macromolecules.

MRI and MRS are established techniques for the evaluation of intracranial mass lesions and cysts. The 2.03 ppm signal recorded in their (1)H-MRS spectra is often assigned to NAA from outer volume contamination, although it has also been detected in non-infiltrating tumours and large cysts. We have investigated the molecular origin of this resonance in ten samples of cystic fluids from human brain tumours. The NMR detected content of the 2.03 ppm resonance in 136 ms echo time spectra, assuming an N- CH(3) origin, was 3.19 +/- 1.01 mM. Only one third (34 +/- 12%) of the N-acetyl containing compound (NAC) signal could be extracted by perchloric acid (PCA) indicating that most of it originated in a macromolecular PCA-insoluble component. Chemical analysis of the cyst fluids showed that sialic acid bound to macromolecules would account for 64.3% and hexuronic containing compounds for 29.2% of the NMR-detectable ex vivo signal, 93.4% of the signal at TE 136 ms. Lactate content measured by NMR (6.4 +/- 4.4 mM) and the predominance of NAC originating in sialic acid point to a major origin from tumour rather than from plasma for this 2.03 ppm resonance.

Classification of brain tumours using short echo time 1H MR spectra.

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas (n = 87), meningiomas (n = 57), metastases (n = 39), and astrocytomas grade II (n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

Brain tumor classification based on long echo proton MRS signals.

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo 1H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

Utility of proton MR spectroscopy in the diagnosis of radiologically atypical intracranial meningiomas.

Our aim was to evaluate the usefulness of proton MR spectroscopy ((1)H MRS) in the diagnosis of radiologically atypical brain meningiomas. We studied 37 patients with intracranial meningiomas with MRI and (1)H MRS (TE 136 ms). Their spectra were quantitatively assessed and compared with those of 93 other intracranial brain neoplasms: 15 low-grade and 14 anaplastic astrocytomas, 30 glioblastomas and 34 metastases. The most characteristic features of meningiomas were the presence of alanine, high relative concentrations of choline and glutamine/glutamate and low concentrations of creatine-containing compounds, N-acetyl-containing compounds and lipids. These resonances were assembled in algorithms for two-way differentiation between meningioma and the other tumours. The performance of the algorithms was tested in the 130 patients using the leave-one-out method, with 94% success in differentiating between meningioma and other tumour. Of the 37 meningiomas, five (14%) were thought atypical on MRI, and in only one of these, found to be malignant on histology, was a diagnosis other than meningioma suggested by the algorithm. The other four were correctly classified. We suggest that (1)H MRS provides information on intracranial meningiomas which may be useful in diagnosis of radiologically atypical cases.