PET imaging in glioma: techniques and current evidence

PET holds potential to provide additional information about tumour metabolic processes, which could aid brain tumour differential diagnosis, grading, molecular subtyping and/or the distinction of therapy effects from disease recurrence. This review discusses PET techniques currently in use for untreated and treated glioma characterization and aims to critically assess the evidence for different tracers ([F]Fluorodeoxyglucose, choline and amino acid tracers) in this context

Endogenous chemical exchange saturation transfer (CEST) MR imaging for the diagnosis and therapy response assessment of brain tumors: A systematic review

Purpose: To generate a narrative synthesis of published data on the use of endogenous chemical exchange saturation transfer (CEST) MR imaging in brain tumors. Materials and Methods: A systematic database search (PubMed, Ovid Embase, Cochrane Library) was used to collate eligible studies. Two researchers independently screened publications according to predefined exclusion and inclusion criteria, followed by comprehensive data extraction. All included studies were subjected to a bias risk assessment using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Results: The electronic database search identified 430 studies, of which 36 studies fulfilled the inclusion criteria. The final selection of included studies was categorized into 5 groups as follows: grading gliomas, 19 studies (areas under the curve (AUC) 0.500-1.000); predicting molecular subtypes of gliomas, 5 studies (AUC 0.610-0.920); distinction of different brain tumor types, 7 studies (AUC 0.707-0.905); therapy response assessment, 3 studies (AUC not given) and differentiating recurrence from treatment-related changes, 5 studies (AUC 0.880- 0.980). A high bias risk was observed in a substantial proportion of studies. Conclusion: Endogenous CEST imaging offers valuable, potentially unique information in brain tumors, but its diagnostic accuracy remains incompletely known. Further research is required to assess the method’s role in support of molecular genetic diagnosis, to investigate its use in the post treatment phase, and to compare techniques with a view to standardization

Venous infarction mimicking a neoplasm in spontaneous intracranial hypotension: an unusual cause of Parinaud's syndrome

We present a case of longstanding, undiagnosed spontaneous intracranial hypotension (SIH) with an acute presentation of Parinaud's syndrome, in whom serial imaging demonstrated development of a midbrain mass. The patient was ultimately diagnosed with tumefactive venous infarction secondary to SIH. However, this patient underwent a brainstem biopsy, which in retrospect may have been avoidable. This case demonstrates the imaging features of tumefactive venous infarction in SIH and highlights the risk of misinterpretation as a neoplasm with potentially catastrophic consequences

Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre

Introduction: Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre. Methods: We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period. Results: In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results. Conclusions: Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma

Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults

Background: To assess anatomical and quantitative diffusion-weighted MR imaging features in a recently classified lethal neoplasm, H3 K27M histone-mutant diffuse midline glioma [World Health Organization (WHO) IV]. / Methods: Fifteen untreated gliomas in teenagers and adults (median age 19, range, 14–64) with confirmed H3 K27M histone-mutant genotype were analysed at a national referral centre. Morphological characteristics including tumour epicentre(s), T2/FLAIR and Gadolinium enhancement patterns, calcification, haemorrhage and cyst formation were recorded. Multiple apparent diffusion coefficient (ADCmin, ADCmean) regions of interest were sited in solid tumour and normal appearing white matter (ADCNAWM) using post-processing software (Olea Sphere v2.3, Olea Medical). ADC histogram data (2nd, 5th, 10th percentile, median, mean, kurtosis, skewness) were calculated from volumetric tumour segmentations and tested against the regions of interest (ROI) data (Wilcoxon signed rank test). / Results: The median interval from imaging to tissue diagnosis was 9 (range, 0–74) days. The structural MR imaging findings varied between individuals and within tumours, often featuring signal heterogeneity on all MR sequences. All gliomas demonstrated contact with the brain midline, and 67% exhibited rim-enhancing necrosis. The mean ROI ADCmin value was 0.84 (±0.15 standard deviation, SD) ×10−3 mm2/s. In the largest tumour cross-section (excluding necrosis), an average ADCmean value of 1.12 (±0.25)×10−3 mm2/s was observed. The mean ADCmin/NAWM ratio was 1.097 (±0.149), and the mean ADCmean/NAWM ratio measured 1.466 (±0.299). With the exception of the 2nd centile, no statistical difference was observed between the regional and histogram derived ADC results. / Conclusions: H3 K27M-mutant gliomas demonstrate variable morphology and diffusivity, commonly featuring moderately low ADC values in solid tumour. Regional ADC measurements appeared representative of volumetric histogram data in this study

Apparent diffusion coefficient for molecular subtyping of non-gadolinium-enhancing WHO grade II/III glioma: volumetric segmentation versus two-dimensional region of interest analysis

OBJECTIVES: To investigate if quantitative apparent diffusion coefficient (ADC) measurements can predict genetic subtypes of non-gadolinium-enhancing gliomas, comparing whole tumour against single slice analysis. METHODS: Volumetric T2-derived masks of 44 gliomas were co-registered to ADC maps with ADC mean (ADCmean) calculated. For the slice analysis, two observers placed regions of interest in the largest tumour cross-section. The ratio (ADCratio) between ADCmeanin the tumour and normal appearing white matter was calculated for both methods. RESULTS: Isocitrate dehydrogenase (IDH) wild-type gliomas showed the lowest ADC values throughout (p < 0.001). ADCmeanin the IDH-mutant 1p19q intact group was significantly higher than in the IDH-mutant 1p19q co-deleted group (p < 0.01). A volumetric ADCmeanthreshold of 1201 × 10-6mm2/s identified IDH wild-type with a sensitivity of 83% and a specificity of 86%; a volumetric ADCratiocut-off value of 1.65 provided a sensitivity of 80% and a specificity of 92% (area under the curve (AUC) 0.9-0.94). A slice ADCratiothreshold for observer 1 (observer 2) of 1.76 (1.83) provided a sensitivity of 80% (86%), specificity of 91% (100%) and AUC of 0.95 (0.96). The intraclass correlation coefficient was excellent (0.98). CONCLUSIONS: ADC measurements can support the distinction of glioma subtypes. Volumetric and two-dimensional measurements yielded similar results in this study. KEY POINTS: • Diffusion-weighted MRI aids the identification of non-gadolinium-enhancing malignant gliomas • ADC measurements may permit non-gadolinium-enhancing glioma molecular subtyping • IDH wild-type gliomas have lower ADC values than IDH-mutant tumours • Single cross-section and volumetric ADC measurements yielded comparable results in this study

World Health Organization Grade II/III Glioma Molecular Status: Prediction by MRI Morphologic Features and Apparent Diffusion Coefficient

BACKGROUND: A readily implemented MRI biomarker for glioma genotyping is currently lacking. PURPOSE: To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. MATERIALS AND METHODS: In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADC_{mi}) and mean (ADC_{mean}) regions of interest were defined in tumor and normal appearing white matter (ADC_{NAWM}). visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. RESULTS: he study included 290 patients (median age, 40 years; interquartile range, 33–52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADC_{mean}-to-ADC_{NAWM} ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. CONCLUSION: Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE: Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem). METHODS: Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists’ classifications. RESULTS: SVM classification based on combined perfusion and structural features outperformed radiologists’ classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists’ classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists). CONCLUSION: Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001)

The Perceived Impact of COVID-19 on Student Well-Being and the Mediating Role of the University Support: Evidence From France, Germany, Russia, and the UK

The rapid and unplanned change to teaching and learning in the online format brought by COVID-19 has likely impacted many, if not all, aspects of university students' lives worldwide. To contribute to the investigation of this change, this study focuses on the impact of the pandemic on student well-being, which has been found to be as important to student lifelong success as their academic achievement. Student well-being has been linked to their engagement and performance in curricular, co-curricular, and extracurricular activities, intrinsic motivation, satisfaction, meaning making, and mental health. The purpose of this study was to examine how student perceptions of their degree completion and future job prospects during the pandemic impact their well-being and what role university support plays in this relationship. We used the conservation of resources theory to frame our study and to develop five hypotheses that were later tested via structural equation modeling. Data were collected from 2,707 university students in France, Germany, Russia, and UK via an online survey. The results showed that university support provided by instructors and administration plays a mediating role in the relationship between the perceived impact of COVID-19 on degree completion and future job prospects and levels of student well-being. Student well-being is decreased by their concerns for their degree completion but not by their concerns for future job prospects. In turn, concerns for future job prospects affect student well-being over time. These results suggest that in a “new normal,” universities could increase student well-being by making support to student studies a priority, especially for undergraduates. Also, universities should be aware of the students' changing emotional responses to crisis and ensure visibility and accessibility of student support.Peer reviewe

Filtration‐histogram based magnetic resonance texture analysis (Mrta) for the distinction of primary central nervous system lymphoma and glioblastoma

Primary central nervous system lymphoma (PCNSL) has variable imaging appearances, which overlap with those of glioblastoma (GBM), thereby necessitating invasive tissue diagnosis. We aimed to investigate whether a rapid filtration histogram analysis of clinical MRI data supports the distinction of PCNSL from GBM. Ninety tumours (PCNSL n = 48, GBM n = 42) were analysed using pre‐treatment MRI sequences (T1‐weighted contrast‐enhanced (T1CE), T2‐weighted (T2), and apparent diffusion coefficient maps (ADC)). The segmentations were completed with proprietary texture analysis software (TexRAD version 3.3). Filtered (five filter sizes SSF = 2–6 mm) and unfil-tered (SSF = 0) histogram parameters were compared using Mann‐Whitney U non‐parametric test-ing, with receiver operating characteristic (ROC) derived area under the curve (AUC) analysis for significant results. Across all (n = 90) tumours, the optimal algorithm performance was achieved using an unfiltered ADC mean and the mean of positive pixels (MPP), with a sensitivity of 83.8%, specificity of 8.9%, and AUC of 0.88. For subgroup analysis with >1/3 necrosis masses, ADC permit-ted the identification of PCNSL with a sensitivity of 96.9% and specificity of 100%. For T1CE‐derived regions, the distinction was less accurate, with a sensitivity of 71.4%, specificity of 77.1%, and AUC of 0.779. A role may exist for cross‐sectional texture analysis without complex machine learning models to differentiate PCNSL from GBM. ADC appears the most suitable sequence, especially for necrotic lesion distinction