Global health status and fatigue score in isocitrate dehydrogenase-mutant diffuse glioma grades 2 and 3: A longitudinal population-based study from surgery to 12-month follow-up.

Background: At the group level, health-related quality of life (HRQoL) in patients with IDH-mutant diffuse glioma grades 2 and 3 seems to remain stable over time. However, clinical experience indicates that there are patients with unfavorable outcomes on key HRQoL subdomains. The aim of this longitudinal population-based study, following patients over a period of 12 months from surgery, was to describe individual-level data on global health status and fatigue score and explore possible predictors of deterioration. Methods: All patients undergoing surgery for presumed glioma grades 2 or 3 at the Sahlgrenska University Hospital during 2017-2022, were screened for the study. Patients were invited to complete the European Organization of Research and Treatment of Cancer core questionnaires and brain module at baseline, 3 and 12 months postoperatively. Data is reported with respect to minimal clinical important difference (MCID). Results: We included 51 patients with IDH-mutant diffuse glioma grades 2 or 3. There was no difference in group-level data of either global health status or fatigue score from baseline to the 12-month follow-up (P-value > .05). Unfavorable individual changes (beyond MCID) in global health status and fatigue score were observed in 12 and in 17 patients, respectively (23.5% and 33.3%). A lower proportion of proton radiotherapy was found in patients with unfavorable changes in fatigue (10/15, 66.7%) compared to all other patients undergoing radiotherapy (22/23, 95.7%, P-value .03). Conclusions: Deterioration beyond MCID was seen in approximately one-third of patients. Changes in global health status could not be predicted, but changes in fatigue may be influenced by tumor-targeted and symptomatic treatment.

Passive or active drainage system for chronic subdural haematoma-a single-center retrospective follow-up study.

BACKGROUND: Postoperative drainage systems have become a standard treatment for chronic subdural hematoma (CSDH). We previously compared treatment results from three Scandinavian centers using three different postoperative drainage systems and concluded that the active subgaleal drainage was associated with lower recurrence and complication rates than the passive subdural drainage. We consequently changed clinical practice from using the passive subdural drainage to the active subgaleal drainage. OBJECTIVE: The aim of the present study was to assess a potential change in reoperation rates for CSDH after conversion to the active subgaleal drainage. METHODS: This single-center cohort study compared the reoperation rates for recurrent same-sided CSDH and postoperative complication rates between patients treated during two study periods (passive subdural drainage cohort versus active subgaleal drainage cohort). RESULTS: In total, 594 patients were included in the study. We found no significant difference in reoperation rates between the passive subdural drain group and the active subgaleal drain group (21.6%, 95% CI 17.5-26.4% vs. 18.0%, 95% CI 13.8-23.2%; p = 0.275). There was no statistical difference in the rate of serious complications between the groups. The operating time was significantly shorter for patients operated with the active subgaleal drain than patients with the passive subdural drain (32.8 min, 95% CI 31.2-34.5 min vs. 47.6 min, 95% CI 44.7-50.4 min; p < 0.001). CONCLUSIONS: Conversion from the passive subdural to the active subgaleal drainage did not result in a clear reduction of reoperation rates for CSDH in our center.

Growth dynamics of untreated meningiomas.

Background: Knowledge about meningioma growth characteristics is needed for developing biologically rational follow-up routines. In this study of untreated meningiomas followed with repeated magnetic resonance imaging (MRI) scans, we studied growth dynamics and explored potential factors associated with tumor growth. Methods: In a single-center cohort study, we included 235 adult patients with radiologically suspected intracranial meningioma and at least 3 MRI scans during follow-up. Tumors were segmented using an automatic algorithm from contrast-enhanced T1 series, and, if needed, manually corrected. Potential meningioma growth curves were statistically compared: linear, exponential, linear radial, or Gompertzian. Factors associated with growth were explored. Results: In 235 patients, 1394 MRI scans were carried out in the median 5-year observational period. Of the models tested, a Gompertzian growth curve best described growth dynamics of meningiomas on group level. 59% of the tumors grew, 27% remained stable, and 14% shrunk. Only 13 patients (5%) underwent surgery during the observational period and were excluded after surgery. Tumor size at the time of diagnosis, multifocality, and length of follow-up were associated with tumor growth, whereas age, sex, presence of peritumoral edema, and hyperintense T2-signal were not significant factors. Conclusions: Untreated meningiomas follow a Gompertzian growth curve, indicating that increasing and potentially doubling subsequent follow-up intervals between MRIs seems biologically reasonable, instead of fixed time intervals. Tumor size at diagnosis is the strongest predictor of future growth, indicating a potential for longer follow-up intervals for smaller tumors. Although most untreated meningiomas grow, few require surgery.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification.

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

A novel federated deep learning scheme for glioma and its subtype classification.

Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (-1.17%, -0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.

Stalled oligodendrocyte differentiation in IDH-mutant gliomas.

BACKGROUND: Roughly 50% of adult gliomas harbor isocitrate dehydrogenase (IDH) mutations. According to the 2021 WHO classification guideline, these gliomas are diagnosed as astrocytomas, harboring no 1p19q co-deletion, or oligodendrogliomas, harboring 1p19q co-deletion. Recent studies report that IDH-mutant gliomas share a common developmental hierarchy. However, the neural lineages and differentiation stages in IDH-mutant gliomas remain inadequately characterized. METHODS: Using bulk transcriptomes and single-cell transcriptomes, we identified genes enriched in IDH-mutant gliomas with or without 1p19q co-deletion, we also assessed the expression pattern of stage-specific signatures and key regulators of oligodendrocyte lineage differentiation. We compared the expression of oligodendrocyte lineage stage-specific markers between quiescent and proliferating malignant single cells. The gene expression profiles were validated using RNAscope analysis and myelin staining and were further substantiated using data of DNA methylation and single-cell ATAC-seq. As a control, we assessed the expression pattern of astrocyte lineage markers. RESULTS: Genes concordantly enriched in both subtypes of IDH-mutant gliomas are upregulated in oligodendrocyte progenitor cells (OPC). Signatures of early stages of oligodendrocyte lineage and key regulators of OPC specification and maintenance are enriched in all IDH-mutant gliomas. In contrast, signature of myelin-forming oligodendrocytes, myelination regulators, and myelin components are significantly down-regulated or absent in IDH-mutant gliomas. Further, single-cell transcriptomes of IDH-mutant gliomas are similar to OPC and differentiation-committed oligodendrocyte progenitors, but not to myelinating oligodendrocyte. Most IDH-mutant glioma cells are quiescent; quiescent cells and proliferating cells resemble the same differentiation stage of oligodendrocyte lineage. Mirroring the gene expression profiles along the oligodendrocyte lineage, analyses of DNA methylation and single-cell ATAC-seq data demonstrate that genes of myelination regulators and myelin components are hypermethylated and show inaccessible chromatin status, whereas regulators of OPC specification and maintenance are hypomethylated and show open chromatin status. Markers of astrocyte precursors are not enriched in IDH-mutant gliomas. CONCLUSIONS: Our studies show that despite differences in clinical manifestation and genomic alterations, all IDH-mutant gliomas resemble early stages of oligodendrocyte lineage and are stalled in oligodendrocyte differentiation due to blocked myelination program. These findings provide a framework to accommodate biological features and therapy development for IDH-mutant gliomas.

Effect of Disulfiram and Copper Plus Chemotherapy vs Chemotherapy Alone on Survival in Patients With Recurrent Glioblastoma: A Randomized Clinical Trial.

Importance: Disulfiram has demonstrated broad antitumoral effect in several preclinical studies. One of the proposed indications is for the treatment of glioblastoma. Objective: To evaluate the efficacy and safety of disulfiram and copper as add-on to alkylating chemotherapy in patients with recurrent glioblastoma. Design, Setting, and Participants: This was a multicenter, open-label, randomized phase II/III clinical trial with parallel group design. Patients were recruited at 7 study sites in Sweden and 2 sites in Norway between January 2017 and November 2020. Eligible patients were 18 years or older, had a first recurrence of glioblastoma, and indication for treatment with alkylating chemotherapy. Patients were followed up until death or a maximum of 24 months. The date of final follow-up was January 15, 2021. Data analysis was performed from February to September 2022. Interventions: Patients were randomized 1:1 to receive either standard-of-care (SOC) alkylating chemotherapy alone, or SOC with the addition of disulfiram (400 mg daily) and copper (2.5 mg daily). Main Outcomes and Measures: The primary end point was survival at 6 months. Secondary end points included overall survival, progression-free survival, adverse events, and patient-reported quality of life. Results: Among the 88 patients randomized to either SOC (n = 45) or SOC plus disulfiram and copper (n = 43), 63 (72%) were male; the mean (SD) age was 55.4 (11.5) years. There was no significant difference between the study groups (SOC vs SOC plus disulfiram and copper) in 6 months survival (62% [26 of 42] vs 44% [19 of 43]; P = .10). Median overall survival was 8.2 months (95% CI, 5.4-10.2 months) with SOC and 5.5 months (95% CI, 3.9-9.3 months) with SOC plus disulfiram and copper, and median progression-free survival was 2.6 months (95% CI, 2.4-4.6 months) vs 2.3 months (95% CI, 1.7-2.6 months), respectively. More patients in the SOC plus disulfiram and copper group had adverse events grade 3 or higher (34% [14 of 41] vs 11% [5 of 44]; P = .02) and serious adverse events (41% [17 of 41] vs 16% [7 of 44]; P = .02), and 10 patients (24%) discontinued disulfiram treatment because of adverse effects. Conclusions and Relevance: This randomized clinical trial found that among patients with recurrent glioblastoma, the addition of disulfiram and copper to chemotherapy, compared with chemotherapy alone, resulted in significantly increased toxic effects, but no significant difference in survival. These findings suggest that disulfiram and copper is without benefit in patients with recurrent glioblastoma. Trial Registration: ClinicalTrials.gov Identifier: NCT02678975; EUDRACT Identifier: 2016-000167-16.

Longitudinal DNA methylation analysis of adult-type IDH-mutant gliomas.

Diffuse gliomas are the most prevalent malignant primary brain tumors in adults and remain incurable despite standard therapy. Tumor recurrence is currently inevitable, which contributes to a persistent high morbidity and mortality in these patients. In this study, we examined the genome-wide DNA methylation profiles of primary and recurrent adult-type IDH-mutant gliomas to elucidate DNA methylation changes associated with tumor progression (with or without malignant transformation). We analyzed DNA methylation profiles of 37 primary IDH-mutant gliomas and 42 paired recurrences using the DNA methylation EPIC beadChip array. DNA methylation-based classification reflected the tumor progression over time. We observed a methylation subtype switch in a proportion of IDH-mutant astrocytomas; the primary tumors were subclassified as low-grade astrocytomas, which progressed to high-grade astrocytomas in the recurrent tumors. The CNS WHO grade 4 IDH-mutant astrocytomas did not always resemble methylation subclasses of higher grades. The number of differentially methylated CpG sites increased over time, and astrocytomas accumulated more differentially methylated CpG sites than oligodendrogliomas during tumor progression. Few differentially methylated CpG sites were shared between patients. We demonstrated that DNA methylation profiles are mostly maintained during IDH-mutant glioma progression, but CpG site-specific methylation alterations can occur.

Seizures in patients with IDH-mutated lower grade gliomas.

PURPOSE: Most patients with Lower Grade Gliomas (LGG) present with epileptic seizures. Since the advent of molecular diagnostics, more homogenous sub-entities have emerged, including the isocitrate dehydrogenase-mutated (IDH-mutated) astrocytomas and 1p19q-codeleted oligodendrogliomas. We aimed to describe the occurrence of seizures in patients with molecularly defined LGG pre- and postoperatively and to analyze factors affecting seizure status postoperatively. METHODS: A population-based cohort of 130 adult patients with IDH-mutated WHO grade 2 or 3 astrocytomas and oligodendrogliomas was assessed pertaining to seizure burden before and after surgery. RESULTS: Fifty-four (79.4%) patients with astrocytoma and 45 (72.6%) patients with oligodendroglioma had a history of seizures before surgery. At 12 months postoperatively, 51/67 (76.1%) patients with astrocytoma and 47/62 (75.8%) patients with oligodendrogliomas were seizure free. In a multivariable logistic regression analysis, lower extent of resection (EOR) (OR 0.98; 95% CI 0.97-1.00, p = 0.01) and insular tumor location (OR 5.02; 95% CI 1.01-24.87, p = 0.048) were associated with presence of seizures within 1 year postoperatively in the entire LGG cohort. In sub-entities, EOR was in a similar manner associated with seizures postoperatively in astrocytomas (OR 0.98; 95% CI 0.96-0.99, p < 0.01) but not in oligodendrogliomas (p = 0.34). CONCLUSION: Our results are well in line with data published for non-molecularly defined LGG with a large proportion of patients being seizure free at 1 year postoperative. Better seizure outcome was observed with increased EOR in astrocytomas, but this association was absent in oligodendrogliomas.

Is intracranial volume a risk factor for IDH-mutant low-grade glioma? A case-control study.

PURPOSE: Risk of cancer has been associated with body or organ size in several studies. We sought to investigate the relationship between intracranial volume (ICV) (as a proxy for lifetime maximum brain size) and risk of IDH-mutant low-grade glioma. METHODS: In a multicenter case-control study based on population-based data, we included 154 patients with IDH-mutant WHO grade 2 glioma and 995 healthy controls. ICV in both groups was calculated from 3D MRI brain scans using an automated reverse brain mask method, and then compared using a binomial logistic regression model. RESULTS: We found a non-linear association between ICV and risk of glioma with increasing risk above and below a threshold of 1394 ml (p < 0.001). After adjusting for ICV, sex was not a risk factor for glioma. CONCLUSION: Intracranial volume may be a risk factor for IDH-mutant low-grade glioma, but the relationship seems to be non-linear with increased risk both above and below a threshold in intracranial volume.

A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas.

In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS'17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts' time annotating tumors and a small drop in segmentation performance.

Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.

OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient. METHODS: Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases. RESULTS: We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases. CONCLUSIONS: Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.

Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas.

Adult-type diffuse gliomas and meningiomas are the most common primary intracranial tumors of the central nervous system. DNA methylation profiling is a novel diagnostic technique increasingly used also in the clinic. Although molecular heterogeneity is well described in these tumors, DNA methylation heterogeneity is less studied. We therefore investigated the intratumor genetic and epigenetic heterogeneity in diffuse gliomas and meningiomas, with focus on potential clinical implications. We further investigated tumor purity as a source for heterogeneity in the tumors. We analyzed genome-wide DNA methylation profiles generated from 126 spatially separated tumor biopsies from 39 diffuse gliomas and meningiomas. Moreover, we evaluated five methods for measurement of tumor purity and investigated intratumor heterogeneity by assessing DNA methylation-based classification, chromosomal copy number alterations and molecular markers. Our results demonstrated homogeneous methylation-based classification of IDH-mutant gliomas and further corroborates subtype heterogeneity in glioblastoma IDH-wildtype and high-grade meningioma patients after excluding samples with low tumor purity. We detected a large number of differentially methylated CpG sites within diffuse gliomas and meningiomas, particularly in tumors of higher grades. The presence of CDKN2A/B homozygous deletion differed in one out of two patients with IDH-mutant astrocytomas, CNS WHO grade 4. We conclude that diffuse gliomas and high-grade meningiomas are characterized by intratumor heterogeneity, which should be considered in clinical diagnostics and in the assessment of methylation-based and molecular markers.

Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors.

BACKGROUND: For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) is desirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help the classification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated data with ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consuming process with high demand on medical personnel. As an alternative automatic segmentation is often used. However, it does not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRI acquisition parameters across imaging centers, as segmentation is an ill-defined problem. Analogous to visual object tracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas in MR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding box areas (e.g. ellipse shaped boxes) for classification without a significant drop in performance. METHOD: In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments were conducted on two datasets (US and TCGA) consisting of multi-modality MRI scans where the US dataset contained patients with diffuse low-grade gliomas (dLGG) exclusively. RESULTS: Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and 79.50% for IDH mutation/wild-type on TCGA dataset. Comparisons with that of using annotated GT tumor data for training showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype). CONCLUSION: Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for training a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With more data that can be made available, this may be a reasonable trade-off where decline in performance may be counteracted with more data.

Primary versus recurrent surgery for glioblastoma-a prospective cohort study.

BACKGROUND: There is currently limited evidence for surgery in recurrent glioblastoma (GBM). Our aim was to compare primary and recurrent surgeries, regarding changes in perioperative, generic health-related quality of life (HRQoL), complications, extents of resection and survival. METHODS: Between 2007 and 2018, 65 recurrent and 160 primary GBM resections were prospectively enrolled. HRQoL was recorded with EQ-5D 3L preoperatively and at 1 month postoperatively. Median perioperative change in HRQoL and change greater than the minimal clinically important difference (MCID) were assessed. Tumour volume and extent of resection were obtained from pre- and postoperative MRI scans. Survival was assessed from date of surgery. RESULTS: Comparing recurrent surgeries and primary resections, most variables were balanced at baseline, but median age (59 vs. 62, p = 0.005) and median preoperative tumour volume (14.9 vs. 25.3 ml, p = 0.001) were lower in recurrent surgeries. There were no statistically significant differences regarding complication rates, neurological deficits, extents of resection or EQ-5D 3L index values at baseline and at follow-up. Twenty (36.4%) recurrent resections vs. 39 (27.5%) primary resections reported clinically significant deterioration in HRQoL at follow-up. Stratified by clinically significant change in EQ-5D 3L, the survival distributions were not statistically significantly different in either group. Survival was associated with extent of resection (p = 0.015) in recurrent surgeries only. CONCLUSIONS: Outcomes after primary and recurrent surgeries were quite similar in our practice. As surgery may prolong life in patients where gross total resection is obtainable with reasonable risk, the indication for surgery in GBM should perhaps not differ that much in primary and recurrent resections.

Are there predilection sites for intracranial meningioma? A population-based atlas.

Meningioma is the most common benign intracranial tumor and is believed to arise from arachnoid cap cells of arachnoid granulations. We sought to develop a population-based atlas from pre-treatment MRIs to explore the distribution of intracranial meningiomas and to explore risk factors for development of intracranial meningiomas in different locations. All adults (≥ 18 years old) diagnosed with intracranial meningiomas and referred to the department of neurosurgery from a defined catchment region between 2006 and 2015 were eligible for inclusion. Pre-treatment T1 contrast-enhanced MRI-weighted brain scans were used for semi-automated tumor segmentation to develop the meningioma atlas. Patient variables used in the statistical analyses included age, gender, tumor locations, WHO grade and tumor volume. A total of 602 patients with intracranial meningiomas were identified for the development of the brain tumor atlas from a wide and defined catchment region. The spatial distribution of meningioma within the brain is not uniform, and there were more tumors in the frontal region, especially parasagittally, along the anterior part of the falx, and on the skull base of the frontal and middle cranial fossa. More than 2/3 meningioma patients were females (p < 0.001) who also were more likely to have multiple meningiomas (p < 0.01), while men more often have supratentorial meningiomas (p < 0.01). Tumor location was not associated with age or WHO grade. The distribution of meningioma exhibits an anterior to posterior gradient in the brain. Distribution of meningiomas in the general population is not dependent on histopathological WHO grade, but may be gender-related.

Spinal cord compression in relation to clinical symptoms in patients with spinal meningiomas.

OBJECTIVE: Spinal meningiomas are common primary tumors of the spinal canal and the resulting spinal cord compression (SCC) is intrinsically related to symptoms and outcome, but literature concerning this association is limited. We aimed to present data on both degree of SCC and tumor occupancy percentage in relation to neurological symptoms and outcome. METHODS: Patients ≥ 18 years with a histological diagnosis of spinal canal meningioma treated between 2000 and 2017 were retrospectively evaluated for symptoms and neurological outcome in relation to SCC (i.e. compression of spinal cord at maximal tumor compression compared to maximum area above/below compression) and tumor occupancy percentage (percentage of dural sac area occupied by tumor at maximal tumor compression). Area segmentation of spinal cord, tumor and dural sac (as marker of spinal canal) was performed manually on magnetic resonance imaging (MRI) scans. The neurological deficit was assessed pre- and postoperatively according to the McCormick score. A logistic regression was made with a training set to identify the cut-off level for motor deficit. RESULTS: The cohort included 111 patients with a mean age of 62.5 years and 77.5% were female. The dominating symptoms preoperatively were sensory disturbance (91.0%), motor deficit (80.2%) and gait disturbance (67.6%). Postoperatively 53.2% of patients, also in some of those with severe deficit and high tumor occupancy, improved their neurological deficit and 43.2% were unchanged. Patients with intradural meningioma and assessable MRI scans were included to evaluate SCC (n = 83). The mean extent of SCC was 50.6%. Exploration of tumor occupancy percentage identified a cut-off at 65% tumor occupancy to best discriminate between patients with or without motor deficit. CONCLUSION: Patients with an intradural tumor occupancy percentage of > 65% are more likely to have a preoperative symptom and deficit, validating previous findings. Therefore, we suggest that even in asymptomatic, otherwise fit, patients with tumor occupancy approaching 65% should be considered for surgery since there is a high risk of developing deficit with even minimal growth. Concerning recovery, patients with tumor both high tumor occupancy and significantly impaired function tended to improve their functional level postoperatively.

What is the current clinico-radiological diagnostic accuracy for intracranial tumours?

OBJECTIVE: To determine the diagnostic accuracy of routine clinico-radiological workup for a population-based selection of intracranial tumours. METHODS: In this prospective cohort study, we included consecutive adult patients who underwent a primary surgical intervention for a suspected intracranial tumour between 2015 and 2019 at a single-neurosurgical centre. The treating team estimated the expected diagnosis prior to surgery using predefined groups. The expected diagnosis was compared to final histopathology and the accuracy of preoperative clinico-radiological diagnosis (sensitivity, specificity, positive and negative predictive values) was calculated. RESULTS: 392 patients were included in the data analysis, of whom 319 underwent a primary surgical resection and 73 were operated with a diagnostic biopsy only. The diagnostic accuracy varied between different tumour types. The overall sensitivity, specificity and diagnostic mismatch rate of clinico-radiological diagnosis was 85.8%, 97.7% and 4.0%, respectively. For gliomas (including differentiation between low-grade and high-grade gliomas), the same diagnostic accuracy measures were found to be 82.2%, 97.2% and 5.6%, respectively. The most common diagnostic mismatch was between low-grade gliomas, high-grade gliomas and metastases. Accuracy of 90.2% was achieved for differentiation between diffuse low-grade gliomas and high-grade gliomas. CONCLUSIONS: The current accuracy of a preoperative clinico-radiological diagnosis of brain tumours is high. Future non-invasive diagnostic methods need to outperform our results in order to add much value in a routine clinical setting in unselected patients.

Depression and ability to work after vestibular schwannoma surgery: a nationwide registry-based matched cohort study on antidepressants, sedatives, and sick leave.

BACKGROUND: In patients with vestibular schwannomas (VS), tumor control is often achieved, and life expectancy is relatively good. The main risks of surgical treatment are hearing loss and facial nerve function. The occurrence of mood and sleeping disorders in relation to surgery is an important aspect of health that has rarely been studied. Similarly, only limited data exist on the rate of sick leave for patients with VS. In this nationwide registry-based study, we define the use of antidepressants and sedatives and the sick leave pattern before and after VS surgery. METHODS: Adult patients with histopathologically verified VS were identified in the Swedish Brain Tumor Registry (SBTR) and clinical data were linked to relevant national registries after assigning five matched controls to each patient. We studied patterns of dispensed antidepressants and sedative drugs as well as patterns of sick leave compared to respective controls at 2 years before and 2 years following surgery. RESULTS: We identified 333 patients and 1662 matched controls. The rate of antidepressant use was similar between patients and controls 2 years before surgery (6.0% vs 6.3%) and 2 years after surgery (10.1% vs 7.5%). The rate of sedative use was also similar 2 years before surgery (3.9% vs 4.3%) and 2 years after surgery (4.8% vs 5.3%). The rate of sick leave was similar at baseline between patients and controls, but at 2 years after surgery, 75% of patients vs 88% of controls (p < 0.01) had no registered sick leave. Long-term sick leave after surgery was predicted by use of sedatives (OR 0.60, 95% CI 0.38-0.94, p = 0.03), more preoperative sick leave (OR 0.91, 95% CI 0.89-0.93, p < 0.001), and new-onset neurological deficits after surgery (OR 0.42, 95% CI 0.24-0.76, p = 0.004). CONCLUSION: This nationwide study shows no significant differences in the use of antidepressants and sedatives between patients and controls, while the rate of postoperative sick leave was higher in patients than in controls after VS surgery. Our findings underpin the importance of avoiding surgical sequelae and facilitating return to normal professional life.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas.

BACKGROUND: DNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study. RESULTS: A total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features. CONCLUSION: The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.