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Background: Chronic subdural hematoma (CSDH) is commonly treated by burr-hole drainage with subgaleal or subdural drain insertion, mostly based on surgeon's preference. We analyzed the recurrence rate and clinical outcomes after burr-hole drainage for CSDH and subdural or subgaleal drain insertion in a single center, retrospective cohort study. Methods: 700 cases of burr-hole drainage for CSDH between 2017 and 2022 were included. Subdural drain insertion was compared to subgaleal drain insertion. The primary outcome were the rates of recurrence and reoperation. The secondary outcomes consisted of morbidity, postoperative complications, and mortality. Results: Baseline characteristics were comparable. The recurrence and reoperation rate after subdural drainage were respectively 15.3% (38/249) and 9.6% (24/249). The recurrence and reoperation rate after subgaleal drainage were respectively 13.4% (55/409) and 10.8% (44/409). There were no significant associations found in recurrence and reoperation rate between both drain insertions. No differences in morbidity, complication rate and mortality between drain insertion locations was found. Conclusion: We found relative equipoise between subdural or subgaleal drain insertion concerning recurrence, reoperation rate or clinical outcome. A large multicenter randomized controlled trial could be designed to further assess the outcomes of subdural and subgaleal drain placement after burr-hole drainage for CSDH. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods: We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results: The nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA's median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions: The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods: We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results: The nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions: The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM), the most common glial primary brain tumour, is without exception lethal. Every year approximately 600 patients are diagnosed with this heterogeneous disease in The Netherlands. Despite neurosurgery, chemo -and radiation therapy, these tumours inevitably recur. Currently, there is no gold standard at time of recurrence and treatment options are limited. Unfortunately, the results of dedicated trials with new drugs have been very disappointing. The goal of the project is to obtain the evidence for changing standard of care (SOC) procedures to include whole genome sequencing (WGS) and consequently adapt care guidelines for this specific patient group with very poor prognosis by offering optimal and timely benefit from novel therapies, even in the absence of traditional registration trials for this small volume cancer indication. Methods: The GLOW study is a prospective diagnostic cohort study executed through collaboration of the Hartwig Medical Foundation (Hartwig, a non-profit organisation) and twelve Dutch centers that perform neurosurgery and/or treat GBM patients. A total of 200 patients with a first recurrence of a glioblastoma will be included. Dual primary endpoint is the percentage of patients who receive targeted therapy based on the WGS report and overall survival. Secondary endpoints include WGS report success rate and number of targeted treatments available based on WGS reports and number of patients starting a treatment in presence of an actionable variant. At recurrence, study participants will undergo SOC neurosurgical resection. Tumour material will then, together with a blood sample, be sent to Hartwig where it will be analysed by WGS. A diagnostic report with therapy guidance, including potential matching off-label drugs and available clinical trials will then be sent back to the treating physician for discussing of the results in molecular tumour boards and targeted treatment decision making. Discussion: The GLOW study aims to provide the scientific evidence for changing the SOC diagnostics for patients with a recurrent glioblastoma by investigating complete genome diagnostics to maximize treatment options for this patient group. Trial registration: ClinicalTrials.gov Identifier: NCT05186064. [ABSTRACT FROM AUTHOR]

The glial fibrillary acidic protein (GFAP) is a type III intermediate filament (IF) protein that is highly expressed in astrocytes, neural stem cells, and in gliomas. Gliomas are a heterogeneous group of primary brain tumors that arise from glia cells or neural stem cells and rely on accurate diagnosis for prognosis and treatment strategies. GFAP is differentially expressed between glioma subtypes and, therefore, often used as a diagnostic marker. However, GFAP is highly regulated by the process of alternative splicing; many different isoforms have been identified. Differential expression of GFAP isoforms between glioma subtypes suggests that GFAP isoform-specific analyses could benefit diagnostics. In this study we report on the differential expression of a new GFAP isoform between glioma subtypes, GFAPμ. A short GFAP transcript resulting from GFAP exon 2 skipping was detected by RNA sequencing of human glioma. We show that GFAPμ mRNA is expressed in healthy brain tissue, glioma cell lines, and primary glioma cells and that it translates into a ~21 kDa GFAP protein. 21 kDa GFAP protein was detected in the IF protein fraction isolated from human spinal cord as well. We further show that induced GFAPμ expression disrupts the GFAP IF network. The characterization of this new GFAP isoform adds on to the numerous previously identified GFAP splice isoforms. It emphasizes the importance of studying the contribution of IF splice variants to specialized functions of the IF network and to glioma research. [ABSTRACT FROM AUTHOR]