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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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Purpose: Standards for surgical decisions are unavailable, hence treatment decisions can be personalized, but also introduce variation in treatment and outcome. National registrations seek to monitor healthcare quality. The goal of the study is to measure between-hospital variation in risk-standardized survival outcome after glioblastoma surgery and to explore the association between survival and hospital characteristics in conjunction with patient-related risk factors., Methods: Data of 2,409 adults with first-time glioblastoma surgery at 14 hospitals were obtained from a comprehensive, prospective population-based Quality Registry Neuro Surgery in The Netherlands between 2011 and 2014. We compared the observed survival with patient-specific risk-standardized expected early (30-day) mortality and late (2-year) survival, based on age, performance, and treatment year. We analyzed funnel plots, logistic regression and proportional hazards models., Results: Overall 30-day mortality was 5.2% and overall 2-year survival was 13.5%. Median survival varied between 4.8 and 14.9 months among hospitals, and biopsy percentages ranged between 16 and 73%. One hospital had lower than expected early mortality, and four hospitals had lower than expected late survival. Higher case volume was related with lower early mortality (P = 0.031). Patient-related risk factors (lower age; better performance; more recent years of treatment) were significantly associated with longer overall survival. Of the hospital characteristics, longer overall survival was associated with lower biopsy percentage (HR 2.09, 1.34-3.26, P = 0.001), and not with academic setting, nor with case volume., Conclusions: Hospitals vary more in late survival than early mortality after glioblastoma surgery. Widely varying biopsy percentages indicate treatment variation. Patient-related factors have a stronger association with overall survival than hospital-related factors.