Basal cisternostomy as an adjunct to decompressive hemicraniectomy in moderate to severe traumatic brain injury: a systematic review and meta-analysis.

BACKGROUND: Basal cisternostomy (BC) is a surgical technique to reduce intracranial hypertension following moderate to severe traumatic brain injury (TBI). As the efficacy and safety of BC in patients with TBI has not been well-studied, we aim to summarize the published evidence on the effect of BC as an adjunct to decompressive hemicraniectomy (DHC) on clinical outcome following moderate to severe TBI. METHODS: A systematic literature review was carried out in PubMed/MEDLINE and EMBASE to identify studies evaluating BC as an adjunct to decompressive hemicraniectomy (DHC) in moderate to severe TBI. Random effects meta-analysis was performed to calculate summary effect estimates. RESULTS: Eight studies reporting on 1345 patients were included in the qualitative analysis, of which five (1206 patients) were considered for meta-analysis. Overall, study quality was low and clinical heterogeneity was high. Adjuvant BC (BC + DHC) compared to standalone DHC was associated with a reduction in the length of stay in the ICU (Mean difference [MD]: -3.25 days, 95% CI: -5.41 to -1.09 days, p = 0.003), significantly lower mean brain outward herniation (MD: -0.68 cm, 95% CI: -0.90 to -0.46 cm, p < 0.001), reduced odds of requiring osmotherapy (OR: 0.09, 95% CI: 0.02 to 0.41, p = 0.002) as well as decreased odds of mortality at discharge (OR 0.68, 95% CI: 0.4 to 0.96, p = 0.03). Adjuvant BC compared to DHC did not result in higher odds of a favourable neurological outcome (OR = 2.50, 95% CI: 0.95-6.55, p = 0.06) and did not affect mortality at final follow-up (OR: 0.80, 95% CI: 0.17 to 3.74, p = 0.77). CONCLUSION: There is insufficient data to demonstrate a potential beneficial effect of adjuvant BC. Despite some evidence for reduced mortality and length of stay, there is no effect on neurological outcome. However, these results need to be interpreted with caution as they carry a high risk of bias due to overall scarcity of published clinical data, technical variations, methodological differences, limited cohort sizes, and a considerable heterogeneity in study design and reported outcomes.

A randomized controlled trial of social media promotion in neurosurgical publishing.

The importance of social media has seen a dramatic increase in recent times, but much about its influence in academia is still unknown. To date, no comparative studies analysing the effect of social media promotion on citation counts have been undertaken in neurosurgical publishing. We randomized 177 articles published in Acta Neurochirurgica from May to September 2020. The 89 articles in the intervention group received a standardized social media promotion through one post on our official Twitter/X account, whereas the 88 articles in the control group did not receive any social media promotion. Citation counts, website visits and PDF downloads were tracked at one and two years post-promotion. We found no significant difference in number of citations at one year post-promotion (Intervention: 1.85 ± 3.94 vs. Control: 2.67 ± 6.65, p = 0.322) or at two years (5.35 ± 7.39 vs. 7.09 ± 12.1, p = 0.249). Similarly, no difference was detected in website visits at one (587.46 ± 568.04 vs. 590.65 ± 636.25, p = 0.972) or two years (865.79 ± 855.80 vs. 896.31 ± 981.97, p = 0.826) and PDF downloads at one (183.40 ± 152.02 vs. 187.78 ± 199.01, p = 0.870) or two years (255.99 ± 218.97 vs. 260.97 ± 258.44, p = 0.890). In a randomized study, a structured promotion of general neurosurgical articles on Twitter/X did not significantly impact citation count, website visits, or PDF downloads compared to no social media promotion. Combined with published evidence to date, the impact of social media on citation counts in academic publishing ultimately remains unclear.

Multiple craniotomies in patients with brain metastases: a two-center, propensity score-matched study.

The current study addresses the question of whether the resection of more than one BM by multiple craniotomies within the same operation is associated with more adverse events (AEs) and worse functional outcome compared to cases in which only one BM was resected. All patients who underwent more than one craniotomy for resection of multiple BM at two Swiss tertiary neurosurgical care centers were included. Any AEs, functional outcome, and overall survival (OS) were analyzed after 1:1 propensity score matching with patients who underwent removal of a single BM only. A total of 94 patients were included in the final study cohort (47 of whom underwent multiple craniotomies). There was no significant difference in the incidence of AEs between the single and the multiple craniotomy group (n = 2 (4.3%) vs. n = 4 (8.5%), p = .7). Change in modified Rankin Scale (mRS) and Karnofsky Performance Status (KPS) at discharge demonstrated that slightly more single craniotomy patients improved in mRS, while the proportion of patients who worsened in mRS (16.3 vs. 16.7%) and KPS (13.6 vs. 15.2%) was similar in both groups (p = .42 for mRS and p = .92 for KPS). Survival analysis showed no significant differences in OS between patients with single and multiple craniotomies (p = .18). Resection of multiple BM with more than one craniotomy may be considered a safe option without increased AEs or worse functional outcome.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease.

BACKGROUND: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity. RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.

Role of microsurgical tumor burden reduction in patients with breast cancer brain metastases considering molecular subtypes: a two-center volumetric survival analysis.

BACKGROUND: Advancements in metastatic breast cancer (BC) treatment have enhanced overall survival (OS), leading to increased rates of brain metastases (BM). This study analyzes the association between microsurgical tumor reduction and OS in patients with BCBM, considering tumor molecular subtypes and perioperative treatment approaches. METHODS: Retrospective analysis of surgically treated patients with BCBM from two tertiary brain tumor Swiss centers. The association of extent of resection (EOR), gross-total resection (GTR) achievement, and postoperative residual tumor volume (RV) with OS and intracranial progression-free survival (IC-PFS) was evaluated using Cox proportional hazard model. RESULTS: 101 patients were included in the final analysis, most patients (38%) exhibited HER2-/HR + BC molecular subtype, followed by HER2 + /HR + (25%), HER2-/HR- (21%), and HER2 + /HR- subtypes (13%). The majority received postoperative systemic treatment (75%) and radiotherapy (84%). Median OS and intracranial PFS were 22 and 8 months, respectively. The mean pre-surgery intracranial tumor volume was 26 cm3, reduced to 3 cm3 post-surgery. EOR, GTR achievement and RV were not significantly associated with OS or IC-PFS, but higher EOR and lower RV correlated with extended OS in patients without extracranial metastases. HER2-positive tumor status was associated with longer OS, extracranial metastases at BM diagnosis and symptomatic lesions with shorter OS and IC-PFS. CONCLUSIONS: Our study found that BC molecular subtypes, extracranial disease status, and BM-related symptoms were associated with OS in surgically treated patients with BCBM. Additionally, while extensive resection to minimize residual tumor volume did not significantly affect OS across the entire cohort, it appeared beneficial for patients without extracranial metastases.

Challenges and Considerations of Preclinical Development for iPSC-Based Myogenic Cell Therapy.

Cell therapies derived from induced pluripotent stem cells (iPSCs) offer a promising avenue in the field of regenerative medicine due to iPSCs' expandability, immune compatibility, and pluripotent potential. An increasing number of preclinical and clinical trials have been carried out, exploring the application of iPSC-based therapies for challenging diseases, such as muscular dystrophies. The unique syncytial nature of skeletal muscle allows stem/progenitor cells to integrate, forming new myonuclei and restoring the expression of genes affected by myopathies. This characteristic makes genome-editing techniques especially attractive in these therapies. With genetic modification and iPSC lineage specification methodologies, immune-compatible healthy iPSC-derived muscle cells can be manufactured to reverse the progression of muscle diseases or facilitate tissue regeneration. Despite this exciting advancement, much of the development of iPSC-based therapies for muscle diseases and tissue regeneration is limited to academic settings, with no successful clinical translation reported. The unknown differentiation process in vivo, potential tumorigenicity, and epigenetic abnormality of transplanted cells are preventing their clinical application. In this review, we give an overview on preclinical development of iPSC-derived myogenic cell transplantation therapies including processes related to iPSC-derived myogenic cells such as differentiation, scaling-up, delivery, and cGMP compliance. And we discuss the potential challenges of each step of clinical translation. Additionally, preclinical model systems for testing myogenic cells intended for clinical applications are described.

Metabolic and inflammatory parameters in relation to baseline characterization and treatment outcome in patients with prolactinoma: insights from a retrospective cohort study at a single tertiary center.

Background: Prolactinomas (PRLs) are prevalent pituitary adenomas associated with metabolic changes and increased cardiovascular morbidity. This study examined clinical, endocrine, metabolic, and inflammatory profiles in PRL patients, aiming to identify potential prognostic markers. Methods: The study comprised data from 59 PRL patients gathered in a registry at the University Hospital of Zurich. Diagnostic criteria included MRI findings and elevated serum prolactin levels. We assessed baseline and follow-up clinical demographics, metabolic markers, serum inflammation-based scores, and endocrine parameters. Treatment outcomes were evaluated based on prolactin normalization, tumor shrinkage, and cabergoline dosage. Results: The PRL cohort exhibited a higher prevalence of overweight/obesity, prediabetes/diabetes mellitus, and dyslipidemia compared to the general population. Significant correlations were found between PRL characteristics and BMI, HbA1c, and fT4 levels. Follow-up data indicated decreases in tumor size, tumor volume, prolactin levels, and LDL-cholesterol, alongside increases in fT4 and sex hormones levels. No significant associations were observed between baseline parameters and tumor shrinkage at follow-up. A positive association was noted between PRL size/volume and the time to achieve prolactin normalization, and a negative association with baseline fT4 levels. Conclusion: This study underscores the metabolic significance of PRL, with notable correlations between PRL parameters and metabolic indices. However, inflammatory markers were not significantly correlated with patient stratification or outcome prediction. These findings highlight the necessity for standardized follow-up protocols and further research into the metabolic pathogenesis in PRL patients.

Virtual Reality Planning of Microvascular Decompression in Trigeminal Neuralgia: Technique and Clinical Outcome.

BACKGROUND: A neurovascular conflict (NVC) is considered the cause of trigeminal neuralgia (TN) in 75% of cases, and if so, a microvascular decompression (MVD) can lead to significant pain relief. A reliable preoperative detection of NVC is essential for clinical decision-making and surgical planning, making detailed neuroradiologic imaging an important component. We present our experiences and clinical outcomes with preoperative planning of the MVD procedure in a virtual reality (VR) environment, based on magnetic resonance imaging (MRI) including magnetic resonance angiography (MRA) and magnetic resonance venography (MRV) sequences. METHODS: We analyzed the data of 30 consecutive MVDs in patients treated for TN, in a retrospective single-surgeon (R.A. Kockro) study. Out of the 30 cases, 26 were included. Preoperatively, MRA/MRV and MRI series were fused and three dimensionally reconstructed in a VR environment. All critical structures such as the trigeminal nerve as well as the arteries and veins of the cerebellopontine angle, the brainstem, the neighboring cranial nerves, and the transverse and sigmoid sinus were segmented. The NVC was visualized and a simulation of a retrosigmoid approach, with varying trajectories, to the NVC was performed. The intraoperative findings were then compared with the data of the simulation. The clinical outcome was assessed by a detailed review of medical reports, and follow-up-interviews were conducted in all available patients (20/26). RESULTS: The VR planning was well integrated into the clinical workflow, and imaging processing time was 30 to 40 minutes. There was a sole arterial conflict in 13 patients, a venous conflict in 4 patients, and a combined arteriovenous conflict in 9 patients. The preoperative simulations provided a precise visualization of the anatomical relationships of the offending vessels and the trigeminal nerves as well as the surrounding structures. For each case, the approach along the most suitable surgical corridor was simulated and the exact steps of the decompression were planned. The NVC and the anatomy of the cerebellopontine angle as seen intraoperatively matched with the preoperative simulations in all cases and the MVC could be performed as planned. At follow-up, 92.3% (24/26) of patients were pain free and all the patients who completed the questionnaire would undergo the surgery again (20/20). The surgical complication rate was zero. CONCLUSION: Current imaging technology allows detailed preoperative visualization of the pathoanatomical spatial relationships in cases of TN. 3D interactive VR technology allows establishing a clear dissection and decompression strategy, resulting in safe vascular microsurgery and excellent clinical results.

From molecular signatures to radiomics: tailoring neurooncological strategies through forecasting of glioma growth.

OBJECTIVE: Contemporary oncological paradigms for adjuvant treatment of low- and intermediate-grade gliomas are often guided by a limited array of parameters, overlooking the dynamic nature of the disease. The authors' aim was to develop a comprehensive multivariate glioma growth model based on multicentric data, to facilitate more individualized therapeutic strategies. METHODS: Random slope models with subject-specific random intercepts were fitted to a retrospective cohort of grade II and III gliomas from the database at Kepler University Hospital (n = 191) to predict future mean tumor diameters. Deep learning-based radiomics was used together with a comprehensive clinical dataset and evaluated on an external prospectively collected validation cohort from University Hospital Zurich (n = 9). Prediction quality was assessed via mean squared prediction error. RESULTS: A mean squared prediction error of 0.58 cm for the external validation cohort was achieved, indicating very good prognostic value. The mean ± SD time to adjuvant therapy was 28.7 ± 43.3 months and 16.1 ± 14.6 months for the training and validation cohort, respectively, with a mean of 6.2 ± 5 and 3.6 ± 0.7, respectively, for number of observations. The observed mean tumor diameter per year was 0.38 cm (95% CI 0.25-0.51) for the training cohort, and 1.02 cm (95% CI 0.78-2.82) for the validation cohort. Glioma of the superior frontal gyrus showed a higher rate of tumor growth than insular glioma. Oligodendroglioma showed less pronounced growth, anaplastic astrocytoma-unlike anaplastic oligodendroglioma-was associated with faster tumor growth. Unlike the impact of extent of resection, isocitrate dehydrogenase (IDH) had negligible influence on tumor growth. Inclusion of radiomics variables significantly enhanced the prediction performance of the random slope model used. CONCLUSIONS: The authors developed an advanced statistical model to predict tumor volumes both pre- and postoperatively, using comprehensive data prior to the initiation of adjuvant therapy. Using radiomics enhanced the precision of the prediction models. Whereas tumor extent of resection and topology emerged as influential factors in tumor growth, the IDH status did not. This study emphasizes the imperative of advanced computational methods in refining personalized low-grade glioma treatment, advocating a move beyond traditional paradigms.

Safety and Effectiveness of an Enhanced Recovery Protocol in Patients Undergoing Burr Hole Evacuation for Chronic Subdural Hematoma.

BACKGROUND AND OBJECTIVES: Enhanced recovery programs may be especially useful in patients with chronic subdural hematoma or hygroma (cSDH), who frequently exhibit frailty and multimorbidity. We aim to evaluate the real-world safety and effectiveness of an enhanced recovery protocol in this population. METHODS: From a prospective registry, burr hole evacuations for cSDH carried out under the protocol (including early thromboprophylaxis, no flat bed rest, early mobilization without drain clamping, and early resumption of antithrombotic medication) were extracted, along with those procedures carried out within the past year before protocol change. Propensity score-based matching was carried out. A range of clinical and imaging outcomes were analyzed, including modified Rankin Scale as effectiveness and Clavien-Dindo adverse event grading as safety primary end points. RESULTS: Per group, 91 procedures were analyzed. At discharge, there was no significant difference in the modified Rankin Scale among the standard and enhanced recovery groups (1 [1; 2] vs 1 [1; 3], P = .552), or in Clavien-Dindo adverse event grading classifications of adverse events (P = .282) or occurrence of any adverse events (15.4% vs 20.9%, P = .442). There were no significant differences in time to drain removal (2.00 [2.00; 2.00] vs 2.00 [1.25; 2.00] days, P = .058), time from procedure to discharge (4.0 [3.0; 6.0] vs 4.0 [3.0; 6.0] days, P = .201), or total hospital length of stay (6.0 [5.0; 9.0] vs 5.0 [4.0; 8.0] days, P = .113). All-cause mortality was similar in both groups (8.8% vs 4.4%, P = .289), as was discharge disposition (P = .192). Other clinical and imaging outcomes were similar too (all P > .05). CONCLUSION: In a matched cohort study comparing perioperative standard of care with a novel enhanced recovery protocol focusing on evidence-based drainage, mobilization, and thromboprophylaxis regimens as well as changes to the standardized reuptake of oral anticoagulants and antiaggregants, no differences in safety or effectiveness were observed after burr hole evacuation of cSDH.

Whole Spine Segmentation Using Object Detection and Semantic Segmentation.

OBJECTIVE: Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis. METHODS: Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets. RESULTS: Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively. CONCLUSION: We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.

TomoRay: Generating Synthetic Computed Tomography of the Spine From Biplanar Radiographs.

OBJECTIVE: Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively. METHODS: A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS). RESULTS: At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively. CONCLUSION: Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.

Intraoperative ultrasonography in microsurgical resection of vestibular schwannomas via retrosigmoid approach: surgical technique and proof-of-concept illustrative case series.

PURPOSE: Intraoperative ultrasonography (ioUS) is an established tool for the real-time intraoperative orientation and resection control in intra-axial oncological neurosurgery. Conversely, reports about its implementation in the resection of vestibular schwannomas (VS) are scarce. The aim of this study is to describe the role of ioUS in microsurgical resection of VS. METHODS: ioUS (Craniotomy Transducer N13C5, BK5000, B Freq 8 MHz, BK Medical, Burlington, MA, USA) is integrated into the surgical workflow according to a 4-step protocol (transdural preresection, intradural debulking control, intradural resection control, transdural postclosure). Illustrative cases of patients undergoing VS resection through a retrosigmoid approach with the use of ioUS are showed to illustrate advantages and pitfalls of the technique. RESULTS: ioUS allows clear transdural identification of the VS and its relationships with surgically relevant structures of the posterior fossa and of the cerebellopontine cistern prior to dural opening. Intradural ioUS reliably estimates the extent of tumor debulking, thereby helping in the choice of the right moment to start peripheral preparation and in the optimization of the extent of resection in those cases where subtotal resection is the ultimate goal of surgery. Transdural postclosure ioUS accurately depicts surgical situs. CONCLUSION: ioUS is a cost-effective, safe, and easy-to-use intraoperative adjunctive tool that can provide a significant assistance during VS surgery. It can potentially improve patient safety and reduce complication rates. Its efficacy on clinical outcomes, operative time, and complication rate should be validated in further studies.

A critical moment in machine learning in medicine: on reproducible and interpretable learning.

Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.

Microsurgical management of midbrain gliomas: surgical results and long-term outcome in a large, single-surgeon, consecutive series.

OBJECTIVE: The authors report on a large, consecutive, single-surgeon series of patients undergoing microsurgical removal of midbrain gliomas. Emphasis is put on surgical indications, technique, and results as well as long-term oncological follow-up. METHODS: A retrospective analysis was performed of prospectively collected data from a consecutive series of patients undergoing microneurosurgery for midbrain gliomas from March 2006 through June 2022 at the authors' institution. According to the growth pattern and location of the lesion in the midbrain (tegmentum, central mesencephalic structures, and tectum), one of the following approaches was chosen: transsylvian (TS), extreme anterior interhemispheric transcallosal (eAIT), posterior interhemispheric transtentorial subsplenial (PITS), paramedian supracerebellar transtentorial (PST), perimedian supracerebellar (PeS), perimedian contralateral supracerebellar (PeCS), and transuvulotonsillar fissure (TUTF). Clinical and radiological data were gathered according to a standard protocol and reported according to common descriptive statistics. The main outcomes were rate of gross-total resection; extent of resection; occurrence of any complications; variation in Karnofsky Performance Status score at discharge, 3 months, and last follow-up; progression-free survival (PFS); and overall survival (OS). RESULTS: Fifty-four patients (28 of them pediatric) met the inclusion criteria (6 with high-grade and 48 with low-grade gliomas [LGGs]). Twenty-two tumors were in the tegmentum, 7 in the central mesencephalic structures, and 25 in the tectum. In no instance did the glioma originate in the cerebral peduncle. TS was performed in 2 patients, eAIT in 6, PITS in 23, PST in 16, PeS in 4, PeCS in 1, and TUTF in 2 patients. Gross-total resection was achieved in 39 patients (72%). The average extent of resection was 98.0% (median 100%, range 82%-100%). There were no deaths due to surgery. Nine patients experienced transient and 2 patients experienced permanent new neurological deficits. At a mean follow-up of 72 months (median 62, range 3-193 months), 49 of the 54 patients were still alive. All patients with LGGs (48/54) were alive with no decrease in their KPS score, whereas 42 showed improvement compared with their preoperative status. CONCLUSIONS: Microneurosurgical removal of midbrain gliomas is feasible with good surgical results and long-term clinical outcomes, particularly in patients with LGGs. As such, microneurosurgery should be considered as the first therapeutic option. Adequate microsurgical technique and anesthesiological management, along with an accurate preoperative understanding of the tumor's exact topographic origin and growth pattern, is crucial for a good surgical outcome.

Automated volumetric assessment of pituitary adenoma.

PURPOSE: Assessment of pituitary adenoma (PA) volume and extent of resection (EOR) through manual segmentation is time-consuming and likely suffers from poor interrater agreement, especially postoperatively. Automated tumor segmentation and volumetry by use of deep learning techniques may provide more objective and quick volumetry. METHODS: We developed an automated volumetry pipeline for pituitary adenoma. Preoperative and three-month postoperative T1-weighted, contrast-enhanced magnetic resonance imaging (MRI) with manual segmentations were used for model training. After adequate preprocessing, an ensemble of convolutional neural networks (CNNs) was trained and validated for preoperative and postoperative automated segmentation of tumor tissue. Generalization was evaluated on a separate holdout set. RESULTS: In total, 193 image sets were used for training and 20 were held out for validation. At validation using the holdout set, our models (preoperative / postoperative) demonstrated a median Dice score of 0.71 (0.27) / 0 (0), a mean Jaccard score of 0.53 ± 0.21/0.030 ± 0.085 and a mean 95th percentile Hausdorff distance of 3.89 ± 1.96./12.199 ± 6.684. Pearson's correlation coefficient for volume correlation was 0.85 / 0.22 and -0.14 for extent of resection. Gross total resection was detected with a sensitivity of 66.67% and specificity of 36.36%. CONCLUSIONS: Our volumetry pipeline demonstrated its ability to accurately segment pituitary adenomas. This is highly valuable for lesion detection and evaluation of progression of pituitary incidentalomas. Postoperatively, however, objective and precise detection of residual tumor remains less successful. Larger datasets, more diverse data, and more elaborate modeling could potentially improve performance.

Mixed Reality for Cranial Neurosurgical Planning: A Single-Center Applicability Study With the First 107 Subsequent Holograms.

BACKGROUND AND OBJECTIVES: Mixed reality (MxR) benefits neurosurgery by improving anatomic visualization, surgical planning and training. We aim to validate the usability of a dedicated certified system for this purpose. METHODS: All cases prepared with MxR in our center in 2022 were prospectively collected. Holographic rendering was achieved using an incorporated fully automatic algorithm in the MxR application, combined with contrast-based semiautomatic rendering and/or manual segmentation where necessary. Hologram segmentation times were documented. Visualization during surgical preparation (defined as the interval between finalized anesthesiological induction and sterile draping) was performed using MxR glasses and direct streaming to a side screen. Surgical preparation times were compared with a matched historical cohort of 2021. Modifications of the surgical approach after 3-dimensional (3D) visualization were noted. Usability was assessed by evaluating 7 neurosurgeons with more than 3 months of experience with the system using a Usefulness, Satisfaction and Ease of use (USE) questionnaire. RESULTS: One hundred-seven neurosurgical cases prepared with a 3D hologram were collected. Surgical indications were oncologic (63/107, 59%), cerebrovascular (27/107, 25%), and carotid endarterectomy (17/107, 16%). Mean hologram segmentation time was 39.4 ± 20.4 minutes. Average surgical preparation time was 48.0 ± 17.3 minutes for MxR cases vs 52 ± 17 minutes in the matched 2021 cohort without MxR (mean difference 4, 95% CI 1.7527-9.7527). Based on the 3D hologram, the surgical approach was modified in 3 cases. Good usability was found by 57% of the users. CONCLUSION: The perioperative use of 3D holograms improved direct anatomic visualization while not significantly increasing intraoperative surgical preparation time. Usability of the system was adequate. Further technological development is necessary to improve the automatic algorithms and reduce the preparation time by circumventing manual and semiautomatic segmentation. Future studies should focus on quantifying the potential benefits in teaching, training, and the impact on surgical and functional outcomes.

Safety of microneurosurgical interventions for superficial and deep-seated brain metastases: single-center cohort study of 637 consecutive cases.

PURPOSE: Microneurosurgical techniques have greatly improved over the past years due to the introduction of new technology and surgical concepts. To reevaluate the role of micro-neurosurgery in brain metastases (BM) resection in the era of new systemic and local treatment options, its safety profile needs to be reassessed. The aim of this study was to analyze the rate of adverse events (AEs) according to a systematic, comprehensive and reliably reproducible grading system after microneurosurgical BM resection in a large and modern microneurosurgical series with special emphasis on anatomical location. METHODS: Prospectively collected cases of BM resection between 2013 and 2022 were retrospectively analyzed. Number of AEs, defined as any deviations from the expected postoperative course according to Clavien-Dindo-Grade (CDG) were evaluated. Patient, surgical, and lesion characteristics, including exact anatomic tumor locations, were analyzed using uni- and multivariate logistic regression and survival analysis to identify predictive factors for AEs. RESULTS: We identified 664 eligible patients with lung cancer being the most common primary tumor (44%), followed by melanoma (25%) and breast cancer (11%). 29 patients (4%) underwent biopsy only whereas BM were resected in 637 (96%) of cases. The overall rate of AEs was 8% at discharge. However, severe AEs (≥ CDG 3a; requiring surgical intervention under local/general anesthesia or ICU treatment) occurred in only 1.9% (n = 12) of cases with a perioperative mortality of 0.6% (n = 4). Infratentorial tumor location (OR 5.46, 95% 2.31-13.8, p = .001), reoperation (OR 2.31, 95% 1.07-4.81, p = .033) and central region tumor location (OR 3.03, 95% 1.03-8.60) showed to be significant predictors in a multivariate analysis for major AEs (CDG ≥ 2 or new neurological deficits). Neither deep supratentorial nor central region tumors were associated with more major AEs compared to convexity lesions. CONCLUSIONS: Modern microneurosurgical resection can be considered an excellent option in the management of BM in terms of safety, as the overall rate of major AEs are very rare even in eloquent and deep-seated lesions.

Development and external validation of clinical prediction models for pituitary surgery.

Introduction: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63-0.80) for GTR, 0.69 (0.52-0.83) for BR, as well as 0.82 (0.76-0.89) for IMP. Discussion and conclusion: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient.