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.

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.

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.

Predictive value of the five-repetition sit-to-stand test for outcomes after surgery for lumbar disc herniation: prospective study.

OBJECTIVE: It is unknown whether presence of pre-operative objective functional impairment (OFI) can predict post-operative outcomes in patients with lumbar disc herniation (LDH). We aimed to determine whether pre-operative OFI measured by the five-repetition sit-to-stand test (5R-STS) could predict outcomes at 12-months post-discectomy. METHODS: Adult patients with LDH scheduled for surgery were prospectively recruited from a Dutch short-stay spinal clinic. The 5R-STS time and patient reported outcome measures (PROMs) including Oswestry Disability Index, Roland-Morris Disability Questionnaire, Visual Analogue Scale (VAS) for back and leg pain, EQ-5D-3L health-related quality of life, EQ5D-VAS and ability to work were recorded pre-operatively and at 12-months. A 5R-STS time cut-off of ≥ 10.5 s was used to determine OFI. Mann-Whitney and Chi-square tests were employed to determine significant differences in post-operative outcomes between groups stratified by presence of pre-operative OFI. RESULTS: We recruited 134 patients in a prospective study. Twelve-month follow-up was completed by 103 (76.8%) patients. Mean age was 53.2 ± 14.35 years and 50 (48.5%) patients were female. Pre-operatively, 53 (51.5%) patients had OFI and 50 (48.5%) did not. Post-operatively, patients with OFI experienced a significantly greater mean change (p < 0.001) across all PROMs compared to patients without OFI, except leg pain (p = 0.176). There were no significant differences in absolute PROMs between groups at 12-months (all p > 0.05). CONCLUSIONS: The presence of OFI based on 5R-STS time does not appear to decrease a patient's likelihood of experiencing satisfactory post-operative outcomes. The 5R-STS cannot predict how a patient with LDH will respond to surgery at 12-month follow-up.

Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.

OBJECTIVES: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.

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.

How we do it: the Zurich Microsurgery Lab technique for placenta preparation.

BACKGROUND: Perfused placentas provide an excellent and accessible model for microvascular dissection, microsuturing and microanastomosis training - particularly in the early microsurgical learning curve. This way, a significant amount of live animals can be spared. METHOD: We present the Zurich Microsurgery Lab protocol, detailing steps for obtaining, selecting, cleaning, flushing, cannulating, and preserving human placentas - as well as microsurgical training examples - in a tried-and-true, safe, cost-effective, and high-yield fashion. CONCLUSION: Our technique enables highly realistic microsurgical training (microdissection, microvascular repair, microanastomosis) based on readily available materials. Proper handling, preparation, and preservation of the perfused placenta models is key.

Promotion of a neurosurgical academic journal on social media: a 1-year experience.

BACKGROUND: Social media (SoMe) use, in all of its forms, has seen massively increased throughout the past two decades, including academic publishing. Many journals have established a SoMe presence, yet the influence of promotion of scientific publications on their visibility and impact remains poorly studied. The European Journal of Neurosurgery «Acta Neurochirurgica¼ has established its SoMe presence in form of a Twitter account that regularly promotes its publications. We aim to analyze the impact of this initial SoMe campaign on various alternative metrics (altmetrics). METHODS: A retrospective analysis of all articles published in the journal Acta Neurochirurgica between May 1st, 2018, and April 30th, 2020, was performed. These articles were divided into a historical control group - containing the articles published between May 1st, 2018, and April 30th, 2019, when the SoMe campaign was not yet established - and into an intervention group. Several altmetrics were analyzed, along with website visits and PDF downloads per month. RESULTS: In total, 784 articles published during the study period, 128 (16.3%) were promoted via Twitter. During the promotion period, 29.7% of published articles were promoted. Overall, the published articles reached a mean of 31.3 ± 50.5 website visits and 17.5 ± 31.25 PDF downloads per month. Comparing the two study periods, no statistically significant differences in website visits (26.91 ± 32.87 vs. 34.90 ± 61.08, p = 0.189) and PDF downloads (17.52 ± 31.25 vs. 15.33 ± 16.07, p = 0.276) were detected. However, overall compared to non-promoted articles, promoted articles were visited (48.9 ± 95.0 vs. 29.0 ± 37.0, p = 0.005) and downloaded significantly more (25.7 ± 66.7 vs. 16.6 ± 18.0, p = 0.045) when compared to those who were not promoted during the promotion period. CONCLUSIONS: We report a 1-year initial experience with promotion of a general neurosurgical journal on Twitter. Our data suggest a clear benefit of promotion on article site visits and article downloads, although no single responsible element could be determined in terms of altmetrics. The impact of SoMe promotion on other metrics, including traditional bibliometrics such as citations and journal impact factor, remains to be determined.

Influence of gender and sexual hormones on outcomes after pituitary surgery: a systematic review and meta-analysis.

BACKGROUND: Although there is an increasing body of evidence showing gender differences in various medical domains as well as presentation and biology of pituitary adenoma (PA), gender differences regarding outcome of patients who underwent transsphenoidal resection of PA are poorly understood. The aim of this study was to identify gender differences in PA surgery. METHODS: The PubMed/MEDLINE database was searched up to April 2023 to identify eligible articles. Quality appraisal and extraction were performed in duplicate. RESULTS: A total of 40 studies including 4989 patients were included in this systematic review and meta-analysis. Our analysis showed odds ratio of postoperative biochemical remission in males vs. females of 0.83 (95% CI 0.59-1.15, P = 0.26), odds ratio of gross total resection in male vs. female patients of 0.68 (95% CI 0.34-1.39, P = 0.30), odds ratio of postoperative diabetes insipidus in male vs. female patients of 0.40 (95% CI 0.26-0.64, P < 0.0001), and a mean difference of preoperative level of prolactin in male vs. female patients of 11.62 (95% CI - 119.04-142.27, P = 0.86). CONCLUSIONS: There was a significantly higher rate of postoperative DI in female patients after endoscopic or microscopic transsphenoidal PA surgery, and although there was some data in isolated studies suggesting influence of gender on postoperative biochemical remission, rate of GTR, and preoperative prolactin levels, these findings could not be confirmed in this meta-analysis and demonstrated no statistically significant effect. Further research is needed and future studies concerning PA surgery should report their data by gender or sexual hormones and ideally further assess their impact on PA surgery.

Differences between real-world and score-based decision-making in the microsurgical management of patients with unruptured intracranial aneurysms.

BACKGROUND: Management of unruptured intracranial aneurysms (UIAs) is complex, balancing the risk of rupture and risk of treatment. Therefore, prediction scores have been developed to support clinicians in the management of UIAs. We analyzed the discrepancies between interdisciplinary cerebrovascular board decision-making factors and the results of the prediction scores in our cohort of patients who received microsurgical treatment of UIAs. METHODS: Clinical, radiological, and demographical data of 221 patients presenting with 276 microsurgically treated aneurysms were collected, from January 2013 to June 2020. UIATS, PHASES, and ELAPSS were calculated for each treated aneurysm, resulting in subgroups favoring treatment or conservative management for each score. Cerebrovascular board decision-factors were collected and analyzed. RESULTS: UIATS, PHASES, and ELAPSS recommended conservative management in 87 (31.5%) respectively in 110 (39.9%) and in 81 (29.3%) aneurysms. The cerebrovascular board decision-factors leading to treatment in these aneurysms (recommended to manage conservatively in the three scores) were: high life expectancy/young age (50.0%), angioanatomical factors (25.0%), multiplicity of aneurysms (16.7%). Analysis of cerebrovascular board decision-making factors in the "conservative management" subgroup of the UIATS showed that angioanatomical factors (P=0.001) led more frequently to surgery. PHASES and ELAPSS subgroups "conservative management" were more frequently treated due to clinical risk factors (P=0.002). CONCLUSIONS: Our analysis showed more aneurysms were treated based on "real-world" decision-making than recommended by the scores. This is because these scores are models trying to reproduce reality, which is yet not fully understood. Aneurysms, which were recommended to manage conservatively, were treated mainly because of angioanatomy, high life expectancy, clinical risk factors, and patient's treatment wish. The UIATS is suboptimal regarding assessment of angioanatomy, the PHASES regarding clinical risk factors, complexity, and high life expectancy, and the ELAPSS regarding clinical risk factors and multiplicity of aneurysms. These findings support the need to optimize prediction models of UIAs.

Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival.

The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology's unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical-clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

BACKGROUND AND OBJECTIVES: In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data. METHODS: We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. RESULTS: In the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service. DISCUSSION: We show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas. METHODS: For the development of the preoperative segmentation model (U-Net), MRI scans of 1053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 as well as from patients who underwent surgery at the University Hospital in Zurich were used. Subsequently, the model was evaluated on a holdout set containing 285 images from the same sources. The postoperative model was developed using 72 scans and validated on 45 scans obtained from the BraTS 2015 and Zurich dataset. Performance is evaluated using Dice Similarity score, Jaccard coefficient and Hausdorff 95%. RESULTS: We were able to achieve an overall mean Dice Similarity Score of 0.59 and 0.29 on the pre- and postoperative holdout sets, respectively. Our algorithm managed to determine correct EOR in 44.1%. CONCLUSION: Although our models are not suitable for clinical use at this point, the possible applications are vast, going from automated lesion detection to disease progression evaluation. Precise determination of EOR is a challenging task, but we managed to show that deep learning can provide fast and objective estimates.

Predictors of five-repetition sit-to-stand test performance in patients with lumbar degenerative disease.

BACKGROUND: The five-repetition sit-to-stand test (5R-STS) has recently been validated as an objective measure of functional impairment in patients with lumbar degenerative disease (LDD). Knowledge of factors influencing 5R-STS performance is useful to correct for confounders, create personalized adjusted test times, and potentially identify prognostic subgroups. We evaluate factors predicting the 5R-STS performance in patients with LDD. METHODS: Patients with LDD requiring surgery were included. Each participant performed the 5R-STS and completed a questionnaire that included their age, gender, weight, height, body mass index (BMI), smoking status, education level, employment type, ability to work, analgesic drug usage, history of previous spinal surgery, and EQ5D depression and anxiety domain. Surgical indication and index level of the spinal pathology were also recorded. Predictors of 5R-STS were identified through multivariable linear regression. RESULTS: The cohort consisted of 240 patients, 47.9% being female (mean age, 47.7 ± 13.6 years). In the final multivariable model incorporating confounders, height (regression coefficient (RC), 0.08; 95% confidence interval (CI), 0.003/0.16, p = 0.042) and being an active smoker (RC, 2.44; 95%CI, 0.56/4.32, p = 0.012) were significant predictors of worse 5R-STS performance. Full ability to work (RC, - 2.39; 95%CI, - 4.39/ - 0.39, p = 0.020) was associated with a better 5R-STS performance. Age, height, surgical indication, index level of pathology, history of previous spine surgery, history of pain, analgesic drug use, employment type, and severity of anxiety and depression symptoms demonstrated confounding effect on the 5R-STS time. CONCLUSIONS: Greater height, being an active smoker, and inability to work are significant predictors of worse 5R-STS performance in patients with LDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03303300 and NCT03321357.