Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136 patients with pituitary tumors, annotated for tumor segmentation and accompanied by clinical, radiological and pathological metadata. This diverse dataset captures variations in tumor size, location, and pathological activity, essential for understanding this complex condition. Expert annotations of both the tumor and adjacent carotid arteries ensure precise delineation, facilitating the development of automated segmentation algorithms. Our initiative addresses the need for standardized data in pituitary oncology, fosters collaboration and innovation, and enables the development and benchmarking of workflows that utilize pituitary radiomics for treatment planning and outcome prediction.

The aim of this study was to investigate the effects of anticoagulation with DOACs and warfarin on the characteristics of chronic subdural hematomas (CSDHs), specifically, the size of the hematomas, the presence of midline shift and the effect on consciousness levels, measured via the Glasgow Coma Scale (GCS). A multi-centre retrospective case series analysis from January 2015 to May 2020 was conducted. Patients who were anticoagulated with DOACs and warfarin were of primary interest. The CSDH characteristics that were focussed on included the size of the CSDH, midline shift and GCS. Chi-squared analysis and independent t-tests were conducted for inter-variable analysis. Relative risk was also calculated. Two thousand, six hundred seventy-five patients across two tertiary neurosurgical units referred with CSDHs were included in the analysis. 1799 patients were male (67.3

This study aimed to describe the relationship between blood and CSF volumes in different compartments on baseline CT after aSAH, assess if they independently predict long-term outcome, and explore their interaction with age. CT scans from patients participating in a prospective multicenter randomized controlled trial of patients with aSAH were segmented for blood and CSF volumes. The primary outcomes were the mRS, and the Subarachnoid Hemorrhage Outcome Tool (SAHOT) at day 28 and 180. Univariate regressions were conducted to identify significant predictors of poor outcomes, followed by principal component analysis to explore correlations between imaging variables and WFNS. A multivariate predictive model was then developed and optimized using stepwise regression. CT scans from 97 patients with a median delay from symptom onset of 271 min (131–547) were analyzed. Univariate analysis showed only WFNS, and total blood volume (TBV) were significant predictors of both short and long-term outcome with WFNS more predictive of mRS and TBV more predictive of SAHOT. Principal component analysis showed strong dependencies between the imaging predictors. Multivariate ordinal regression showed models with WFNS alone were most predictive of day 180 mRS and models with TBV alone were most predictive of SAHOT. TBV was the most significant measured imaging predictor of poor long-term outcome after aSAH. All these imaging predictors are correlated, however, and may have multiple complex interactions necessitating larger datasets to detect if they provide any additional predictive value for long-term outcome.

Abstract Introduction Datathons are high-yield research events where cross-disciplinary researchers collaborate to investigate pertinent healthcare problems by analysing multimodal electronic patient data. This paper aims to demonstrate the value of a surgical datathon as a learning model in students using both objective and qualitative assessments. Methods Fifteen individuals were recruited for the 24-hour datathon with representation from medicine, psychology, and clinical neurosciences including two postgraduate neurosurgery faculty. All reported interest in learning more academic skills. The aim was to solve a pertinent neurosurgical challenge, namely the prediction of intracranial pressure based on brain MR-imaging, with the goal of completing a full manuscript within 24 hours. Participants were trained and split into smaller open working groups, supervised allowing real-time specialist feedback. Groups intermittently merged to cross-collaborate. Surveys with mixed-methods analysis explored participant opinions and the value of developing research skills at multiple time points. Results Participants’ knowledge were tested showing an increased mean test score post-datathon (69.92%) compared to pre-datathon scores (57.31%). Wilcoxon signed-rank test results demonstrated a significant change for these results (p=0.04, 95% CI -27.21 - -0.04). Likert-scales showed that majority of participants strongly agreed that the datathon accelerated their productivity (58.3%). Data collection was rated particularly useful (100%) despite majority being less than confident in analysing neuro-radiological images (84.6%) pre-datathon. Time pressures were the largest barrier participants faced (47.1%). Conclusion A full manuscript was produced and the datathon goal was met with conference acceptance. Surgical datathons represent a pragmatic learning model for students of all backgrounds.

Since the start of the pandemic, over 400 million COVID-19 swab tests have been conducted in the UK with a non-trivial number associated with skull base injury. Given the continuing use of nasopharyngeal swabs, further cases of swab-associated skull base injury are anticipated. We describe a 54-year-old woman presenting with persistent colourless nasal discharge for 2 weeks following a traumatic COVID-19 nasopharyngeal swab. A β2-transferrin test confirmed cerebrospinal fluid (CSF) rhinorrhoea and a high-resolution sinus computed tomography (CT) scan demonstrated a cribriform plate defect. Magnetic resonance imaging showed radiological features of idiopathic intracranial hypertension (IIH): a Yuh grade V empty sella and thinned anterior skull base. Twenty-four hour intracranial pressure (ICP) monitoring confirmed raised pressures, prompting insertion of a ventriculoperitoneal shunt. The patient underwent CT cisternography and endoscopic transnasal repair of the skull base defect using a fluorescein adjuvant, without complications. A systematic search was performed to identify cases of COVID-19 swab-related injury. Eight cases were obtained, of which three presented with a history of IIH. Two cases were complicated by meningitis and were managed conservatively, whereas six required endoscopic skull base repair and one had a ventriculoperitoneal shunt inserted. A low threshold for high-resolution CT scanning is suggested for patients presenting with rhinorrhoea following a nasopharyngeal swab. The literature review suggests an underlying association between IIH, CSF rhinorrhoea and swab-related skull base injury. We highlight a comprehensive management pathway for these patients, including high-resolution CT with cisternography, ICP monitoring, shunt and fluorescein-based endoscopic repair to achieve the best standard of care.

Abstract Aim Cognitive biases and heuristics represent mental shortcuts that can lead to errors in judgement. Both are noted in surgical settings and risk patient safety. Yet, up till now, the extent of biases in surgical settings has not been systematically reviewed. Method This PROSPERO registered (CRD42022334828) review was conducted in accordance with PRISMA guidelines. MEDLINE and four other major databases were searched from their inception to 28th August 2022 using “surg*” with “cognitive bia*” and “heuristics” terms. Original primary research studies in English were included, with blinded full text screening. Relevant risk of bias (RoB) tools were employed for each study. Results The search identified 26,640 papers. Following de-duplication and screening, 16 final papers were included. 34 biases were identified including three prospective experiments, three retrospective analyses, and 10 questionnaire studies. Confirmation and anchoring biases were the most represented. RoB analysis found broad heterogeneity, with four studies being deemed of high risk. All identified cognitive biases were found to influence relevant surgical outcomes and seven studies cited a negative impact on patient care. Conclusions Cognitive biases will continue to contribute to surgical errors and never events unless they are described and recognised with greater precision and de-biasing strategies are implemented. Of note, mindfulness-based training and deliberate reflection tested by two studies demonstrated reduced rates of surgical error. Nevertheless, our review demonstrates a lack of experimental studies which are crucial to understanding why and how biases contribute to negative outcomes and we suggest avenues for further research and systems change.

Purpose Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods A data-driven analysis was performed using all available electronic neurosurgical referrals (2014–2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. Conclusion This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.

Healthcare dashboards make key information about service and clinical outcomes available to staff in an easy-to-understand format. Most dashboards are limited to providing insights based on group-level inference, rather than individual prediction. Here, we evaluate a dashboard which could analyze and forecast acute neurosurgical referrals based on 10,033 referrals made to a large volume tertiary neurosciences center in central London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021. As anticipated, referral volumes significantly increased in this period, largely due to an increase in spinal referrals. Applying a range of validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point. Using a mixed-methods approach, we determined that the dashboard was usable, feasible, and acceptable to key stakeholders. Dashboards provide an effective way of visualizing acute surgical referral data and for predicting future volume without the need for data-science expertise.

"Designing undergraduate neurosurgical e-learning: medical students' perspective." British Journal of Neurosurgery, 33(1), p. 79

Cerebral white-matter injury is common in preterm-born infants and is associated with neurocognitive impairments. Identifying the pattern of connectivity changes in the brain following premature birth may provide a more comprehensive understanding of the neurobiology underlying these impairments. Here, we characterize whole-brain, macrostructural connectivity following preterm delivery and explore the influence of age and prematurity using a data-driven, nonsubjective analysis of diffusion magnetic resonance imaging data. T1- and T2-weighted and -diffusion MRI were obtained between 11 and 31 months postconceptional age in 49 infants, born between 25 and 35 weeks postconception. An optimized processing pipeline, combining anatomical, and tissue segmentations with probabilistic diffusion tractography, was used to map mean tract anisotropy. White-matter tracts where connection strength was related to age of delivery or imaging were identified using sparse-penalized regression and stability selection. Older children had stronger connections in tracts predominantly involving frontal lobe structures. Increasing prematurity at birth was related to widespread reductions in connection strength in tracts involving all cortical lobes and several subcortical structures. This nonsubjective approach to mapping whole-brain connectivity detected hypothesized changes in the strength of intracerebral connections during development and widespread reductions in connectivity strength associated with premature birth.