Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time. Previous research has focused solely on segmentation tasks without assessment of impact and usability of deep learning solutions in clinical practice. Herein, we demonstrate a three-dimensional convolutional neural network (3D-CNN) that performs expert-level, automated meningioma segmentation and volume estimation on MRI scans. A 3D-CNN was initially trained by segmenting entire brain volumes using a dataset of 10,099 healthy brain MRIs. Using transfer learning, the network was then specifically trained on meningioma segmentation using 806 expert-labeled MRIs. The final model achieved a median performance of 88.2% reaching the spectrum of current inter-expert variability (82.6–91.6%). We demonstrate in a simulated clinical scenario that a deep learning approach to meningioma segmentation is feasible, highly accurate and has the potential to improve current clinical practice.

OBJECTIVE: Patient-reported outcome measures (PROMs) are currently the gold standard to evaluate patient physical performance and ability to recover after spine surgery. However, PROMs have significant limitations due to the qualitative and subjective nature of the information reported as well as the impossibility of using this method in a continuous manner. The smartphone global positioning system (GPS) can be used to provide continuous, quantitative, and objective information on patient mobility. The aim of this study was to use daily mobility features derived from the smartphone GPS to characterize the perioperative period of patients undergoing spine surgery and to compare these objective measurements to PROMs, the current gold standard. METHODS: Eight daily mobility features were derived from smartphone GPS data in a population of 39 patients undergoing spine surgery for a period of 2 months starting 3weeks before surgery. In parallel, three different PROMs for pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]) and functional status (Patient-Reported Outcomes Measurement Information System [PROMIS]) were serially measured. Segmented linear regression analysis was used to assess trends before and after surgery. The Student paired t-test was used to compare pre- and postoperative PROM scores. Pearson’s correlation was calculated between the daily average of each GPS-based mobility feature and the daily average of each PROM score during the recovery period. RESULTS: Smartphone GPS features provided data documenting a reduction in mobility during the immediate postoperative period, followed by a progressive and steady increase with a return to baseline mobility values 1 month after surgery. PROMs measuring pain, physical performance, and disability were significantly different 1 month after surgery compared to the 2 immediate preoperative weeks. The GPS-based features presented moderate to strong linear correlation with pain VAS and PROMIS physical score during the recovery period (Pearson r > 0.7), whereas the ODI and PROMIS mental scores presented a weak correlation (Pearson r approximately 0.4). CONCLUSIONS: Smartphone-derived GPS features were shown to accurately characterize perioperative mobility trends in patients undergoing surgery for spine-related diseases. Features related to time (rather than distance) were better at describing patient physical and performance status. Smartphone GPS has the potential to be used for the development of accurate, noninvasive and personalized tools for patient mobility monitoring after surgery.

A noninvasive prediction tool for preoperative MRI helped identify patients likely to develop new symptoms at adjacent levels after anterior cervical diskectomy. Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clini...

The evolution of neurosurgery coincides with the evolution of visualization and navigation. Augmented reality technologies, with their ability to bring digital information into the real environment, have the potential to provide a new, revolutionary perspective to the neurosurgeon. To provide an overview on the historical and technical aspects of visualization and navigation in neurosurgery, and to provide a systematic review on augmented reality (AR) applications in neurosurgery. We provided an overview on the main historical milestones and technical features of visualization and navigation tools in neurosurgery. We systematically searched PubMed and Scopus databases for AR applications in neurosurgery and specifically discussed their relationship with current visualization and navigation systems, as well as main limitations. The evolution of visualization in neurosurgery is embodied by four magnification systems: surgical loupes, endoscope, surgical microscope and more recently the exoscope, each presenting independent features in terms of magnification capabilities, eye-hand coordination and the possibility to implement additional functions. In regard to navigation, two independent systems have been developed: the frame-based and the frame-less systems. The most frequent application setting for AR is brain surgery (71.6%), specifically neuro-oncology (36.2%) and microscope-based (29.2%), even though in the majority of cases AR applications presented their own visualization supports (66%). The evolution of visualization and navigation in neurosurgery allowed for the development of more precise instruments; the development and clinical validation of AR applications, have the potential to be the next breakthrough, making surgeries safer, as well as improving surgical experience and reducing costs.

BACKGROUND: Growing evidence associates traumatic brain injury (TBI) with increased risk of dementia, but few studies have evaluated associations in patients younger than 55 yr using non-TBI orthopedic trauma (NTOT) patients as controls to investigate the influence of age and TBI severity, and to identify predictors of dementia after trauma. OBJECTIVE: To investigate the relationship between TBI and dementia in an institutional group. METHODS: Retrospective cohort study (2000-2018) of TBI patients aged 45 to 100 yr vs NTOT controls. Primary outcome was dementia after TBI (followed ≤10 yr). Cox proportional hazards models were used to assess risk of dementia; logistic regression models assessed predictors of dementia. RESULTS: Among 24 846 patients, TBI patients developed dementia (7.5% vs 4.6%) at a younger age (78.6 vs 82.7 yr) and demonstrated higher 10-yr mortality than controls (27% vs 14%; P < .001). Mild TBI patients had higher incidence of dementia (9%) than moderate/severe TBI (5.4%), with lower 10-yr mortality (20% vs 31%; P < .001). Risk of dementia was significant in all mild TBI age groups, even 45 to 54 yr (hazard ratio 4.1, 95% CI 2.7-7.8). A total of 10-yr cumulative incidence was higher in mild TBI (14.4%) than moderate/severe TBI (11.3%) and controls (6.8%) (P < .001). Predictors of dementia include TBI, sex, age, hypertension, hyperlipidemia, stroke, depression, anxiety, and Injury Severity Score. CONCLUSION: Mild and moderate/severe TBI patients experienced higher incidence of dementia, even in the youngest group (45-54 yr old), than NTOT controls. All TBI patients, especially middle-aged adults with minor injury who are more likely to be overlooked, should be monitored for dementia.

Butterfly glioblastomas (bGBM) are grade IV gliomas that spread to bilateral hemispheres by infiltrating the corpus callosum. Data on the effect of surgery are limited to small case series. The aim of this meta-analysis was to compare resection vs. biopsy in terms of survival outcomes and postoperative complications. A systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane databases through March 2021 in accordance with the PRISMA checklist. Pooled hazard ratios were calculated and meta-analyzed in a random-effects model including assessment of heterogeneity. Out of 3367 articles, seven studies were included with 293 patients. Surgical resection was significantly associated with longer overall survival (HR 0.39, 95%CI 0.2–0.55) than biopsy. Low heterogeneity was observed (I²: 0%). In further analysis, the effect persisted in extent of resection subgroups of both ≥80% and <80%. No statistically significant difference between surgery and biopsy was detected in terms of postoperative complications, although these were numerically larger for surgery. In patients with bGBM, surgical resection was associated with longer survival prospects compared with biopsy.