How well do neurosurgeons predict survival in patients with high-grade glioma?

Due to the lack of reliable prognostic tools, prognostication and surgical decisions largely rely on the neurosurgeons’ clinical prediction skills. The aim of this study was to assess the accuracy of neurosurgeons’ prediction of survival in patients with high-grade glioma and explore factors possibly associated with accurate predictions. In a prospective single-center study, 199 patients who underwent surgery for high-grade glioma were included. After surgery, the operating surgeon predicted the patient’s survival using an ordinal prediction scale. A survival curve was used to visualize actual survival in groups based on this scale, and the accuracy of clinical prediction was assessed by comparing predicted and actual survival. To investigate factors possibly associated with accurate estimation, a binary logistic regression analysis was performed. The surgeons were able to diferentiate between patients with diferent lengths of survival, and median survival fell within the predicted range in all groups with predicted survival24 months, median survival was shorter than predicted. The overall accuracy of surgeons’ survival estimates was 41%, and over- and underestimations were done in 34% and 26%, respectively. Consultants were 3.4 times more likely to accurately predict survival compared to residents (p=0.006). Our fndings demonstrate that although especially experienced neurosurgeons have rather good predictive abilities when estimating survival in patients with high-grade glioma on the group level, they often miss on the individual level. Future prognostic tools should aim to beat the presented clinical prediction skills.publishedVersio

The study design of UDRIVE: the Naturalistic Driving Study across Europe for cars, trucks and scooters

Purpose: UDRIVE is the first large-scale European Naturalistic Driving Study on cars, trucks and powered two wheelers. The acronym stands for "European naturalistic Driving and Riding for Infrastructure & Vehicle safety and Environment". The purpose of the study is to gain a better understanding of what happens on the road in everyday traffic situations. Methods: The paper describes Naturalistic Driving Studies, a method which provides insight into the actual real-world behaviour of road users, unaffected by experimental conditions and related biases. Naturalistic driving can be defined as a study undertaken to provide insight into driver behaviour during everyday trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control. Data collection will take place in six EU Member States. Results: Road User Behaviour will be studied with a focus on both safety and environment. The UDRIVE project follows the steps of the FESTA-V methodology, which was originally designed for Field Operational Tests. Conclusions: Defining research questions forms the basis of the study design and the specification of the recording equipment. Both will be described in this paper. Although the project has just started collecting data from drivers, we consider the process of designing the study as a major result which may help other initiatives to set up similar studies

Spatial distribution of malignant transformation in patients with low-grade glioma

Background Malignant transformation represents the natural evolution of diffuse low-grade gliomas (LGG). This is a catastrophic event, causing neurocognitive symptoms, intensified treatment and premature death. However, little is known concerning the spatial distribution of malignant transformation in patients with LGG. Materials and methods Patients histopathological diagnosed with LGG and subsequent radiological malignant transformation were identified from two different institutions. We evaluated the spatial distribution of malignant transformation with (1) visual inspection and (2) segmentations of longitudinal tumor volumes. In (1) a radiological transformation site < 2 cm from the tumor on preceding MRI was defined local transformation. In (2) overlap with pretreatment volume after importation into a common space was defined as local transformation. With a centroid model we explored if there were particular patterns of transformations within relevant subgroups. Results We included 43 patients in the clinical evaluation, and 36 patients had MRIs scans available for longitudinal segmentations. Prior to malignant transformation, residual radiological tumor volumes were > 10 ml in 93% of patients. The transformation site was considered local in 91% of patients by clinical assessment. Patients treated with radiotherapy prior to transformation had somewhat lower rate of local transformations (83%). Based upon the segmentations, the transformation was local in 92%. We did not observe any particular pattern of transformations in examined molecular subgroups. Conclusion Malignant transformation occurs locally and within the T2w hyperintensities in most patients. Although LGG is an infiltrating disease, this data conceptually strengthens the role of loco-regional treatments in patients with LGG.publishedVersio

Clinical Course in Chronic Subdural Hematoma Patients Aged 18–49 Compared to Patients 50 Years and Above: A Multicenter Study and Meta-Analysis

Objective: Chronic Subdural Hematoma (cSDH) is primarily a disease of elderly, and is rare in patients &lt;50 years, and this may in part be related to the increased brain atrophy from 50 years of age. This fact may also influence clinical presentation and outcome. The aim of this study was to study the clinical course with emphasis on clinical presentation of cSDH patients in the young (&lt;50 years).Methods: A retrospective review of a population-based cohort of 1,252 patients operated for cSDH from three Scandinavian neurosurgical centers was conducted. The primary end-point was difference in clinical presentation between the patients &lt;50 y/o and the remaining patients (≥50 y/o group). The secondary end-points were differences in perioperative morbidity, recurrence and mortality between the two groups. In addition, a meta-analysis was performed comparing clinical patterns of cSDH in the two age groups.Results: Fifty-two patients (4.2%) were younger than 50 years. Younger patients were more likely to present with headache (86.5% vs. 37.9%, p &lt; 0.001) and vomiting (25% vs. 5.2%, p &lt; 0.001) than the patients ≥50 y/o, while the ≥50 y/o group more often presented with limb weakness (17.3% vs. 44.8%, p &lt; 0.001), speech impairment (5.8% vs. 26.2%, p = 0.001) and gait disturbance or falls (23.1% vs. 50.7%, p &lt; 0.001). There was no difference between the two groups in recurrence, overall complication rate and mortality within 90 days. Our meta-analysis confirmed that younger patients are more likely to present with headache (p = 0.015) while the hemispheric symptoms are more likely in patients ≥50 y/o (p &lt; 0.001).Conclusion: Younger patients with cSDH present more often with signs of increased intracranial pressure, while those ≥50 y/o more often present with hemispheric symptoms. No difference exists between the two groups in terms of recurrence, morbidity, and short-term mortality. Knowledge of variations in clinical presentation is important for correct and timely diagnosis in younger cSDH patients

Religious education for spiritual bricoleurs? the perceptions of students in ten Christian-ethos secondary schools in England and Wales

Religious Education (RE) in England and Wales functions within a post-secular culture. In the last fifty years, approaches characterised by academic rigour, impartiality, and professionalism have been prioritised. In this post-secular culture, the notion of bricolage aptly describes how some young people seek meaning, explore the spiritual dimension of life, with fragmented understandings of, experiences and encounters with the religious traditions. This paper draws on data from an empirical research project involving 350 students, to explore why students in ten Christian-ethos secondary schools in England and Wales recognised Religious Education (RE) as a significant contributor to their spiritual development. The analysis is illuminated by employing Roebben's (2009) concept of a narthical learning space (NLS) as the lens with which to examine young people’s experiences. Three aspects of RE are explored: the debating of existential questions; opportunities to theologise and reflect; and encounters with the beliefs, practices, and opinions of others. This article argues that the concept of RE as a narthical learning space alongside the notion of young people as spiritual bricoleurs illuminates how the students in this study interpret the contribution of RE to their spiritual development

Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.Comment: 13 pages, 4 figures, 4 table

Extended Driving Impairs Nocturnal Driving Performances

Though fatigue and sleepiness at the wheel are well-known risk factors for traffic accidents, many drivers combine extended driving and sleep deprivation. Fatigue-related accidents occur mainly at night but there is no experimental data available to determine if the duration of prior driving affects driving performance at night. Participants drove in 3 nocturnal driving sessions (3–5am, 1–5am and 9pm–5am) on open highway. Fourteen young healthy men (mean age [±SD] = 23.4 [±1.7] years) participated Inappropriate line crossings (ILC) in the last hour of driving of each session, sleep variables, self-perceived fatigue and sleepiness were measured. Compared to the short (3–5am) driving session, the incidence rate ratio of inappropriate line crossings increased by 2.6 (95% CI, 1.1 to 6.0; P<.05) for the intermediate (1–5am) driving session and by 4.0 (CI, 1.7 to 9.4; P<.001) for the long (9pm–5am) driving session. Compared to the reference session (9–10pm), the incidence rate ratio of inappropriate line crossings were 6.0 (95% CI, 2.3 to 15.5; P<.001), 15.4 (CI, 4.6 to 51.5; P<.001) and 24.3 (CI, 7.4 to 79.5; P<.001), respectively, for the three different durations of driving. Self-rated fatigue and sleepiness scores were both positively correlated to driving impairment in the intermediate and long duration sessions (P<.05) and increased significantly during the nocturnal driving sessions compared to the reference session (P<.01). At night, extended driving impairs driving performances and therefore should be limited

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection