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Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.

Background: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes. Methods: This paper systematically reviews these factors associated with LC to select candidate predictor features for a practical application of predictive modeling. A systematic literature search was conducted to identify studies in which the LC of brain metastases is assessed for adult patients. EMBASE, PubMed, Web-of-Science, and the Cochrane Database were searched up to December 24, 2020. All studies investigating the LC of brain metastases as one of the endpoints were included, regardless of primary tumor type or treatment type. We first grouped studies based on primary tumor types resulting in lung, breast, and melanoma groups. Studies that did not focus on a specific primary cancer type were grouped based on treatment types resulting in surgery, SRT, and whole-brain radiotherapy groups. For each group, significant factors associated with LC were identified and discussed.. As a second project, we assessed the practical importance of selected features in predicting LC after Stereotactic Radiotherapy (SRT) with a Random Forest machine learning model. Accuracy and Area Under the Curve (AUC) of the Random Forest model, trained with the list of factors that were found to be associated with LC for the SRT treatment group, were reported. Results: The systematic literature search identified 6270 unique records. After screening titles and abstracts, 410 full texts were considered, and ultimately 159 studies were included for review. Most of the studies focused on the LC of the brain metastases for a specif

Patients with meningiomas frequently exhibit impairments in executive functioning. There are few studies specifically examining the role of frontal meningioma localization in executive functioning impairments. This study examines whether frontally located meningiomas are specifically associated with executive functioning impairments in a large sample of meningioma patients before treatment, using an axis-wise and lobe-based approach to meningioma localization. We retrospectively examined cognitive performances in 353 patients with frontal, frontally-involved and non-frontal meningiomas on a battery of tests including tests of executive functioning. We applied an axis-based approach to meningioma location, in addition to qualitative lobe-based localization. We examined the association between meningioma coordinates on an anterior-posterior axis and continuous cognitive performance scores in univariate correlations and linear regression analyses. We also examined the association between meningioma coordinates on an anterior-posterior axis with cognitive impairments in multivariable logistic regression analyses. Meningioma position on the anterior-posterior axis was only univariately associated with mean performance on the Stroop test Interference ratio and Symbol Digit Coding task. There was no (multivariable) association with impairments on tests of executive or non-executive domains. Increased odds of impairment on executive functioning tasks were associated with left-localization (Verbal Fluency) and larger meningioma volumes (Shifting Attention). We did not find a specific relation between a frontal meningioma location and executive functioning impairments, which may be explained by widespread organization of executive functioning throughout the brain, diffuse cognitive effects of the mass of meningiomas, functional reorganization due to neuroplasticity, or functional involvement of less-anteriorly located frontal areas.

Purpose Little is known about cognitive complaints (self-reported problems in cognitive functioning) in patients with Obstructive Sleep Apnea (OSA). We compared the prevalence and severity of cognitive complaints in patients with untreated OSA to patients with neurological and respiratory diseases. We also studied risk factors for cognitive complaints across these diseases, including OSA. Methods We used a convenience sample to compare untreated OSA patients (N = 86) to patients with stroke (N = 166), primary brain tumor (N = 197) and chronic obstructive pulmonary disease (COPD, N = 204) on cognitive complaints (Cognitive Failure Questionnaire, CFQ), anxiety and depression (Hospital Anxiety and Depression Scale, HADS) and cognitive impairments using neuropsychological tests. We combined all patient groups (OSA, stroke, brain tumor and COPD) and studied potential risk factors (demographic variables, anxiety, depression and cognitive impairments) for cognitive complaints across all patient groups using regression analysis. Results The prevalence of cognitive complaints was higher in OSA patients and complaints of forgetfulness and distractibility were more severe compared to stroke and primary brain tumor patients, but similar to or lower than COPD patients. Regression analysis for the combined sample of all patient groups showed that cognitive complaints were most strongly associated with symptoms of anxiety and depression. Conclusion A high rate of OSA reported clinically significant cognitive complaints, comparable to other respiratory and neurological patients. Symptoms of anxiety and depression are important risk factors for cognitive complaints in patients with various neurological and respiratory diseases. Future studies should examine the relation between anxiety, depression and cognitive complaints in patients with OSA.

Background: In May 2021, a new guideline on the diagnosis and treatment of multiple sclerosis and related disorders was released in Germany. Since the success of a guideline depends on how it integrates into everyday clinical practice, the German Society for Neurology (DGN) has launched a multimethod implementation project. Here we report on the results based on the consultation version of the guideline. Methods: We used qualitative and quantitative data analyses to capture the nature and extent of barriers and facilitating factors to the implementation. We centered on the guideline's chapter A on diagnosis, relapse therapy, and immunotherapy of multiple sclerosis. We performed nine online focus group discussions and a web-based survey and analyzed emails and letters with comments from stakeholders and independent parties that were sent spontaneously or by invitation. Results: 94 neurologists answered the survey, and >= 70% agreed with the recommendations of the guideline on each major content topic. Barriers to implementation were detected in group discussions and written input. The most controversial issues of the guideline were early treatment, criteria for starting or switching therapy, stepwise escalation versus early aggressive treatment, classification of drugs into three categories of efficacy and the scenarios on treatment cessation. Some appreciated the highly structured recommendations, but others felt that the guideline restricts the free choice of therapy, or they were afraid of recourse claims. Some considered the guideline as too cautious regarding treatment initiation, possibly delaying necessary therapies. Others appreciated that conflicts of interests of the guideline's authoring group were minimized and thought that the new guideline is clearer, more extensive and practical. Conclusion: In contrast to the survey, feedback in the focus group discussions and from individuals was diverse and sometimes more critical. Based on the overall feedback rate

We performed a web-based survey among German-speaking neurologists to evaluate the acceptance of the 2021 German guideline in the diagnosis and treatment of multiple sclerosis. Based on 327 replies in total, the current survey largely reproduced the findings of an earlier, smaller survey on the prefinal consultation version of the guideline and confirmed high acceptance rates. Half of the participants were practising neurologists. Neurologists from MS centers with more than 500 patients per year (n=26) were more critical of the guideline. They reiterated some of the criticisms of the previous feedback, and, in particular, felt that safety aspects are overemphasized in the guideline, thereby superseding early aggressive therapy.

Background Manual segmentation of brain tumors requires expertise, is time-consuming, and is subject to inter-rater variability. Fully automatic brain tumor segmentation is possible for glioma and meningioma when volumetric T1, T1 contrast-enhanced (T1c), T2, and Fluid-attenuated inversion recovery (FLAIR) MRIs are available. In clinical care of meningiomas, however, often only volumetric T1c scans are available. In this work, we trained a deep learning network to segment meningiomas using only T1c scans for use in clinical research. Material and Methods NnU-Net, a deep learning model that is optimized for medical image segmentation, was trained to segment meningiomas from T1c images. This was performed on a large clinically collected meningioma dataset (n=374) of T1c scans with semi-automatically generated enhancing tumor masks and additional data from the BraTS2020 glioma dataset. Model performance was compared against inter-rater reliability, between different models, between anatomical tumor locations, and against models using multiple MRI modalities. Results The best performing model obtained a Dice score of 0.90. This performance was 0.03 points lower when compared to inter-rater reliability (Dice=0.93) and almost equal to models using multiple MRI modalities. Model performance split over anatomical tumor locations was between 0.90 and 0.97 (Dice). Conclusion Fully automatic meningioma segmentation using only T1c images is possible with an accuracy that is similar to inter-rater reliability and models using multiple imaging modalities.

Aims: Stereotactic radiosurgery is increasingly used to treat multiple (four or more) brain metastases. Preserving cognitive functions is a highly relevant treatment goal because cognitive deteriorations may negatively affect a patient's quality of life. The aim of this study was to assess cognitive change, at the group and individual level, in patients with 1 to 10 brain metastases up to 9 months after Gamma Knife radiosurgery (GKRS).Materials and methods: Ninety-two patients with 1 to 10 newly diagnosed brain metastases, expected survival >3 months and Karnofsky Performance Status (KPS) ≥70 and 104 non-cancer controls were included. A neuropsychological test battery was administered before GKRS (n = 92) and at 3 (n = 66), 6 (n = 52) and 9 (n = 41) months after GKRS. The course of test performances, while taking into account practice effects, was analysed using linear mixed models. Pre-GKRS predictors of cognitive trajectories were analysed. To determine proportions of individuals with cognitive changes, reliable change indices, with correction for practice effects, were calculated.Results: At the group level, immediate memory, working memory and information processing speed significantly improved over 9 months after GKRS. There were no cognitive declines. Neither number nor volume of brain metastases influenced cognitive change over time. At the individual level, proportions of patients with stable, improved or declined performances were comparable with controls, except for information processing speed (more individuals with improvements in patients) and motor dexterity (more improvements and declines in patients).Conclusions: Cognitive functioning in patients with 1 to 10 brain metastases was preserved, or improved, up to 9 months after GKRS. Neither number nor volume of brain metastases influenced cognitive performance.

Introduction: Central Nervous System Vital Signs (CNS VS) is a computerized neuropsychological battery that is translated into many languages. However, published CNS VS’ normative data were established over a decade ago, are solely age-corrected, and collected in an American population only. Method: Mean performance of healthy Dutch participants on CNS VS was compared with the original CNS VS norms (N = 1,069), and effects of sociodemographic variables were examined. Results: z tests demonstrated no significant differences in performance on four out of seven cognitive domains; however, Dutch participants (N = 158) showed higher scores on processing and psychomotor speed, as well as on cognitive flexibility. Although the original CNS VS norms are solely age-corrected, effects of education and sex on CNS VS performance were also identified in the Dutch sample. Discussion: Users should be cautious when interpreting CNS VS performance based on the original American norms, and sociodemographic factors must also be considered.