PET/MRI Radiomics in Patients With Brain Metastases

Targeting DBS to the centrolateral thalamic nucleus improves movement in a lesion-based model of acquired cerebellar dystonia in mice

Dystonia is the third most common movement disorder and an incapacitating co-morbidity in a variety of neurologic conditions. Dystonia can be caused by genetic, degenerative, idiopathic, and acquired etiologies, which are hypothesized to converge on a "dystonia network" consisting of the basal ganglia, thalamus, cerebellum, and cerebral cortex. In acquired dystonia, focal lesions to subcortical areas in the network - the basal ganglia, thalamus, and cerebellum - lead to a dystonia that can be difficult to manage with canonical treatments, including deep brain stimulation (DBS). While studies in animal models have begun to parse the contribution of individual nodes in the dystonia network, how acquired injury to the cerebellar outflow tracts instigates dystonia; and how network modulation interacts with symptom latency remain as unexplored questions. Here, we present an electrolytic lesioning paradigm that bilaterally targets the cerebellar outflow tracts. We found that lesioning these tracts, at the junction of the superior cerebellar peduncles and the medial and intermediate cerebellar nuclei, resulted in acute, severe dystonia. We observed that dystonia is reduced with one hour of DBS of the centrolateral thalamic nucleus, a first order node in the network downstream of the cerebellar nuclei. In contrast, one hour of stimulation at a second order node in the short latency, disynaptic projection from the cerebellar nuclei, the striatum, did not modulate the dystonia in the short-term. Our study introduces a robust paradigm for inducing acute, severe dystonia, and demonstrates that targeted modulation based on network principles powerfully rescues motor behavior. These data inspire the identification of therapeutic targets for difficult to manage acquired dystonia.

The Progress in Molecular Transport and Therapeutic Development in Human Blood-Brain Barrier Models in Neurological Disorders

The blood-brain barrier (BBB) is responsible for maintaining homeostasis within the central nervous system (CNS). Depending on its permeability, certain substances can penetrate the brain, while others are restricted in their passage. Therefore, the knowledge about BBB structure and function is essential for understanding physiological and pathological brain processes. Consequently, the functional models can serve as a key to help reveal this unknown. There are many in vitro models available to study molecular mechanisms that occur in the barrier. Brain endothelial cells grown in culture are commonly used to modeling the BBB. Current BBB platforms include: monolayer platforms, transwell, matrigel, spheroidal, and tissue-on-chip models. In this paper, the BBB structure, molecular characteristic, as well as its dysfunctions as a consequence of aging, neurodegeneration, or under hypoxia and neurotoxic conditions are presented. Furthermore, the current modelling strategies that can be used to study BBB for the purpose of further drugs development that may reach CNS are also described.

Integrated analysis of m6A regulator-mediated RNA methylation modification patterns and immune characteristics in Sjögren's syndrome

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention.

GABAergic interneurons contribute to the fatal seizure phenotype of CLN2 disease mice

GABAergic interneuron deficits have been implicated in the epileptogenesis of multiple neurological diseases. While epileptic seizures are a key clinical hallmark of CLN2 disease, a childhood-onset neurodegenerative lysosomal storage disorder caused by a deficiency of tripeptidyl peptidase 1 (TPP1), the etiology of these seizures remains elusive. Given that Cln2 R207X/R207X mice display fatal spontaneous seizures and an early loss of several cortical interneuron populations, we hypothesized that those two events might be causally related. To address this hypothesis, we first generated an inducible transgenic mouse expressing lysosomal membrane-tethered TPP1 (TPP1LAMP1) on the Cln2 R207X/R207X genetic background to study the cell-autonomous effects of cell-type-specific TPP1 deficiency. We crossed the TPP1LAMP1 mice with Vgat-Cre mice to introduce interneuron-specific TPP1 deficiency. Vgat-Cre ; TPP1LAMP1 mice displayed storage material accumulation in several interneuron populations both in cortex and striatum, and increased susceptibility to die after PTZ-induced seizures. Secondly, to test the role of GABAergic interneuron activity in seizure progression, we selectively activated these cells in Cln2 R207X/R207X mice using Designer Receptor Exclusively Activated by Designer Drugs (DREADDs) in in Vgat-Cre : Cln2 R207X/R207X mice. EEG monitoring revealed that DREADD-mediated activation of interneurons via chronic deschloroclozapine administration accelerated the onset of spontaneous seizures and seizure-associated death in Vgat-Cre : Cln2 R207X/R207X mice, suggesting that modulating interneuron activity can exert influence over epileptiform abnormalities in CLN2 disease. Taken together, these results provide new mechanistic insights into the underlying etiology of seizures and premature death that characterize CLN2 disease.

The use of PET/MRI in radiotherapy

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a hybrid imaging technique that quantitatively combines the metabolic and functional data from positron emission tomography (PET) with anatomical and physiological information from MRI. As PET/MRI technology has advanced, its applications in cancer care have expanded. Recent studies have demonstrated that PET/MRI provides unique advantages in the field of radiotherapy and has become invaluable in guiding precision radiotherapy techniques. This review discusses the rationale and clinical evidence supporting the use of PET/MRI for radiation positioning, target delineation, efficacy evaluation, and patient surveillance.Critical relevance statement This article critically assesses the transformative role of PET/MRI in advancing precision radiotherapy, providing essential insights into improved radiation positioning, target delineation, efficacy evaluation, and patient surveillance in clinical radiology practice.Key points• The emergence of PET/MRI will be a key bridge for precise radiotherapy.• PET/MRI has unique advantages in the whole process of radiotherapy.• New tracers and nanoparticle probes will broaden the use of PET/MRI in radiation.• PET/MRI will be utilized more frequently for radiotherapy.

Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease

Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive computational markers from primary tumor and BM data at [Formula: see text] and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are [Formula: see text], the proliferation rate of a single tumor cell; and [Formula: see text], the per day, per cell, probability to metastasize. The predictive value of these individual computational biomarkers was evaluated. The model was able to correctly describe the number and size of metastases at [Formula: see text] for 20 patients. Parameters [Formula: see text] and [Formula: see text] were significantly associated with overall survival (OS) (HR 1.65 (1.07-2.53) p = 0.0029 and HR 1.95 (1.31-2.91) p = 0.0109, respectively). Adding the computational markers to the clinical ones significantly improved the predictive value of OS (c-index increased from 0.585 (95% CI 0.569-0.602) to 0.713 (95% CI 0.700-0.726), p < 0.0001). We demonstrated that our model was applicable to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive potential. This may help lung cancer physicians to guide and personalize the management of NSCLC patients with intracranial oligoprogression.

Risk Factors for Birth Asphyxia in Hospital-Delivered Newborns in Dodoma, Tanzania: A Case-Control Study

Introduction

Asphyxia at birth remains the leading cause of neonatal morbidity and mortality worldwide, accounting for ∼23% of all neonatal deaths. Although the causes vary from country to country, early identification and treatment of risk factors can improve the situation.

Objectives

To determine the risk factors of birth asphyxia in hospital-delivered neonates in Dodoma, Tanzania.

Methods

A matched case-control study was conducted from May to July 2017 at Dodoma Region Referral Hospital. Data were collected using a semistructured questionnaire and a standard antenatal care index card. Cases were neonates diagnosed with asphyxia at birth ( N  =  100), while controls were neonates not diagnosed with asphyxia at birth ( N  =  300). A binary logistic regression model was used to assess the independent variables associated with birth asphyxia and reported as crude and adjusted odds ratios along with their 95% confidence intervals.

Results

A total of 400 newborns and their birth mothers were involved in the study. The average age of the case mothers was 26.9 years ( SD  =  7.85) and that of the control mothers was 27.24 years ( SD  =  6.08). Place of residence, anemia, maternal age, prenatal visits attended, use of herbs during labor, previously complicated pregnancy, duration of labor, meconium-stained amniotic fluid, and mode of delivery were predictors of birth asphyxia.

Conclusion

The study showed that most predictors of birth asphyxia can be prevented. The results suggest appropriate health education before conception, effective follow-up through prenatal care, early identification and treatment of high-risk pregnant women, and proper monitoring of labor and delivery.

Quantitation of Oncologic Image Features for Radiomic Analyses in PET

Radiomics is an emerging and exciting field of study involving the extraction of many quantitative features from radiographic images. Positron emission tomography (PET) images are used in cancer diagnosis and staging. Utilizing radiomics on PET images can better quantify the spatial relationships between image voxels and generate more consistent and accurate results for diagnosis, prognosis, treatment, etc. This chapter gives the general steps a researcher would take to extract PET radiomic features from medical images and properly develop models to implement.

Risk Factors for Birth Asphyxia in Hospital-Delivered Newborns in Dodoma, Tanzania: A Case-Control Study

Introduction

Asphyxia at birth remains the leading cause of neonatal morbidity and mortality worldwide, accounting for ∼23% of all neonatal deaths. Although the causes vary from country to country, early identification and treatment of risk factors can improve the situation.

Objectives

To determine the risk factors of birth asphyxia in hospital-delivered neonates in Dodoma, Tanzania.

Methods

A matched case-control study was conducted from May to July 2017 at Dodoma Region Referral Hospital. Data were collected using a semistructured questionnaire and a standard antenatal care index card. Cases were neonates diagnosed with asphyxia at birth (N  =  100), while controls were neonates not diagnosed with asphyxia at birth (N  =  300). A binary logistic regression model was used to assess the independent variables associated with birth asphyxia and reported as crude and adjusted odds ratios along with their 95% confidence intervals.

Results

A total of 400 newborns and their birth mothers were involved in the study. The average age of the case mothers was 26.9 years (SD  =  7.85) and that of the control mothers was 27.24 years (SD  =  6.08). Place of residence, anemia, maternal age, prenatal visits attended, use of herbs during labor, previously complicated pregnancy, duration of labor, meconium-stained amniotic fluid, and mode of delivery were predictors of birth asphyxia.

Conclusion

The study showed that most predictors of birth asphyxia can be prevented. The results suggest appropriate health education before conception, effective follow-up through prenatal care, early identification and treatment of high-risk pregnant women, and proper monitoring of labor and delivery.

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning

OBJECTIVE

Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS

Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS

The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS

Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Detection and classification of adult epilepsy using hybrid deep learning approach

The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative effects of epileptic seizures on patients. The classification of minimally pre-processed, raw multichannel EEG signal recordings is the foundation of this article's unique method for identifying seizures in pre-adult patients. The new method makes use of the automatic feature learning capabilities of a three-dimensional deep convolution auto-encoder (3D-DCAE) associated with a neural network-based classifier to build an integrated framework that endures training in a supervised manner to attain the highest level of classification precision among brain state signals, both ictal and interictal. A pair of models were created and evaluated for testing and assessing our method, utilizing three distinct EEG data section lengths, and a tenfold cross-validation procedure. Based on five evaluation criteria, the labelled hybrid convolutional auto-encoder (LHCAE) model, which utilizes a classifier based on bidirectional long short-term memory (Bi-LSTM) and an EEG segment length of 4 s, had the best efficiency. This proposed model has 99.08 ± 0.54% accuracy, 99.21 ± 0.50% sensitivity, 99.11 ± 0.57% specificity, 99.09 ± 0.55% precision, and an F1-score of 99.16 ± 0.58%, according to the publicly available Children's Hospital Boston (CHB) dataset. Based on the obtained outcomes, the proposed seizure classification model outperforms the other state-of-the-art method's performance in the same dataset.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

DNA hypomethylation promotes the expression of CASPASE-4 which exacerbates neuroinflammation and amyloid-β deposition in Alzheimer's disease The Ohio State University College of Medicine

Alzheimer's Disease (AD) is the 6th leading cause of death in the US. It is established that neuroinflammation contributes to the synaptic loss, neuronal death, and symptomatic decline of AD patients. Accumulating evidence suggests a critical role for microglia, innate immune phagocytes of the brain. For instance, microglia release proinflammatory products such as IL-1β which is highly implicated in AD pathobiology. The mechanisms underlying the transition of microglia to proinflammatory promoters of AD remain largely unknown. To address this gap, we performed Reduced Representation Bisulfite Sequencing (RRBS) to profile global DNA methylation changes in human AD brains compared to no disease controls. We identified differential DNA methylation of CASPASE-4 (CASP4), which when expressed, can be involved in generation of IL-1β and is predominantly expressed in immune cells. DNA upstream of the CASP4 transcription start site was hypomethylated in human AD brains, which was correlated with increased expression of CASP4. Furthermore, microglia from a mouse model of AD (5xFAD) express increased levels of CASP4 compared to wild-type (WT) mice. To study the role of CASP4 in AD, we developed a novel mouse model of AD lacking the mouse ortholog of CASP4, CASP11, which is encoded by mouse Caspase-4 (5xFAD/Casp4-/-). The expression of CASP11 was associated with increased accumulation of pathologic protein aggregate amyloid-β (Aβ) and increased microglial production of IL-1β in 5xFAD mice. Utilizing RNA sequencing, we determined that CASP11 promotes unique transcriptomic phenotypes in 5xFAD mouse brains, including alterations of neuroinflammatory and chemokine signaling pathways. Notably, in vitro, CASP11 promoted generation of IL-1β from macrophages in response to cytosolic Aβ through cleavage of downstream effector Gasdermin D (G SDMD). We describe a role for CASP11 and GSDMD in the generation of IL-1β in response to Aβ and the progression of pathologic inflammation in AD. Overall, our results demonstrate that overexpression of CASP4 due to differential methylation in AD microglia contributes to the progression of AD pathobiology, thus identifying CASP4 as a potential target for immunotherapies for the treatment of AD.

Thrombus imaging characteristics within acute ischemic stroke: similarities and interdependence

BACKGROUND

The effects of thrombus imaging characteristics on procedural and clinical outcomes after ischemic stroke are increasingly being studied. These thrombus characteristics - for eg, size, location, and density - are commonly analyzed as separate entities. However, it is known that some of these thrombus characteristics are strongly related. Multicollinearity can lead to unreliable prediction models. We aimed to determine the distribution, correlation and clustering of thrombus imaging characteristics based on a large dataset of anterior-circulation acute ischemic stroke patients.

METHODS

We measured thrombus imaging characteristics in the MR CLEAN Registry dataset, which included occlusion location, distance from the intracranial carotid artery to the thrombus (DT), thrombus length, density, perviousness, and clot burden score (CBS). We assessed intercorrelations with Spearman's coefficient (ρ) and grouped thrombi based on 1) occlusion location and 2) thrombus length, density and perviousness using unsupervised clustering.

RESULTS

We included 934 patients, of which 22% had an internal carotid artery (ICA) occlusion, 61% M1, 16% M2, and 1% another occlusion location. All thrombus characteristics were significantly correlated. Higher CBS was strongly correlated with longer DT (ρ=0.67, p<0.01), and moderately correlated with shorter thrombus length (ρ=-0.41, p<0.01). In more proximal occlusion locations, thrombi were significantly longer, denser, and less pervious. Unsupervised clustering analysis resulted in four thrombus groups; however, the cohesion within and distinction between the groups were weak.

CONCLUSIONS

Thrombus imaging characteristics are significantly intercorrelated - strong correlations should be considered in future predictive modeling studies. Clustering analysis showed there are no distinct thrombus archetypes - novel treatments should consider this thrombus variability.

Machine learning in the positron emission tomography imaging of Alzheimer's disease

The utilization of machine learning techniques in medicine has exponentially increased over the last decades due to innovations in computer processing, algorithm development, and access to big data. Applications of machine learning techniques to neuroimaging specifically have unveiled various hidden interactions, structures, and mechanisms related to various neurological disorders. One application of interest is the imaging of Alzheimer's disease, the most common cause of progressive dementia. The diagnoses of Alzheimer's disease, mild cognitive impairment, and preclinical Alzheimer's disease have been difficult. Molecular imaging, particularly via PET scans, holds tremendous value in the imaging of Alzheimer's disease. To date, many novel algorithms have been developed with great success that leverage machine learning in the context of Alzheimer's disease. This review article provides an overview of the diverse applications of machine learning to PET imaging of Alzheimer's disease.

Development of MAPT S305 mutation models exhibiting elevated 4R tau expression, resulting in altered neuronal and astrocytic function

Due to the importance of 4R tau in the pathogenicity of primary tauopathies, it has been challenging to model these diseases in iPSC-derived neurons, which express very low levels of 4R tau. To address this problem we have developed a panel of isogenic iPSC lines carrying the MAPT splice-site mutations S305S, S305I or S305N, derived from four different donors. All three mutations significantly increased the proportion of 4R tau expression in iPSC-neurons and astrocytes, with up to 80% 4R transcripts in S305N neurons from as early as 4 weeks of differentiation. Transcriptomic and functional analyses of S305 mutant neurons revealed shared disruption in glutamate signaling and synaptic maturity, but divergent effects on mitochondrial bioenergetics. In iPSC-astrocytes, S305 mutations induced lysosomal disruption and inflammation and exacerbated internalization of exogenous tau that may be a precursor to the glial pathologies observed in many tauopathies. In conclusion, we present a novel panel of human iPSC lines that express unprecedented levels of 4R tau in neurons and astrocytes. These lines recapitulate previously characterized tauopathy-relevant phenotypes, but also highlight functional differences between the wild type 4R and mutant 4R proteins. We also highlight the functional importance of MAPT expression in astrocytes. These lines will be highly beneficial to tauopathy researchers enabling a more complete understanding of the pathogenic mechanisms underlying 4R tauopathies across different cell types.

Narrative recall in relapsing-remitting multiple sclerosis: A potentially useful speech task for detecting subtle cognitive changes

Our research studied relapsing-remitting multiple sclerosis (RRMS). In half of the RRMS cases, mild cognitive difficulties are present, but often remain undetected despite their adverse effects on individuals' daily life. Detecting subtle cognitive alterations using speech analysis have rarely been implemented in MS research. We applied automatic speech recognition technology to devise a speech task with potential diagnostic value. Therefore, we used two narrative tasks adjusted for the neural and cognitive characteristics of RRMS; namely narrative recall and personal narrative. In addition to speech analysis, we examined the information processing speed, working memory, verbal fluency, and naming skills. Twenty-one participants with RRMS and 21 gender-, age-, and education-matched healthy controls took part in the study. All the participants with RRMS achieved a normal performance on Addenbrooke's Cognitive Examination. The following parameters of speech were measured: articulation and speech rate, the proportion, duration, frequency, and average length of silent and filled pauses. We found significant differences in the temporal parameters between groups and speech tasks. ROC analysis produced high classification accuracy for the narrative recall task (0.877 and 0.866), but low accuracy for the personal narrative task (0.617 and 0.592). The information processing speed affected the speech of the RRMS group but not that of the control group. The higher cognitive load of the narrative recall task may be the cause of significant changes in the speech of the RRMS group relative to the controls. Results suggest that narrative recall task may be effective for detecting subtle cognitive changes in RRMS.

Risk factors for developing symptomatic COVID-19 in older residents of nursing homes: A hypothesis-generating observational study

Objectives

To identify which risk factors were associated with developing Coronavirus Disease-19 (COVID-19) infection, with symptoms, in institutionalized older people.

Methods

A 1-year longitudinal multi-center study was conducted in 5 nursing homes (NHs) over the period December 2019 to March 2021. Inclusion criteria included being a permanent resident in the NH, aged 65 years or older, and a positive diagnosis of COVID-19 objectively confirmed by a diagnostic test. A descriptive and bivariate analysis was performed, calculating relative risk (RR) with 95% confidence intervals and statistical significance at p<0.05.

Results

Of the total sample of 78 individuals who tested positive for COVID-19, the mean age was 84.6 years (SD=±7.8), 62 (79.5%) were female; 40 (51.3%) participants presented with COVID-19 symptoms. Living in a private NH (RR=3.6, 95% CI [1.2-11.0], p=0.023) and having suffered a stroke (RR=4.1, 95% CI [1.1-14.7], p=0.033) were positively associated with developing COVID-19 infection with symptoms.

Conclusions

Having suffered a stroke and living permanently in a private NH were positively associated with symptomatic COVID-19 in this sample of institutionalized older people.Clinical Trials ID: NCT04297904.

An off-label use of flow-diverter stent as a successful treatment of a postoperative middle cerebral artery pseudoaneurysm

A pseudoaneurysm or false aneurysm is the result of the disruption of the vessel wall and the formation of a hematoma in communication with the vascular lumen, restrained by perivascular connective tissue. Intracranial pseudoaneurysms represent a rare entity mainly because of trauma, iatrogenic causes, infectious disease, radiation exposure, connective tissue disease and sometimes spontaneous occurrence. We present a 35-year-old female patient with a history of multiple low-grade glioma debulking surgeries. During the last procedure, laceration of the left middle cerebral artery (MCA) occurred with diffuse subarachnoid hemorrhage. Imaging studies showed the formation of a pseudoaneurysm of the left MCA which was successfully treated with the implantation of a flow diverter across the lesion neck and excellent mid- to long- term results. Flow diverter implantation may be a promising technique for the therapeutic management of cerebral pseudoaneurysms.

The Immediate Effects of Different Types of Augmented Feedback on Fast Walking Speed Performance and Intrinsic Motivation After Stroke

Objective

To examine the immediate effects of different types of augmented feedback on walking speed and intrinsic motivation post-stroke.

Design

A within-subjects repeated-measures design.

Setting

A university rehabilitation center.

Participants

Eighteen individuals with chronic stroke hemiparesis with a mean age of 55.67±13.63 years and median stroke onset of 36 (24, 81) months (N=18).

Interventions

Not applicable.

Primary outcome

Fast walking speed measured on a robotic treadmill for 13 meters without feedback and 13 meters with augmented feedback on each of the 3 experimental conditions: (1) without virtual reality (VR), (2) with a simple VR interface, and (3) with VR-exergame. Intrinsic motivation was measured using the Intrinsic Motivation Inventory (IMI).

Results

Although the differences were not statistically significant, fast-walking speed was higher in the augmented feedback without VR (0.86±0.44 m/s); simple VR interface (0.87±0.41 m/s); VR-exergame (0.87±0.44 m/s) conditions than in the fast-walking speed without feedback (0.81±0.40 m/s) condition. The type of feedback had a significant effect on intrinsic motivation (P=.04). The post hoc analysis revealed borderline significance on IMI-interest and enjoyment between the VR-exergame condition and the without-VR condition (P=.091).

Conclusion

Augmenting feedback affected the intrinsic motivation and enjoyment of adults with stroke asked to walk fast on a robotic treadmill. Additional studies with larger samples are warranted to examine the relations among these aspects of motivation and ambulation training outcomes.

The Role of Molecular Imaging in Patients with Brain Metastases: A Literature Review

Over the last several years, molecular imaging has gained a primary role in the evaluation of patients with brain metastases (BM). Therefore, the "Response Assessment in Neuro-Oncology" (RANO) group recommends amino acid radiotracers for the assessment of BM. Our review summarizes the current use of positron emission tomography (PET) radiotracers in patients with BM, ranging from present to future perspectives with new PET radiotracers, including the role of radiomics and potential theranostics approaches. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2022 were reviewed. Current evidence confirms the important role of amino acid PET radiotracers for the delineation of BM extension, for the assessment of response to therapy, and particularly for the differentiation between tumor progression and radionecrosis. The newer radiotracers explore non-invasively different biological tumor processes, although more consistent findings in larger clinical trials are necessary to confirm preliminary results. Our review illustrates the role of molecular imaging in patients with BM. Along with magnetic resonance imaging (MRI), the gold standard for diagnosis of BM, PET is a useful complementary technique for processes that otherwise cannot be obtained from anatomical MRI alone.

Imaging of brain metastasis in non-small-cell lung cancer: indications, protocols, diagnosis, post-therapy imaging, and implications regarding management

Increased survival (due to the use of targeted therapies based on genomic profiling) has resulted in the increased incidence of brain metastasis during the course of disease, and thus, made it essential to have proper imaging guidelines in place for brain metastasis from non-small-cell lung cancer (NSCLC). Brain parenchymal metastases can have varied imaging appearances, and it is pertinent to be aware of the various molecular risk factors for brain metastasis from NSCLC along with their suggestive imaging appearances, so as to identify them early. Leptomeningeal metastasis requires additional imaging of the spine and an early cerebrospinal fluid (CSF) analysis. Differentiation of post-therapy change from recurrence on imaging has a bearing on the management, hence the need for its awareness. This article will provide in-depth literature review of the epidemiology, aetiopathogenesis, screening, detection, diagnosis, post-therapy imaging, and implications regarding the management of brain metastasis from NSCLC. In addition, we will also briefly highlight the role of artificial intelligence (AI) in brain metastasis screening.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

Impact of the covid-19 pandemic on amyotrophic lateral sclerosis care in the UK

The Covid-19 pandemic has impacted healthcare. Our aim was to identify how amyotrophic lateral sclerosis (ALS) care in the UK has been affected by the pandemic by exploring the experiences of people living with ALS (plwALS), healthcare professionals (HCPs) working with plwALS, and ALS care centers. Three surveys were carried out to explore the experiences of plwALS, HCPs and ALS care centers during the pandemic. Quantitative data were analyzed using descriptive and inferential statistics and triangulated with the qualitative data which were analyzed thematically. Responses from 53 plwALS, 73 HCPs and 23 ALS care centers were analyzed. Five main themes were identified: keeping safe, losses, negative emotions, delivering care and alternative care delivery in a pandemic. PlwALS and HCPs felt that care was sub-optimal as a result of the pandemic. Changes to care included longer waiting times and face-to-face appointments being canceled or replaced by virtual consultations. While benefits of virtual consultations were reported, concerns were raised about incomplete clinical assessments and the disruption of provision of testing and interventions. ALS care has changed as a result of the pandemic. Patients have had a lack of face-to-face contact with HCPs and have experienced delays to investigations and treatments. PlwALS and HCPs were concerned about the impact of this change, but the long-term implications remain unclear. We propose recommendations for HCPs caring for plwALS, that will promote continuity of evidenced based care in the context of a pandemic.

Clinical characteristics and short-term outcomes of adult stroke patients admitted to Jimma Medical Center, Ethiopia: a prospective cohort study

Introduction

sub-Saharan African countries are facing a rapid increase in stroke incidence and mortality. However, there is a paucity of clinical studies on the burden of stroke and its short-term outcomes. Hence, this study is aimed at evaluating risk factors, clinical characteristics, management, and 28-day clinical outcomes among stroke patients.

Methods

a prospective observational study was conducted at Jimma Medical Center, Ethiopia from July 2020 to January 31^st, 2021. All adult patients diagnosed with stroke were enrolled consecutively and followed for 28 days starting from admission. Data were analyzed using SPSS version 23 and Multivariable cox regression was used to identify factors associated with 28-day all-cause mortality.

Results

among 153 patients enrolled in this study, 127 (83%) had brain CT-scan and hemorrhagic stroke accounts for 66 (52%). About half 81 (53%) of the participants were male and the mean age was 57 years. Regarding in-hospital management, antihypertensive, statins, and aspirin was given to 80 (52%), 72 (47%), and 68 (44%) patients respectively. The overall in-hospital mortality rate was 26 (17%) and the all-cause 28-day mortality rate was 39 (25.5%). Rural residence [adjusted Hazard Ratio (aHR): 2.93, 95% Confidence Interval (CI): 1.46-5.81], aspiration pneumonia (aHR= 6.57, 95% CI=3.16-13.66) and increased intracranial pressure (aHR= 3.27, 95% CI=1.56-6.86) were associated with 28-day mortality.

Conclusion

the patients admitted to the hospital with stroke diagnosis had high short-term mortality. Strategies focused on increasing timely arrival and evidence-based management of stroke and its complications could improve stroke patient outcomes.

Clinical description, molecular delineation and genotype–phenotype correlation in 340 patients with KBG syndrome: addition of 67 new patients

Background

KBG syndrome is a highly variable neurodevelopmental disorder and clinical diagnostic criteria have changed as new patients have been reported. Both loss-of-function sequence variants and large deletions (copy number variations, CNVs) involvingANKRD11cause KBG syndrome, but no genotype–phenotype correlation has been reported.

Methods

67 patients with KBG syndrome were assessed using a custom phenotypical questionnaire. Manifestations present in >50% of the patients and a ‘phenotypical score’ were used to perform a genotype–phenotype correlation in 340 patients from our cohort and the literature.

Results

Neurodevelopmental delay, macrodontia, triangular face, characteristic ears, nose and eyebrows were the most prevalentf (eatures. 82.8% of the patients had at least one of seven main comorbidities: hearing loss and/or otitis media, visual problems, cryptorchidism, cardiopathy, feeding difficulties and/or seizures. Associations found included a higher phenotypical score in patients with sequence variants compared with CNVs and a higher frequency of triangular face (71.1% vs 42.5% in CNVs). Short stature was more frequent in patients with exon 9 variants (62.5% inside vs 27.8% outside exon 9), and the prevalence of intellectual disability/attention deficit hyperactivity disorder/autism spectrum disorder was lower in patients with the c.1903_1907del variant (70.4% vs 89.4% other variants). Presence of macrodontia and comorbidities were associated with larger deletion sizes and hand anomalies with smaller deletions.

Conclusion

We present a detailed phenotypical description of KBG syndrome in the largest series reported to date of 67 patients, provide evidence of a genotype–phenotype correlation between some KBG features and specificANKRD11variants in 340 patients, and propose updated clinical diagnostic criteria based on our findings.

Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

Due to different treatment strategies, it is extremely important to differentiate between glioblastoma multiforme (GBM) and brain metastases (MET). It often proves difficult to distinguish between GBM and MET using MRI due to their similar appearance on the imaging modalities. Surgical methods are still necessary for definitive diagnosis, despite the importance of magnetic resonance imaging in detecting, characterizing, and monitoring brain tumors. We introduced an accurate, convenient, and user-friendly method to differentiate between GBM and MET through routine MRI sequence and radiomics analyses. We collected 91 patients from one institution, including 50 with GBM and 41 with MET, which were proven pathologically. The tumors separately were segmented on all MRI images (T1-weighted imaging (T1WI), contrast-enhanced T1-weighted imaging (T1C), T2-weighted imaging (T2WI), and fluid-attenuated inversion recovery (FLAIR)) to form the volume of interest (VOI). Eight ML models and feature reduction strategies were evaluated using routine MRI sequences (T1W, T2W, T1-CE, and FLAIR) in two methods with (second model) and without wavelet transform (first model) radiomics. The optimal model was selected based on each model’s accuracy, AUC-roc, and F1-score values. In this study, we have achieved the result of 0.98, 0.99, and 0.98 percent for accuracy, AUC-roc, and F1-score, respectively, which have yielded a better result than the first model. In most investigated models, there were significant improvements in the multidimensional wavelets model compared to the non-multidimensional wavelets model. Multidimensional discrete wavelet transform can analyze hidden features of the MRI from a different perspective and generate accurate features which are highly correlated with the model accuracy.

Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms

The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm³) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm³) and a small phantom (small QR) (4 × 4 × 3 cm³)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans.

The Interaction Between Sleep and Epilepsy

PURPOSE OF REVIEW

To review the mutual interactions between sleep and epilepsy, including mechanisms of epileptogenesis, the relationship between sleep apnea and epilepsy, and potential strategies to treat seizures.

RECENT FINDINGS

Recent studies have highlighted the role of functional network systems underlying epileptiform activation in sleep in several epilepsy syndromes, including absence epilepsy, benign focal childhood epilepsy, and epileptic encephalopathy with spike-wave activation in sleep. Sleep disorders are common in epilepsy, and early recognition and treatment can improve seizure frequency and potentially reduce SUDEP risk. Additionally, epilepsy is associated with cyclical patterns, which has led to new treatment approaches including chronotherapy, seizure monitoring devices, and seizure forecasting. Adenosine kinase and orexin receptor antagonists are also promising new potential drug targets that could be used to treat seizures. Sleep and epilepsy have a bidirectional relationship that intersects with many aspects of clinical management. In this article, we identify new areas of research involving future therapeutic opportunities in the field of epilepsy.

Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging

Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.

Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review

The recent advancements in hybrid positron emission tomography–magnetic resonance imaging systems (PET/MRI) have brought massive value in the investigation of disease processes, in the development of novel treatments, in the monitoring of both therapy response and disease progression, and, not least, in the introduction of new multidisciplinary molecular imaging approaches. While offering potential advantages over PET/CT, the hybrid PET/MRI proved to improve both the image quality and lesion detectability. In particular, it showed to be an effective tool for the study of metabolic information about lesions and pathological conditions affecting the brain, from a better tumor characterization to the analysis of metabolic brain networks. Based on the PRISMA guidelines, this work presents a systematic review on PET/MRI in basic research and clinical differential diagnosis on brain oncology and neurodegenerative disorders. The analysis includes literature works and clinical case studies, with a specific focus on the use of PET tracers and MRI contrast agents, which are usually employed to perform hybrid PET/MRI studies of brain tumors. A systematic literature search for original diagnostic studies is performed using PubMed/MEDLINE, Scopus and Web of Science. Patients, study, and imaging characteristics were extracted from the selected articles. The analysis included acquired data pooling, heterogeneity testing, sensitivity analyses, used tracers, and reported patient outcomes. Our analysis shows that, while PET/MRI for the brain is a promising diagnostic method for early diagnosis, staging and recurrence in patients with brain diseases, a better definition of the role of tracers and imaging agents in both clinical and preclinical hybrid PET/MRI applications is needed and further efforts should be devoted to the standardization of the contrast imaging protocols, also considering the emerging agents and multimodal probes.

Towards PErsonalised PRognosis for children with traumatic brain injury: the PEPR study protocol

INTRODUCTION

Traumatic brain injury (TBI) in children can be associated with poor outcome in crucial functional domains, including motor, neurocognitive and behavioural functioning. However, outcome varies between patients and is mediated by complex interplay between demographic factors, premorbid functioning and (sub)acute clinical characteristics. At present, methods to understand let alone predict outcome on the basis of these variables are lacking, which contributes to unnecessary follow-up as well as undetected impairments in children. Therefore, this study aims to develop prognostic models for the individual outcome of children with TBI in a range of important developmental domains. In addition, the potential added value of advanced neuroimaging data and the use of machine learning algorithms in the development of prognostic models will be assessed.

METHODS AND ANALYSIS

210 children aged 4-18 years diagnosed with mild-to-severe TBI will be prospectively recruited from a research network of Dutch hospitals. They will be matched 2:1 to a control group of neurologically healthy children (n=105). Predictors in the model will include demographic, premorbid and clinical measures prospectively registered from the TBI hospital admission onwards as well as MRI metrics assessed at 1 month post-injury. Outcome measures of the prognostic models are (1) motor functioning, (2) intelligence, (3) behavioural functioning and (4) school performance, all assessed at 6 months post-injury.

ETHICS AND DISSEMINATION

Ethics has been obtained from the Medical Ethical Board of the Amsterdam UMC (location AMC). Findings of our multicentre prospective study will enable clinicians to identify TBI children at risk and aim towards a personalised prognosis. Lastly, findings will be submitted for publication in open access, international and peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NL71283.018.19 and NL9051.

Effectiveness of a guideline implementation tool for supporting management of mental health complications after mild traumatic brain injury in primary care: protocol for a randomised controlled trial

INTRODUCTION

Mental health problems frequently interfere with recovery from mild traumatic brain injury (mTBI) but are under-recognised and undertreated. Consistent implementation of clinical practice guidelines for proactive detection and treatment of mental health complications after mTBI will require evidence-based knowledge translation strategies. This study aims to determine if a guideline implementation tool can reduce the risk of mental health complications following mTBI. If effective, our guideline implementation tool could be readily scaled up and/or adapted to other healthcare settings.

METHODS AND ANALYSIS

We will conduct a triple-blind cluster randomised trial to evaluate a clinical practice guideline implementation tool designed to support proactive management of mental health complications after mTBI in primary care. We will recruit 535 adults (aged 18-69 years) with mTBI from six emergency departments and two urgent care centres in the Greater Vancouver Area, Canada. Upon enrolment at 2 weeks post-injury, they will complete mental health symptom screening tools and designate a general practitioner (GP) or primary care clinic where they plan to seek follow-up care. Primary care clinics will be randomised into one of two arms. In the guideline implementation tool arm, GPs will receive actionable mental health screening test results tailored to their patient and their patients will receive written education about mental health problems after mTBI and treatment options. In the usual care control arm, GPs and their patients will receive generic information about mTBI. Patient participants will complete outcome measures remotely at 2, 12 and 26 weeks post-injury. The primary outcome is rate of new or worsened mood, anxiety or trauma-related disorder on the Mini International Neuropsychiatric Interview at 26 weeks.

ETHICS AND DISSEMINATION

Study procedures were approved by the University of British Columbia's research ethics board (H20-00562). The primary report for the trial results will be published in a peer-reviewed journal. Our knowledge user team members (patients, GPs, policymakers) will co-create a plan for public dissemination.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04704037).

Review of Current Principles of the Diagnosis and Management of Brain Metastases

Brain metastases are the most common intracranial tumors and are increasing in incidence as overall cancer survival improves. Diagnosis of brain metastases involves both clinical examination and magnetic resonance imaging. Treatment may involve a combination of surgery, radiotherapy, and systemic medical therapy depending on the patient’s neurologic status, performance status, and overall oncologic burden. Advances in these domains have substantially impacted the management of brain metastases and improved performance status and survival for some patients. Indications for surgery have expanded with improved patient selection, imaging, and intraoperative monitoring. Robust evidence supports the use of whole brain radiotherapy and stereotactic radiosurgery, for both standalone and adjuvant indications, in almost all patients. Lastly, while systemic medical therapy has historically provided little benefit, modern immunotherapeutic agents have demonstrated promise. Current investigation seeks to determine the utility of neoadjuvant radiotherapy and laser interstitial thermal therapy, which have shown benefit in limited studies to date. This article provides a review of the epidemiology, pathology, diagnosis, and treatment of brain metastases and the corresponding supporting evidence.

Can Helicopters Solve the Transport Dilemma for Patients With Symptoms of Large-Vessel Occlusion Stroke in Intermediate Density Areas? A Simulation Model Based on Real Life Data

Background

This modeling study aimed to determine if helicopters may optimize the transportation of patients with symptoms of large vessel stroke in “intermediate density” areas, such as Denmark, by bringing them directly to the comprehensive stroke center.

Methods

We estimated the time for the treatment of patients requiring endovascular therapy or intravenous thrombolysis under four configurations: “drip and ship” with and without helicopter and “bypass” with and without helicopter. Time delays, stroke numbers per municipality, and helicopter dispatches for four helicopter bases from 2019 were obtained from the Danish Stroke and Helicopter Registries. Discrete event simulation (DES) was used to estimate the capacity of the helicopter fleet to meet patient transport requests, given the number of stroke codes per municipality.

Results

The median onset-to-needle time at the comprehensive stroke center (CSC) for the bypass model with the helicopter was 115 min [interquartile range (IQR): 108, 124]; the median onset-to-groin time was 157 min (IQR: 150, 166). The median onset-to-needle time at the primary stroke center (PSC) by ground transport was 112 min (IQR: 101, 125) and the median onset-to-groin time when primary transport to the PSC was prioritized was 234 min (IQR: 209, 261).

A linear correlation between travel time by ground and the number of patients transported by helicopter (rho = 0.69, p < 0.001) indicated that helicopters are being used to transport more remote patients. DES demonstrated that an increase in helicopter capture zone by 20 min increased the number of rejected patients by only 5%.

Conclusions

Our model calculations suggest that using helicopters to transport patients with stroke directly to the CSC in intermediate density areas markedly reduce onset-to-groin time without affecting time to thrombolysis. In this setting, helicopter capacity is not challenged by increasing the capture zone.

Social work, Parkinson's disease care, and COVID-19

Parkinson's Disease is a neurological disease affecting over 10 million people worldwide. Interdisciplinary teams provide integrated care to people with Parkinson's Disease, including care for non-motor symptoms such as anxiety and depression, and many of these teams include social workers. This study sought to learn more about (a) clinical social work utilization across the continuum of care of PWP and their family care partners and (b) how patterns in utilization and service provision have shifted during the pandemic. This mixed method study identifies the breadth of roles performed by social workers in the comprehensive care of people with Parkinson's Disease (PWP). Findings underscore the important roles social workers play in providing comprehensive care for PWP and their families and their contributions to interdisciplinary teams providing holistic, integrated care, particularly during COVID-19 and into the future.

Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients

Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

Prion-like strain effects in tauopathies

Tau is a microtubule-associated protein that plays crucial roles in physiology and pathophysiology. In the realm of dementia, tau protein misfolding is associated with a wide spectrum of clinicopathologically diverse neurodegenerative diseases, collectively known as tauopathies. As proposed by the tau strain hypothesis, the intrinsic heterogeneity of tauopathies may be explained by the existence of structurally distinct tau conformers, “strains”. Tau strains can differ in their associated clinical features, neuropathological profiles, and biochemical signatures. Although prior research into infectious prion proteins offers valuable lessons for studying how a protein-only pathogen can encompass strain diversity, the underlying mechanism by which tau subtypes are generated remains poorly understood. Here we summarize recent advances in understanding different tau conformers through in vivo and in vitro experimental paradigms, and the implications of heterogeneity of pathological tau species for drug development.

Platypnoea-orthodeoxia syndrome and hemidiaphragm paralysis

A woman in her 70s was admitted to hospital with worsening shortness of breath and no prior respiratory history of note. This patient's shortness of breath was posture-dependent; symptoms were markedly worse and oxygen saturations were lower on sitting upright than in recumbency. Her shortness of breath had started several weeks prior to admission and had slowly worsened. Chest X-ray revealed a raised right hemidiaphragm. Further investigation revealed a patent foramen ovale, which was managed with percutaneous closure. This is one of several cases that demonstrate right-to-left shunting through a septal defect secondary to right hemidiaphragmatic paralysis. However, previous reports have not provided a clear guide for management of these cases. We suggest where patients are admitted with new onset breathlessness and platypnoea-orthodeoxia, a septal defect should be suspected. In this report, we have suggested a flowchart for the investigation and management of platypnoea-orthodeoxia syndrome.

A novel compound heterozygous mutation in GALC associated with adult-onset Krabbe disease: case report and literature review

Krabbe disease (KD) is a rare autosomal recessive lipid storage leukodystrophy. It is caused by deficient enzyme activity resulting from mutations of the β-galactocerebrosidase (GALC) gene. KD is distinguished into subtypes based on the age of onset; these are early infantile, late infantile, juvenile, and adult-onset. We report a case of a 47-year-old Caucasian man with a 2-year history of muscle atrophy and weakness in both hands associated with pyramidal signs and mild spasticity in the lower limbs. An extensive work-up led this motor neuron disease-like disorder to be diagnosed as adult-onset KD. The patient was found to be compound heterozygous for two GALC mutations (p.G286D and p.Y490N). These two rare missense mutations have previously been reported with other heterozygous mutations. However, their co-occurrence in a KD patient is novel. From the perspective of this case, we review the current literature on compound heterozygous mutations in adult-onset KD and their phenotypic variability.

The Musculoskeletal Involvement After Mild to Moderate COVID-19 Infection

COVID-19, a disease caused by the novel coronavirus SARS-CoV-2, has been drastically affecting the daily lives of millions of people. COVID-19 is described as a multiorgan disease that affects not only the respiratory tract of infected individuals, but it has considerable effects on the musculoskeletal system, causing excessive fatigue, myalgia, arthralgia, muscle weakness and skeletal muscle damage. These symptoms can persist for months, decreasing the quality of life of numerous individuals. Curiously, most studies in the scientific literature focus on patients who were hospitalized due to SARS-CoV-2 infection and little is known about the mechanism of action of COVID-19 on skeletal muscles, especially of individuals who had the mild to moderate forms of the disease (non-hospitalized patients). In this review, we focus on the current knowledge about the musculoskeletal system in COVID-19, highlighting the lack of researches investigating the mild to moderate cases of infection and pointing out why it is essential to care for these patients. Also, we will comment about the need of more experimental data to assess the musculoskeletal manifestations on COVID-19-positive individuals.

Movement Disorder in Demyelinating Disease: Tracing the Charcot's Foot Print

Movement disorders may be one of the neurological manifestations of demyelinating disorders. They can manifest in Parkinsonism or a wide spectrum of hyperkinetic movement disorders including tremor, paroxysmal dyskinesia, dystonia, chorea, and ballism. Some of these disorders occur during an acute episode of demyelination, whereas others can develop later or even may precede the onset of the demyelinating disorders. The pathophysiology of movement disorders in demyelination is complex and the current evidence indicates a wide involvement of different brain networks and spinal cord. Treatment is mainly symptomatic and oral pharmacological agents are the mainstay of the management. Botulinum toxin and neurosurgical interventions may be required in selected patients.

The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Simple Summary

Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters.

Abstract

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.

Brain metastases: the role of clinical imaging

Imaging of brain metastases (BMs) has advanced greatly over the past decade. In this review, we discuss the main challenges that BMs pose in clinical practice and describe the role of imaging.Firstly, we describe the increased incidence of BMs of different primary tumours and the rationale for screening. A challenge lies in selecting the right patients for screening: not all cancer patients develop BMs in their disease course.Secondly, we discuss the imaging techniques to detect BMs. A three-dimensional (3D) T1W MRI sequence is the golden standard for BM detection, but additional anatomical (susceptibility weighted imaging, diffusion weighted imaging), functional (perfusion MRI) and metabolic (MR spectroscopy, positron emission tomography) information can help to differentiate BMs from other intracranial aetiologies.Thirdly, we describe the role of imaging before, during and after treatment of BMs. For surgical resection, imaging is used to select surgical patients, but also to assist intraoperatively (neuronavigation, fluorescence-guided surgery, ultrasound). For treatment planning of stereotactic radiosurgery, MRI is combined with CT. For surveillance after both local and systemic therapies, conventional MRI is used. However, advanced imaging is increasingly performed to distinguish true tumour progression from pseudoprogression.FInally, future perspectives are discussed, including radiomics, new biomarkers, new endogenous contrast agents and theranostics.

[SORD-related hereditary neuropathies]

Mutations in the SORD gene have recently been identified as a cause of autosomal Charcot-Marie-Tooth disease as well as the underlying defect in some cases of hereditary distal motoneuronopathies. Patients may be amenable to therapies in a near future.