Endovascular Simulator Training and Shadowing in Interventional Radiology: A Comparison of Two Teaching Methods in the Curricular Training of Medical Students.

PURPOSE: To identify the impact of endovascular simulator training and shadowing in interventional radiology on medical students' self-assessed IR knowledge. Moreover, the sequence of the teaching methods and its influence on the self-assessed IR knowledge is investigated. MATERIALS AND METHODS: A total of 19 fourth-year medical students participated in this study. Eleven students completed shadowing live cases first and endovascular simulator training the following day. Eight students completed the teaching in reversed order. Questionnaires were completed before and after each teaching method. The students assessed their knowledge of instruments and materials, steps of the Seldinger technique, and aortography on a Likert scale (1 = "I do not agree at all," 5 = "I fully agree"). RESULTS: After simulator training, the students stated a significant increase in perceived knowledge compared with baseline (p < 0.001). Shadowing led to a significant improvement regarding the items "knowledge of instruments and materials" (3.2 vs. 3.8, p = 0.008) and "steps of the Seldinger technique" (3.7 vs. 3.9, p = 0.046). Self-assessed knowledge after simulator training increased significantly more regarding Seldinger technique compared with shadowing (+ 1.2 vs. + 0.2, p < 0.001). Simulator training before shadowing was significantly more effective regarding the increase in "knowledge of the steps of aortography" compared with the reverse sequence (+ 2.0 vs. + 0.9, p = 0.041). CONCLUSION: Endovascular simulator training and shadowing are both feasible tools to improve medical students' perceived knowledge of interventional radiology. When organizing teaching, simulator training before shadowing can have a positive impact on self-assessed knowledge.

Clinical and imaging manifestations of intracerebral hemorrhage in brain tumors and metastatic lesions: a comprehensive overview.

PURPOSE: This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion. METHODS: We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed. RESULTS: We included 116 patients (tICH: 20/116, 17.24%; mICH: 96/116, 82.76%). The most common malignancies causing ICH were lung cancer (27.59%), malignant melanoma (18.10%) and glioblastoma (10.34%). The three most common stroke-like symptoms observed were focal deficit (62/116, 53.45%), dizziness (42/116, 36.21%) and cognitive impairment (27/116, 23.28%). Highest mICH prevalence was seen in the occipital lobe (mICH: 28.13%, tICH: 0.00%; p = 0.004) with tICH more in the corpus callosum (tICH: 10.00%, mICH: 0.00%; p = 0.029). Anticoagulation therapy was only frequent in mICH patients (tICH: 0.00%, mICH: 5.21%; p = 0.586). Hemorrhage (tICH: 12682 mm3, mICH: 5708 mm3, p = 0.020) and edema volumes (tICH: 49389 mm3, mICH: 20972 mm3, p = 0.035) were significantly larger within tICH patients. CONCLUSION: More than half of the patients with neoplastic ICH exhibited stroke-like symptoms. Lung cancer was most common in mICH, glioblastoma in tICH. While clinical presentations were similar, significant differences in tumor location and treatments were discernible.

Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke.

BACKGROUND: A study was undertaken to assess the effectiveness of open-source large language models (LLMs) in extracting clinical data from unstructured mechanical thrombectomy reports in patients with ischemic stroke caused by a vessel occlusion. METHODS: We deployed local open-source LLMs to extract data points from free-text procedural reports in patients who underwent mechanical thrombectomy between September 2020 and June 2023 in our institution. The external dataset was obtained from a second university hospital and comprised consecutive cases treated between September 2023 and March 2024. Ground truth labeling was facilitated by a human-in-the-loop (HITL) approach, with time metrics recorded for both automated and manual data extractions. We tested three models-Mixtral, Qwen, and BioMistral-assessing their performance on precision, recall, and F1 score across 15 clinical categories such as National Institute of Health Stroke Scale (NIHSS) scores, occluded vessels, and medication details. RESULTS: The study included 1000 consecutive reports from our primary institution and 50 reports from a secondary institution. Mixtral showed the highest precision, achieving 0.99 for first series time extraction and 0.69 for occluded vessel identification within the internal dataset. In the external dataset, precision ranged from 1.00 for NIHSS scores to 0.70 for occluded vessels. Qwen showed moderate precision with a high of 0.85 for NIHSS scores and a low of 0.28 for occluded vessels. BioMistral had the broadest range of precision, from 0.81 for first series times to 0.14 for medication details. The HITL approach yielded an average time savings of 65.6% per case, with variations from 45.95% to 79.56%. CONCLUSION: This study highlights the potential of using LLMs for automated clinical data extraction from medical reports. Incorporating HITL annotations enhances precision and also ensures the reliability of the extracted data. This methodology presents a scalable privacy-preserving option that can significantly support clinical documentation and research endeavors.

Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study.

PURPOSE: Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest. METHODS: Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated. RESULTS: 140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR. CONCLUSION: Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest - A Novel Biomarker for Neuroprognostication.

BACKGROUND: Selective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker. METHODS: We conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort. RESULTS: We included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86-100%) and 43% (moderate) sensitivity (95%-CI: 31-56%). CONCLUSION: This pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Similarities in the Electrographic Patterns of Delayed Cerebral Infarction and Brain Death After Aneurysmal and Traumatic Subarachnoid Hemorrhage.

While subarachnoid hemorrhage is the second most common hemorrhagic stroke in epidemiologic studies, the recent DISCHARGE-1 trial has shown that in reality, three-quarters of focal brain damage after subarachnoid hemorrhage is ischemic. Two-fifths of these ischemic infarctions occur early and three-fifths are delayed. The vast majority are cortical infarcts whose pathomorphology corresponds to anemic infarcts. Therefore, we propose in this review that subarachnoid hemorrhage as an ischemic-hemorrhagic stroke is rather a third, separate entity in addition to purely ischemic or hemorrhagic strokes. Cumulative focal brain damage, determined by neuroimaging after the first 2 weeks, is the strongest known predictor of patient outcome half a year after the initial hemorrhage. Because of the unique ability to implant neuromonitoring probes at the brain surface before stroke onset and to perform longitudinal MRI scans before and after stroke, delayed cerebral ischemia is currently the stroke variant in humans whose pathophysiological details are by far the best characterized. Optoelectrodes located directly over newly developing delayed infarcts have shown that, as mechanistic correlates of infarct development, spreading depolarizations trigger (1) spreading ischemia, (2) severe hypoxia, (3) persistent activity depression, and (4) transition from clustered spreading depolarizations to a negative ultraslow potential. Furthermore, traumatic brain injury and subarachnoid hemorrhage are the second and third most common etiologies of brain death during continued systemic circulation. Here, we use examples to illustrate that although the pathophysiological cascades associated with brain death are global, they closely resemble the local cascades associated with the development of delayed cerebral infarcts.

Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.

Objective: To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format. Methods: 168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images). Results: All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping. Conclusion: Our proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.

Cortical matrix remodeling as a hallmark of relapsing-remitting neuroinflammation in MR elastography and quantitative MRI.

Multiple sclerosis (MS) is a chronic neuroinflammatory disease that involves both white and gray matter. Although gray matter damage is a major contributor to disability in MS patients, conventional clinical magnetic resonance imaging (MRI) fails to accurately detect gray matter pathology and establish a clear correlation with clinical symptoms. Using magnetic resonance elastography (MRE), we previously reported global brain softening in MS and experimental autoimmune encephalomyelitis (EAE). However, it needs to be established if changes of the spatiotemporal patterns of brain tissue mechanics constitute a marker of neuroinflammation. Here, we use advanced multifrequency MRE with tomoelastography postprocessing to investigate longitudinal and regional inflammation-induced tissue changes in EAE and in a small group of MS patients. Surprisingly, we found reversible softening in synchrony with the EAE disease course predominantly in the cortex of the mouse brain. This cortical softening was associated neither with a shift of tissue water compartments as quantified by T2-mapping and diffusion-weighted MRI, nor with leukocyte infiltration as seen by histopathology. Instead, cortical softening correlated with transient structural remodeling of perineuronal nets (PNNs), which involved abnormal chondroitin sulfate expression and microgliosis. These mechanisms also appear to be critical in humans with MS, where tomoelastography for the first time demonstrated marked cortical softening. Taken together, our study shows that neuroinflammation (i) critically affects the integrity of PNNs in cortical brain tissue, in a reversible process that correlates with disease disability in EAE, (ii) reduces the mechanical integrity of brain tissue rather than leading to water accumulation, and (iii) shows similar spatial patterns in humans and mice. These results raise the prospect of leveraging MRE and quantitative MRI for MS staging and monitoring treatment in affected patients.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

BACKGROUND: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model.

Purpose: To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Methods: Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated. Results: The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Conclusion: Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Diffusion-weighted magnetic resonance imaging in early central retinal artery occlusion.

INTRODUCTION: Restricted retinal diffusion (RDR) has recently been recognized as a frequent finding on standard diffusion-weighted imaging (DWI) in central retinal artery occlusion (CRAO). However, data on early DWI signal evolution are missing. PATIENTS AND METHODS: Consecutive CRAO patients with DWI performed within 24 h after onset of visual impairment were included in a bicentric, retrospective cross-sectional study. Two blinded neuroradiologists assessed randomized DWI scans for the presence of retinal ischemia. RDR detection rates, false positive ratings, and interrater agreement were evaluated for predefined time groups. RESULTS: Sixty eight CRAO patients (68.4 ± 16.8 years; 25 female) with 72 DWI scans (76.4% 3 T, 23.6% 1.5 T) were included. Mean time-delay between onset of CRAO and DWI acquisition was 13.4 ± 7.0 h. Overall RDR detection rates ranged from 52.8% to 62.5% with false positive ratings in 4.2%-8.3% of cases. RDR detection rates were higher in DWI performed 12-24 h after onset, when compared with DWI acquired within the first 12 h (79.5%vs 39.3%, p < 0.001). The share of false positive ratings was highest for DWI performed within the first 6 h of symptom onset (up to 14.3%). Interrater reliability was "moderate" for DWI performed within the first 18 h (κ = 0.57-0.58), but improved for DWI acquired between 18 and 24 h (κ = 0.94). CONCLUSION: DWI-based detection of retinal ischemia in early CRAO is likely to be time-dependent with superior diagnostic accuracy for DWI performed 12-24 h after onset of visual impairment.

Spreading depolarization and angiographic spasm are separate mediators of delayed infarcts.

In DISCHARGE-1, a recent Phase III diagnostic trial in aneurysmal subarachnoid haemorrhage patients, spreading depolarization variables were found to be an independent real-time biomarker of delayed cerebral ischaemia. We here investigated based on prospectively collected data from DISCHARGE-1 whether delayed infarcts in the anterior, middle, or posterior cerebral artery territories correlate with (i) extravascular blood volumes; (ii) predefined spreading depolarization variables, or proximal vasospasm assessed by either (iii) digital subtraction angiography or (iv) transcranial Doppler-sonography; and whether spreading depolarizations and/or vasospasm are mediators between extravascular blood and delayed infarcts. Relationships between variable groups were analysed using Spearman correlations in 136 patients. Thereafter, principal component analyses were performed for each variable group. Obtained components were included in path models with a priori defined structure. In the first path model, we only included spreading depolarization variables, as our primary interest was to investigate spreading depolarizations. Standardised path coefficients were 0.22 for the path from extravascular bloodcomponent to depolarizationcomponent (P = 0.010); and 0.44 for the path from depolarizationcomponent to the first principal component of delayed infarct volume (P < 0.001); but only 0.07 for the direct path from bloodcomponent to delayed infarctcomponent (P = 0.36). Thus, the role of spreading depolarizations as a mediator between blood and delayed infarcts was confirmed. In the principal component analysis of extravascular blood volume, intraventricular haemorrhage was not represented in the first component. Therefore, based on the correlation analyses, we also constructed another path model with bloodcomponent without intraventricular haemorrhage as first and intraventricular haemorrhage as second extrinsic variable. We found two paths, one from (subarachnoid) bloodcomponent to delayed infarctcomponent with depolarizationcomponent as mediator (path coefficients from bloodcomponent to depolarizationcomponent = 0.23, P = 0.03; path coefficients from depolarizationcomponent to delayed infarctcomponent = 0.29, P = 0.002), and one from intraventricular haemorrhage to delayed infarctcomponent with angiographic vasospasmcomponent as mediator variable (path coefficients from intraventricular haemorrhage to vasospasmcomponent = 0.24, P = 0.03; path coefficients from vasospasmcomponent to delayed infarctcomponent = 0.35, P < 0.001). Human autopsy studies shaped the hypothesis that blood clots on the cortex surface suffice to cause delayed infarcts beneath the clots. Experimentally, clot-released factors induce cortical spreading depolarizations that trigger (i) neuronal cytotoxic oedema and (ii) spreading ischaemia. The statistical mediator role of spreading depolarization variables between subarachnoid blood volume and delayed infarct volume supports this pathogenetic concept. We did not find that angiographic vasospasm triggers spreading depolarizations, but angiographic vasospasm contributed to delayed infarct volume. This could possibly result from enhancement of spreading depolarization-induced spreading ischaemia by reduced upstream blood supply.

The Acute Optic Neuritis Network (ACON): Study protocol of a non-interventional prospective multicenter study on diagnosis and treatment of acute optic neuritis.

Optic neuritis (ON) often occurs at the presentation of multiple sclerosis (MS), neuromyelitis optica spectrum disorders (NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD). The recommended treatment of high-dose corticosteroids for ON is based on a North American study population, which did not address treatment timing or antibody serostatus. The Acute Optic Neuritis Network (ACON) presents a global, prospective, observational study protocol primarily designed to investigate the effect of time to high-dose corticosteroid treatment on 6-month visual outcomes in ON. Patients presenting within 30 days of the inaugural ON will be enrolled. For the primary analysis, patients will subsequently be assigned into the MS-ON group, the aquapotin-4-IgG positive ON (AQP4-IgG+ON) group or the MOG-IgG positive ON (MOG-IgG+ON) group and then further sub-stratified according to the number of days from the onset of visual loss to high-dose corticosteroids (days-to-Rx). The primary outcome measure will be high-contrast best-corrected visual acuity (HC-BCVA) at 6 months. In addition, multimodal data will be collected in subjects with any ON (CIS-ON, MS-ON, AQP4-IgG+ON or MOG-IgG+ON, and seronegative non-MS-ON), excluding infectious and granulomatous ON. Secondary outcomes include low-contrast best-corrected visual acuity (LC-BCVA), optical coherence tomography (OCT), magnetic resonance imaging (MRI) measurements, serum and cerebrospinal fluid (CSF) biomarkers (AQP4-IgG and MOG-IgG levels, neurofilament, and glial fibrillary protein), and patient reported outcome measures (headache, visual function in daily routine, depression, and quality of life questionnaires) at presentation at 6-month and 12-month follow-up visits. Data will be collected from 28 academic hospitals from Africa, Asia, the Middle East, Europe, North America, South America, and Australia. Planned recruitment consists of 100 MS-ON, 50 AQP4-IgG+ON, and 50 MOG-IgG+ON. This prospective, multimodal data collection will assess the potential value of early high-dose corticosteroid treatment, investigate the interrelations between functional impairments and structural changes, and evaluate the diagnostic yield of laboratory biomarkers. This analysis has the ability to substantially improve treatment strategies and the accuracy of diagnostic stratification in acute demyelinating ON. Trial registration: ClinicalTrials.gov, identifier: NCT05605951.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.

PURPOSE: MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation. METHODS: We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment. RESULTS: We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation. CONCLUSION: We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest.

Background: Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods. Methods: Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication. Results: Inter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction. Conclusion: Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.

Prognostic accuracy of head computed tomography for prediction of functional outcome after out-of-hospital cardiac arrest: Rationale and design of the prospective TTM2-CT-substudy.

Background: Head computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA. Methods/design: This is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of "severe HIE". Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0-3) and poor (modified Rankin Scale 4-6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines. Conclusions: The results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.

[68Ga]Ga-PSMA PET/MRI, histological PSMA expression and preliminary experience with [177Lu]Lu-PSMA therapy in relapsing high-grade glioma.

Purpose: High-grade gliomas (HGG) are still associated with a dismal prognosis. Prostate specific membrane antigen (PSMA) is discussed as a theranostic target for PSMA-directed radioligand therapy ([177Lu]Lu-PSMA RLT). Here, we report on the correlation of [68Ga]Ga-PSMA uptake with histological PSMA expression and on our preliminary experience with [177Lu]Lu-PSMA RLT in relapsing HGG. Methods: Patients with relapsing HGG underwent [68Ga]Ga-PSMA PET/MRI to evaluate eligibility for an individualized treatment approach with [177Lu]Lu-PSMA. Standard uptake values (SUV) for tumor and liver and respective tumor-to-background ratios (compared to the liver) (TBR) on [68Ga]Ga-PSMA PET/MRI were assessed. Eligibility criteria for [177Lu]Lu-PSMA therapy were exhaustion of all standard treatment options available and TBRmax>1.0. In 11 samples, immunohistochemical PSMA expression was determined, quantified using the H-score and correlated with uptake on [68Ga]Ga-PSMA PET/MRI. Results: We included 20 patients with a median age of 53 years (IQR 42-57). The median SUV on [68Ga]Ga-PSMA PET/MRI was 4.5 (3.7-6.2) for SUVmax and 1.4 (1.1-1.7) for SUVmean. The respective TBR was maximum 0.6 (0.4-0.8) and mean 0.3 (0.2-0.4). High TBRmax correlated with increased endothelial PSMA expression [H-score of 65 (62.5-77.5)]. Three patients (15%) presented a TBRmax>1.0 and qualified for [177Lu]Lu-PSMA RLT. No treatment related toxicity was observed. Conclusion: Only a minority of patients with relapsing HGG qualified for [177Lu]Lu-PSMA RLT. Our data demonstrates that PSMA expression in the neo-vasculature corresponds to PSMA uptake on [68Ga]Ga-PSMA PET/MRI and might be used as a screening tool for patient selection. Future prospective studies need to focus the debate on TBRmax thresholds as inclusion criteria for PSMA RLT.

Impaired recollection and initially preserved familiarity in a patient with bilateral fornix transection following third ventricle colloid cyst removal: A two-year follow-up study.

OBJECTIVE: Recognition memory is widely accepted as a dual process-based model, namely familiarity and recollection. However, the location of their specific neurobiological substrates remains unclear. Similar to hippocampal damage, fornix damage has been associated with recollection memory but not familiarity memory deficits. To understand the neural basis of recognition memory, determining the importance of the fornix and its hippocampal connections is essential. METHODS: Recognition memory was examined in a 45-year-old male who underwent a complete bilateral fornix section following the removal of a third ventricle colloid cyst. The application of familiarity and recollection for recognition memory decisions was investigated via an immediate and delayed associative recognition test and an immediate and delayed forced-choice task in the patient and a control group (N = 15) over a two-year follow-up period. Complete demographic, neuropsychological, neuropsychiatric, and neuroradiological characterizations of this patient were performed. RESULTS: Persistent immediate and delayed verbal recollection memory deficits were observed in the patient. Moreover, delayed familiarity-based recognition memory declined gradually over the follow-up period, immediate familiarity-based recognition memory was unaffected, and reduced non-verbal memory improved. CONCLUSION: The present findings support models that the extended hippocampal system, including the fornices, does not appear to play a role in familiarity memory but is particularly important for recollection memory. Moreover, our study suggests that bilateral fornix transection may be associated with relatively functional recovery of non-verbal memory.