Stroke: Vascular and Interventional Neurology

PurposeIntracranial atherosclerotic disease-related large vessel occlusion (ICAD-LVO) presents distinct challenges, particularly regarding the high risk of reocclusion and the need for specific management strategies. While several prediction scores exist to differentiate ICAD-LVO from embolic LVO (EMB-LVO), their external validity remains unproven. We aimed to externally validate six established prediction scores for differentiating the two. MethodsWe analyzed data from a prospectively maintained, two-center stroke registry (June 2021-March 2025). Consecutive patients who underwent mechanical thrombectomy and had complete clinical and imaging data necessary for calculating six scores (ISAT, REMIT, ABC2D, ATHE, ICAS-LVO, and Score-ICAD) were included. LVO etiology was defined based on angiographic findings during endovascular treatment. The discriminative performance of each score was assessed using the area under the receiver operating characteristic curve (AUC). ResultsOf 1,288 screened admissions, 91 patients met the inclusion criteria (ICAD-LVO, n = 18; embolic occlusion, n = 73). The AUCs (95% confidence interval) for differentiating etiology were: ISAT, 0.870 (0.664-1.000; P = 0.064); REMIT, 0.793 (0.676-0.911; P <0.001); Score-ICAD, 0.707 (0.582-0.833; P = 0.013); ABC2D, 0.627 (0.504-0.751; P = 0.095); ATHE, 0.600 (0.451-0.749; P = 0.230); and ICAS-LVO, 0.465 (0.301-0.630; P = 0.650). ConclusionIn this external validation, REMIT demonstrated the most robust and statistically significant discrimination between ICAD-LVO and EMB-LVO. Overall, scores incorporating imaging features outperformed those relying on clinical variables. These findings support the concept that ICAD-LVO represents a distinct pathophysiological entity from embolic occlusion and that accurate mechanism inference requires comprehensive imaging assessment of intracranial atherosclerotic disease beyond the occlusion site.

BackgroundSuccessful recanalisation without functional independence is a frequent phenomenon following endovascular thrombectomy for large vessel occlusion stroke. AimTo demonstrate safety and efficacy of adjunct tirofiban therapy after endovascular thrombectomy in patients with anterior circulation large vessel occlusion stroke achieving successful recanalization defined as modified Thrombolysis In Cerebral Infarction (mTICI) 2b-3. DesignThe study of adjunct tirofiban treatment after successful endovascular thrombectomy recanalisation (ATTRACTION) is a multicenter, prospective, double-blind, randomized trial enrolling 1360 patients in China. Eligible patients will be randomised 1:1 to either the tirofiban or placebo group. OutcomeThe primary efficacy outcomes is assessed as the proportion of participants with a modified Rankin Scale (mRS) score of 0-2 at 90 days, and the primary safety outcome is symptomatic intracranial haemorrhage within 48 hours from randomisation. ConclusionThis study will provide evidence on the efficacy and safety of sequential tirofiban therapy after successful recanalisation in patients with anterior circulation large vessel occlusion stroke. Trial registration numberNCT06265051 WHAT IS ALREADY KNOWN ON THIS TOPICSuccessful recanalization without functional independence is a frequent phenomenon following endovascular thrombectomy and previous small-sample, retrospective studies supported the administration of adjunct tirofiban therapy in patients after endovascular thrombectomy achieving successful recanalization. WHAT THIS STUDY ADDSThe ATTRACTION trial aims to access the efficacy and safety of adjunct tirofiban therapy and the protocol describes the rationale and design of the trial. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYATTRACTION trial will inform whether tirofiban therapy after successful recanalisation by endovascular thrombectomy can improve patient outcomes.

Background: Platform trials are an efficient trial design which enable testing of multiple interventions simultaneously. They could advance knowledge of treatments for intracerebral haemorrhage (ICH). We aimed to investigate the views of clinicians involved in stroke research on recruitment to a future platform trial for ICH. Methods: Between April and July 2025, we conducted a UK-wide online survey of clinicians actively involved in stroke research using convenience sampling through professional organisations. Participants considered factors related to the consent process and research environment and could provide optional free text responses about additional barriers or facilitators to recruitment. We used descriptive statistics for quantitative data and content analysis for qualitative data. Results: Among 73 respondents, 46 (63%) were female, 36 (50%) were stroke physicians, 24 (34%) nurses, 6 (8%) allied health professionals, and 7 (10%) were in other roles. 36 (49%) had >20 years of clinical experience, 45 (61%) reported spending <10% of their role in research. 66 (91%) thought that a platform trial would be a good option for testing interventions for patients with stroke due to ICH. Across 11 modifiable factors, clinicians most frequently rated perceived importance of the research question as a facilitator of recruitment (94%), while clinician preference for specific treatments was most frequently rated as a barrier (48%). Two themes emerged from free text responses: study design and infrastructure. Regarding study design respondents perceived consent procedures (n=9), study materials (n=8), study procedures (n=8), eligibility assessment (n=6), the research question (n=3) and randomization (n=3) as important for a future platform trial. Regarding infrastructure, emergent factors were staffing (n=17), local research culture and capacity (n=9), research governance and delivery (n=6), and training (n=6). Conclusion: The overwhelming majority of respondents from the UK clinical stroke community supported a platform trial for ICH, although the influence of survey responder bias is unknown.

AbstractsO_ST_ABSBackgroundC_ST_ABSSelf-management is essential for stroke survivors to maintain a healthy lifestyle and reduce recurrence risk. Although theory-based self-management interventions are widely recommended, the theoretical frameworks underpinning them and their comparative effectiveness remain unclear. AimsTo systematically identify the theories, models, and frameworks (TMFs) used in self-management interventions for stroke survivors, to explore how they guide interventions, and evaluate their effectiveness on self-management behaviors and self-efficacy. MethodsPubMed, Embase, Web of Science, ProQuest Health & Medical Collection and the Cochrane Library were searched from inception to July 15, 2025. Randomized controlled trials or quasi-experimental studies evaluating theory-based self-management interventions for stroke survivors were included. Two reviewers independently screened studies, extracted data, and assessed risk of bias (Cochrane RoB 2.0). Meta-analyses were performed using random-effects models. ResultsFrom 11,495 records, 32 studies with 3,212 participants were included. Sixteen distinct TMFs were identified; self-efficacy theory was most frequent (13/32), followed by social cognitive theory (6/32). All TMFs were middle-range theories. Meta-analysis showed TMFs-based interventions significantly improved self-management behaviors (SMD = 4.26, 95%CI: 0.20-8.31, I{superscript 2} = 98.2%) and self-efficacy (SMD = 0.60, 95%CI: 0.32-0.88, I{superscript 2} = 72.8%). However, the effect for behaviors is likely inflated due to extreme heterogeneity and theoretical diversity. Theory-specific analysis of self-efficacy theory (k = 8) confirmed significant effects on self-efficacy (SMD = 0.64, 95%CI: 0.21-1.08). ConclusionsThis review identified 16 distinct theoretical models; self-efficacy theory was most frequently applied, followed by social cognitive theory. Theory-based interventions significantly improved self-management behaviours and self-efficacy.

ObjectiveTo compare early cerebrospinal fluid (CSF) cytokine profiles in intracerebral hemorrhage (ICH) versus subarachnoid hemorrhage (SAH), with a focus on angiography-negative SAH (anSAH). MethodsWe conducted a retrospective observational cohort study of adults with spontaneous hemorrhagic stroke (ICH or SAH). For cytokine analyses, we included patients with external ventricular drains (EVDs) and analyzed the first CSF sample obtained within 72 hours of symptom onset. Cytokines were measured using a multiplex bead-based assay and included interleukin-6 (IL-6), interleukin-8 (IL-8), vascular endothelial growth factor A (VEGF-A), C-C motif chemokine ligand-2 (CCL2), and granulocyte colony-stimulating factor (G-CSF). Cytokine concentrations were log-transformed due to non-normal distribution. Functional outcomes were assessed using the modified Rankin Scale (mRS) at discharge and 3 months. ResultsCSF cytokine analyses included 120 patients with available CSF samples (43 ICH and 77 SAH), while functional outcome analyses included a broader cohort of 490 patients with ICH or SAH to characterize discharge and 3-month outcomes across hemorrhage subtypes. Compared with SAH, ICH demonstrated higher early CSF log[IL-8] and log[VEGF-A] and had worse functional outcomes at discharge and 3 months. Within SAH, anSAH had higher log[IL-8] and log[VEGF-A] than aSAH, and its cytokine profile more closely aligned with that of primary ICH in hemorrhages without vascular malformations. DiscussionEarly CSF cytokine patterns suggest anSAH shares a more ICH-like inflammatory signature than aneurysmal SAH, supporting anSAH as a potentially biologically distinct SAH phenotype.

BackgroundEndovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and validated machine-learning (ML) models that integrate automated quantitative cerebrovascular morphology and collateral grading with demographic, clinical, laboratory, and imaging variables to predict major post-EVT complications and early neurological outcomes. MethodsUsing a prospectively collected database of 727 AIS patients that underwent EVT, we developed ML models to incorporate patient-specific vascular morphometry with conventional clinical, laboratory, and imaging data to predict emergence of early neurological deterioration (END), symptomatic intracranial hemorrhage (sICH), malignant brain edema (MBE) requiring surgical decompression, and neurogenic respiratory failure and dysphagia requiring tracheostomy/gastrostomy (TC/PEG). ResultsOur analysis of morphological features, including increased tortuosity and reduced vessel diameter, showed strong associations with complications. Morphology-informed (MI) models consistently outperformed baseline-clinical (BC) models for patients with END (AUROC 0.81 for MI model vs. 0.73 for BC), sICH (AUROC 0.68 MI vs. 0.56 BC model), MBE (AUROC 0.67 MI model vs. 0.56 BC), or patients who underwent TC/PEG (AUROC 0.66MI vs. 0.58 BC model). Statistical testing confirmed significant AUROC improvements for END, sICH and mRS (p < 0.05), Finally, patient-specific calibrated probability profiles enabled individualized, multidimensional risk stratification, revealing distinct complication-specific risk patterns across patients. ConclusionsThese findings demonstrate that cerebrovascular structure--an often overlooked yet physiologically fundamental determinant of ischemic injury and reperfusion dynamics--provides significant predictive information that is not captured by standard clinical or visual imaging assessments. Automated vascular segmentation and collateral grading techniques enable rapid and objective integration of cerebrovascular metrics into prognostic models, offering a scalable tool for precision risk stratification, supporting earlier intervention, targeted monitoring, and improved post-EVT management.

ImportanceLarge language models (LLMs) offer potential decision support, but their accuracy varies. Prompt engineering can generally enhance LLM behavior in a clinical context, yet best practices have yet to be formally explored in realistic neurology settings. ObjectiveTo evaluate the impact of structured prompting versus simple prompting on the performance of six LLMs (three closed-source: OpenAI GPT-4o, OpenAI o3, OpenAI GPT-5.2 Thinking; three open-source: Meta Llama-4-Scout-17B-16E-Instruct, Llama-3.3-70B-Instruct-Turbo, and the reasoning model R1-1776) for thrombolytic clinical decision support (CDS) in acute stroke. DesignModels responded to three novel ischemic stroke vignettes using either a simple question ("Should this patient be offered thrombolytics?") or a five-step structured prompt (CARDS) guiding information extraction, timing analysis, contraindication checking, decision process explanation, and risk-benefit discussion. Outputs were assessed across seven domains: guideline adherence, unsafe recommendations, risk recognition, guideline grading accuracy, inclusion of conversational explanation, clarity, and overall helpfulness. ResultsStructured prompts significantly enhanced performance across most domains, with varying effects between model families. For some closed-source models (GPT-4o, o3), prompts structured in the CARDS style improved guideline adherence from 83.3% to 100%, eliminated unsafe recommendations (16.7% to 0%), and increased specific guideline grading accuracy from 0% to 100%. The closed-source reasoning model GPT-5.2 Thinking similarly achieved 100% adherence, 0% unsafe recommendations, and 100% grading accuracy with structured prompts, while also maintaining perfect safety and risk recognition under simple prompting. Similarly, the open-source reasoning model R1-1776 achieved these top-tier outcomes (100% adherence, 0% unsafe, 100% grading, 100% conversation) when structured prompts were applied, with grading and conversation improving from 0%. In contrast, other open-source models (Llama-4-Scout, Llama-3.3-70B) showed more modest gains: risk recognition improved (83.3% to 100%) and guideline grading accuracy increased (0% to 66.7%), while guideline adherence (66.7%) and unsafe recommendations (33.3%) persisted. Overall, structured prompting yielded the largest improvements in guideline grading accuracy and conversational reasoning across multiple models. ConclusionStructured prompting substantially enhances LLM performance for acute stroke thrombolysis CDS. Notably, some models, including the proprietary GPT-4o, o3, and GPT-5.2 Thinking, and the open-source reasoning model R1-1776, achieved excellent safety and adherence with structured prompts. For clinical deployment of any LLM, structured prompts are crucial, and vigilant human oversight remains essential.

BackgroundLarge middle cerebral artery (MCA) infarctions can result in life-threatening cerebral edema. Quantitative brain atrophy may improve risk stratification for severe edema. We examined whether quantitative brain atrophy is associated with severe midline shift after large ischemic stroke and whether incorporating atrophy improves prediction beyond established clinical and radiographic predictors. MethodsThis was a retrospective observational cohort study of patients with [&ge;][1/2] MCA ischemic infarction, presentation within 24 hours of last known well, and at least one follow-up head CT, admitted to two academic hospitals with comprehensive stroke centers between 2006 and 2024. The study was approved by the institutional review boards of both centers. Brain atrophy was quantified as the inverse of standardized brain volume on admission head CT. The primary outcome was severe radiographic mass effect, defined as midline shift [&ge;]5 mm on follow-up CT. The secondary outcome was in-hospital mortality. Multivariable regression models assessed associations between quantified atrophy and outcomes. Incremental prognostic value was evaluated by comparing models with and without atrophy using measures of goodness of fit, calibration, and discrimination. ResultsAmong 565 patients (mean age 67.5{+/-}15.7 years; 49.9% female), 223 (39.5%) developed severe mass effect. Greater atrophy was associated with lower odds of midline shift [&ge;]5 mm (OR 0.44, 95% CI 0.34-0.58), but not with in-hospital mortality. Incorporation of atrophy significantly improved prediction of severe mass effect compared to the baseline model (likelihood ratio test {chi}{superscript 2} (1) = 41, p <0.001; AIC 703 vs. 741; BIC 733 vs. 767; AUC 0.68 vs. 0.60). ConclusionsQuantified brain atrophy is independently associated with a reduced risk of severe mass effect after large MCA stroke and improved the performance of established predictive models. Incorporation of this imaging biomarker may enhance early risk stratification, monitoring, and intervention planning for patients at risk of life-threatening cerebral edema.

PurposePortal hypertension, a major complication of chronic liver disease, leads to significant morbidity and mortality. While portal vein diameter measured on imaging has long been proposed as a non-invasive marker of portal hypertension, normative CT-based reference values and population-level associations remain incompletely characterized. Here, we aim to define contemporary reference values for portal vein diameter on clinically obtained CT and evaluate its associations with demographic, clinical, and imaging factors, as well as its diagnostic performance for portal hypertension. MethodsWe conducted a retrospective analysis of 20,225 clinically obtained CT scans at a single academic medical center. The main portal vein was automatically segmented using Total Segmentator, and maximum diameter extracted using the Vascular Modeling Toolkit. Associations with demographic and imaging factors were evaluated using linear mixed-effects models; prevalent liver disease and portal hypertension using logistic regression; risk of incident ascites and esophageal varices among participants with liver disease using Cox regression; and invasive hepatic venous pressures using correlation analysis and linear regression. ResultsThe mean portal vein diameter was 12.4 mm (95% CI, 12.37-12.45). Larger diameter was independently associated with male sex (+1.4 mm), higher BMI (+0.11 mm/kg/m2), greater height (+0.04 mm/cm), and older age (+0.05 mm/10 years) (all p <0.001), and was substantially larger on contrast-enhanced abdomen/pelvis CT (+2.4 mm, p <0.001). Each 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06; 95% CI, 1.04-1.08) and portal hypertension (OR 1.18; 95% CI, 1.12-1.28). Among individuals with liver disease, greater diameter predicted higher risk of incident esophageal varices (baseline diameter HR 1.50; 95% CI, 1.14-2.08) and ascites (HR per mm increase in diameter 1.06; 95% CI, 1.003-1.12). However, portal vein diameter demonstrated weak to no association with invasively measured hepatic venous pressures. ConclusionIn this large, EHR-linked imaging cohort, the mean portal vein diameter on CT was 12.4 mm and varied with demographic and imaging factors. Larger diameter was associated with liver disease, portal hypertension, and subsequent development of varices and ascites, supporting use of portal vein diameter as a pragmatic screening or enrichment tool within multimodal clinical frameworks. Key ResultsO_LIMean portal vein diameter on routine clinical CT was 12.4 mm (95% CI, 12.37-12.45) and varied with sex, height, BMI, exam type, contrast use, and clinical setting. C_LIO_LIEach 1-mm increase in portal vein diameter was associated with higher odds of prevalent liver disease (OR 1.06) and portal hypertension (OR 1.18). C_LIO_LIAmong individuals with liver disease, larger portal vein diameter predicted higher risk of incident esophageal varices and ascites, independent of demographic and imaging factors. C_LI

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables non-invasive characterization of carotid atherosclerotic plaque. PurposeTo evaluate the performance and reproducibility of a simplified DCE-MRI quantification method for carotid plaque assessment. MethodsT1-weighted black-blood DCE-MRI of the carotid arteries at 3T was performed at baseline and after six months in patients with mild-to-moderate atherosclerotic lesions in a pilot placebo-controlled randomized trial evaluating the effects of low-dose (0.5mg daily) colchicine therapy on carotid plaque volume. DCE-MRI signal intensity was measured in manually drawn regions of interest in the plaque core, remote non-atherosclerotic vessel wall, and skeletal muscle. Peak signal intensities were normalized to skeletal muscle signal in the same slice. ResultsIn patients (n=28, median [interquartile range] age 72 [64-74] years, 36% female, n=13/15 colchicine/placebo), normalized peak signal intensity was higher in the plaque core than in the remote vessel wall at both baseline (3.5 [2.3-4.1] vs 2.1 [1.7-2.5], p<0.001) and follow-up (3.2 [2.5-4.4] vs 2.0 [1.7-2.5], p<0.001). Measurements did not differ between baseline and follow-up for all patients (0.7{+/-}0.7 for plaque core, 0.6{+/-}0.4 for remote vessel wall, p>0.80 for both) nor between colchicine intervention and placebo control (p>0.35 for either region). ConclusionsNormalised peak signal intensity on DCE-MRI was consistently higher in the carotid plaque core than in the remote vessel wall, showed excellent reproducibility in both regions over six months, and was not altered by colchicine treatment. This simplified, muscle-normalised approach may facilitate future studies exploring DCE-MRI measures potentially related to plaque vulnerability.

ImportancePrognosticating functional independence after an acute stroke is critical for anticipatory guidance and rehabilitation planning. Here we demonstrate that poor brain health at the time of incident stroke is linked to worse functional outcomes for women compared to men. ObjectiveTo determine if brain health at time of stroke presentation has a differential effect on functional outcomes between men and women. DesignRetrospective cross-sectional study. SettingAnalysis conducted in 2025 with multi-center patient data that included participants from two large acute ischemic stroke cohorts; local (GASROS) and multinational (MRI-GENIE) between the years 2003 and 2011. ParticipantsClinical data collected for enrolled study participants included demographic data, medical history of hypertension, diabetes mellitus, hyperlipidemia, smoking status, acute stroke severity as measured by National Institutes of Health Stroke Scale (NIHSS), stroke etiology, and modified Rankin Scale (mRS) score at 90 days post-stroke. Brain health was quantified as effective reserve derived from acute neuroimaging data. Exposure(s)designated sex, retrieved from registration records. Main OutcomeFunctional outcome was measured by mRS scores at 90 days post-stroke, in men and women with poor, moderate, or good brain health at time of stroke injury. ResultsA total of 1039 patients were included in the analysis, 37.8 % women, median age 67 [interquartile range 56-77]. Women with poor brain health (i.e. lowest quartile of effective reserve) had worse functional outcomes at 90 days (55.6% with mRS>2) compared to men with poor brain health (31.2% with mRS>2: p < 0.001). This difference between men and women was not observed in categories of moderate or good brain health. There was no observed significant difference in stroke severity, volume of acute lesion, burden of white matter hyperintensities, or stroke etiology between men and women with poor brain health. Conclusions and RelevanceBrain health at the time of incident stroke has a differential effect on functional outcomes at 90 days between men and women. Women with poor brain health endure disproportionately worse outcomes compared to men. This highlights an important step in understanding sex-specific vulnerability in early recovery post-stroke, and can inform disposition, rehabilitation services, and resource allocation planning.

IntroductionIdiopathic normal pressure hydrocephalus (iNPH) is a partially reversible neurological disorder in which imaging biomarkers support diagnosis and surgical decision-making. The callosal angle (CA) is one of the most robust radiological markers of iNPH and has also been associated with postoperative shunt outcome. However, several manual measurement variants exist and artificial intelligence (AI)-based tools now enable automatic CA measurement. Materials and MethodsIn total 71 patients (40 with confirmed iNPH and 31 controls) were included. Six predefined manual methods for measuring CA were applied to preoperative 3D T1-weighted MRI and evaluated for diagnostic performance and interobserver agreement. An AI-derived automatic CA (cMRI from Combinostics) was included as a seventh method and compared with the traditional manual method (perpendicular to the bicommissural plane and through the posterior commissure). Automatic measurements were additionally assessed in pre- and postoperative scans to evaluate robustness against shunt-related artifacts. ResultsAll seven CA variants significantly differentiated iNPH patients from controls (p < 0.05). The traditional method showed the highest discriminative performance (AUC = 0.986, SE = 0.012), while alternative planes demonstrated slightly lower accuracy (AUC range = 0.957-0.978). Interobserver agreement for manual measurements was good to excellent (ICC = 0.687-0.977). Automatic CA measurements showed excellent correlation with the traditional method, preoperative ICC = 0.92; postoperative ICC = 0.96. ConclusionAlthough several CA positions perform comparably, the traditional method remains marginally superior and is best supported by the literature. Automated CA measurements closely match expert manual assessment in pre- and postoperative imaging, supporting clinical implementation.

BackgroundDystonia is a debilitating movement disorder that is difficult to assess when co-existing with spasticity, as is typical in cerebral palsy (CP). Querying caregivers about their childrens movements is known to increase clinical dystonia identification. However, beyond identification, determining whether dystonia is the predominant vs. accompanying movement feature in a child with CP can guide clinical decision making, particularly regarding surgical candidacy. ObjectiveTo determine whether caregivers movement descriptions differed between children with predominant dystonia, predominant spasticity with accompanying dystonia, and predominant spasticity without dystonia. MethodsIn this cross-sectional study, we used conventional content analysis to codify caregivers descriptions of triggered involuntary movements in children with CP seen in a tertiary care CP center between 4/2023 and 12/2024. Movement feature frequencies were compared across tone types using Chi-square tests with Bonferroni corrections for multiple comparisons. ResultsOf 180 children with CP (mean age 9.2, 47.8% male), caregivers of children with predominant dystonia (50/180, 27.8%) more frequently described movements triggered by negative emotions (p<0.002) and affecting their back, trunk, and whole body (p<0.04). Caregivers of children with predominant spasticity with dystonia (99/180, 55.0%) more frequently described movements affecting a single limb (p<0.04). Caregivers of children without dystonia (31/180, 17.2%) described movements as being slight or small (p<0.008). These differences persisted even for caregivers unaware their child had dystonia (77/149, 51.6%). ConclusionsCaregivers movement descriptions differ between children with different combinations of dystonia and spasticity, which may help inform clinical management and guide communication with families about dystonia.

Repetitive head impacts (RHI) from contact and collision sports have been associated with later-life cognitive and neurobehavioral impairments, as well as neurodegenerative conditions such as chronic traumatic encephalopathy (CTE). RHI-associated clinical sequelae among female former soccer players, specifically, are not well understood. This cross-sectional study aimed to examine the relationship of RHI exposure proxies (e.g., total years of soccer play, highest level of play, and estimated cumulative heading frequency) with clinical measures (e.g., subjective cognitive complaints, objective cognitive performance, behavioral dysregulations, and depressive symptoms) among 3,174 women, aged 40 years or above, enrolled in the Head Impact and Trauma Surveillance Study (HITSS), all of whom played organized soccer. HITSS participants completed an online battery that elicited self-reported cognitive and behavioral complaints and depressive symptoms, and that assessed cognitive performing via computerized tests. Multivariable linear and logistic regression models estimated associations between soccer-related RHI proxies and outcome measures, adjusting for age and education. Among the former soccer players, longer duration of soccer play, higher level of play, and greater estimated cumulative heading frequency were significantly associated with worse self-reported cognitive functioning, greater behavioral dysregulation, and elevated depressive symptom severity (range of significant unstandardized B coefficients: 0.02 to 0.52). Higher estimated cumulative heading exposure was associated with higher odds of clinically meaningful elevations on subjective measures (OR range: 1.05 to 1.13) There were no associations between any of the RHI proxies and performance on the objective computerized cognitive assessments. Among middle-aged women who played organized soccer, cumulative RHI exposure was associated with small but statistically significant effects for measures of subjective cognitive complaints, behavioral functioning, and depressive symptoms. We found no associations for objective outcomes of cognitive function. Continued monitoring of this large cohort of female former soccer players will improve understanding of long-term consequences of soccer play.

BackgroundEvolocumab promotes coronary plaque regression in patients with coronary artery disease, but little is known regarding carotid plaques (CP). This study aimed to evaluate the impact of evolocumab on top of lipid-lowering therapy (ELLT) on carotid morphological stabilization (MS) and plaque regression (PR) compared to lipid-lowering therapy (LLT) alone. MethodsAsymptomatic patients with internal carotid stenosis[&ge;]50% and LDL-C[&ge;]100 mg/dL were randomized to ELLT or LLT and monitored by serial duplex ultrasound. The primary endpoint was a composite of 6-month-MS (i.e., switch from morphologic types I-II to III-IV) and/or 12-month-PR (i.e., reduction of carotid stenosis by at least 5% compared to baseline). The secondary endpoint was LDL-C change at 12 months. Major adverse vascular events (MAVE, i.e., cardiac death, stroke, myocardial infarction, carotid or coronary or peripheral revascularization) were recorded. ResultsA total of 170 patients were randomized. Mean carotid stenosis was 57%. At 6 months, MS occurred in the ELLT group (10.3%) only (p=0.29). At 12 months, PR was numerically more frequent in the ELLT group, without reaching statistical significance (43% versus 35.1%, p=0.42). The primary endpoint was met in 44.3% versus 35.1% (p=0.26). As compared to baseline, 6 and 12-month shifts from low to high-risk types were significantly higher in the LLT group (p=0.03). The 12-month LDL-C percentage reduction was -73.5% with ELLT, and -48.3% with LLT (p=0.0001). At 1 year, MAVE were significantly more frequent with LLT (14.6% versus 2.4%, p=0.005), and the absence of evolocumab was the only predictor (OR 7, p=0.014). ConclusionsIn patients with CP[&ge;]50% and LDL-C[&ge;]100 mg/dL, ELLT compared to LLT was associated with numerically but not statistically higher 6-month MS and/or 12-month PR. In the LLT group, 6- and 12-month changes from low to high-risk types, LDL-C, and MAVE were significantly higher. According to these results, evolocumab should be considered standard treatment for patients with CP[&ge;]50%. The study was registered at www.clinicaltrials.gov (NCT04730973) and Eudract (2020-005663-31). SHORT ABSTRACTPatients with carotid stenosis[&ge;]50% and LDL-C[&ge;]100 mg/dL were randomized to evolocumab on top of optimal lipid-lowering therapy (ELLT) or optimal lipid-lowering therapy (LLT) alone to assess the impact of ELLT on carotid plaque morphological stabilization (MS) and plaque regression (PR). At 6 and 12 months, MS and PR occurred in both groups, but were numerically higher in the ELLT group, without reaching statistical significance. In the LLT group, 6- and 12-month changes from low to high-risk types were significantly higher, and the rate of adverse vascular events was sevenfold higher. Evolocumab might become the standard treatment for patients with carotid artery stenosis [&ge;]50%. CLINICAL PERSPECTIVEO_ST_ABSWhat is new?C_ST_ABSO_LIThe CARUSO is the largest randomized trial evaluating the impact of evolocumab on top of lipid-lowering therapy (ELLT) on carotid morphological stabilization (MS) and plaque regression (PR) monitored by serial duplex ultrasound. C_LIO_LIThe primary endpoint was a composite of 6-month-MS (i.e., switch from morphologic types I-II to III-IV) and/or 12-month-PR (i.e., reduction of carotid stenosis by at least 5% compared to baseline) and was numerically higher in the ELLT group compared to lipid-lowering therapy (LLT) alone, without reaching statistical significance. C_LIO_LIThe 1-year rate of major adverse vascular events (MAVE) was sevenfold higher in the LLT group. C_LI What are the clinical implications?O_LICarotid plaque morphology is a dynamic event, and 6 and 12-month shifts from low to high-risk morphological types were significantly higher in the LLT group, thus suggesting that evolocumab added to LLT may prevent morphological deterioration. C_LIO_LIThe absence of evolocumab was the only independent predictor of MAVE; according to our results, ELLT might become the standard treatment for patients with carotid plaques [&ge;]50% and LDL-C not at target. C_LIO_LIFuture larger studies are warranted to validate our findings, assess long-term adherence to therapy, and identify subgroups with higher probability of achieving MS and PR. C_LI

IntroductionThe ischaemic penumbra is the principal therapeutic target in acute ischaemic stroke (AIS). Although perfusion imaging enables identification of salvageable tissue, its availability is limited and iodinated contrast exposure carries risk. Validated blood-based biomarkers could serve as scalable surrogates for imaging-defined penumbra. ObjectiveWe conducted a systematic review and meta-analysis to assess the association between blood-based biomarkers reported in the literature and the ischaemic penumbra. MethodsWe searched Ovid MEDLINE, Embase (Ovid), PsycINFO (Ovid), and Web of Science until December 3, 2025, for studies involving human subjects with AIS aged over 18 years or animal subjects that reported the presence of infarct and ischaemic penumbra. The primary outcome was the difference in mean biomarker levels in subjects with and without ischaemic penumbrae as defined by the study authors. We used the QUADAS-2 tool to assess risk of bias. We calculated each biomarkers pooled standardized mean difference (SMD) and 95% CI where possible. Protein-protein interaction network (PPI) and pathway analyses were conducted in Cytoscape and the enrichR R package (PROSPERO: CRD42023453175). ResultsWe identified 11 studies (1765 human subjects and 8 nonhuman primates) that assessed 53 candidate blood-based biomarkers. Two studies had a low risk of bias, while nine had a risk of bias. A meta-analysis was conducted for seven biomarkers in humans from four studies. Of these, three biomarkers demonstrated significant association with penumbrae in humans: mid-regional pro-adrenomedullin (MR-proADM; SMD 0.80 [95% CI 0.49 to 1.10]), interleukin-10 (IL-10; SMD 1.94 [0.85 to 3.03]), and neuron-specific enolase (NSE; SMD -0.71 [-1.40 to -0.01]). However, substantial statistical heterogeneity was observed for several pooled biomarkers (I{superscript 2} >90%), limiting confidence in effect size precision. Amongst biomarkers where meta-analysis was not possible, 37 biomarkers showed significant association with presence of a penumbra. Oxygen radical absorbance capacity after perchloric acid treatment (ORACPCA; SMD 0.31 [0.01 to 0.60]) showed significant association with penumbra presence; 34 genes (e.g., STK26 r = 0.58, p = 0.003; MGA r = 0.58, p = 0.004; IL1B r = -0.59, p = 0.003; NUP98 r = -0.71, p < 0.001), circOGDH (r = 0.962, p = 0.002), and NT-proBNP (r = 0.199, p < 0.001) were significantly correlated with penumbra volume. PPI analysis identified IL-1{beta} as the most highly connected node (10 interactions), followed by IL-10 and HDAC1/HCAR2. Cdc42 was reported to be significantly associated with penumbrae in nonhuman primates, but there were insufficient data to calculate SMD. Pathway enrichment revealed positive associations with angiogenesis and IL-12 signalling, and negative associations with leukocyte migration, chemokine signalling, and platelet activation. ConclusionsCurrently reported biomarkers of ischaemic penumbra are not ready for clinical implementation. Although implicated pathways converge on inflammatory regulation, haemostasis, and cerebral perfusion, rigorous prospective validation is required before integration into prehospital or emergency triage workflows.

BackgroundKidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical integration has been limited by reader-dependent workflows and semiautomatic methods susceptible to image quality. PurposeTo evaluate whether fully automated CT-based segmentation of renal cortex, medulla and total parenchymal volume provides reproducible volumetric biomarkers associated with global and split renal function in living kidney donor candidates. Materials and MethodsIn this retrospective single-center study, 461 living kidney donor candidates (2003-2021) underwent contrast-enhanced abdominal CT. A convolutional neural network was trained to automatically segment cortical, medullary, and total parenchymal volumes on arterial-phase images. Segmentation performance was evaluated against manual reference annotations. Volumes were indexed to body surface area. Associations with eGFR, 24-hour creatinine clearance, cystatin C, and tubular clearance were assessed using Spearman correlation coefficient ({rho}), and side-specific volume fractions were compared with scintigraphy -derived split function. ResultsAutomated segmentation achieved excellent agreement with expert reference segmentations (Dice 0.95 for cortex; 0.90 for medulla). eGFR correlated moderately with cortical ({rho} = 0.46) and total parenchymal volume ({rho} = 0.45), and modestly with medullary volume ({rho} = 0.30). Similar associations were observed for other global measures, with the strongest correlation for cortical volume and tubular clearance ({rho} = 0.53). Side-specific volume fractions correlated with scintigraphy-derived split renal function ({rho} = 0.49-0.56; all p < 0.001). ConclusionAutomated CT-based renal subcompartment segmentation provides reproducible volumetric biomarkers within routine donor evaluation. Cortical volume performs comparably to total parenchymal volume and tracks split renal function at the cohort level, suggesting potential utility in donor assessment.

Background and Purpose: Magnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and Methods: First, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. Results: Visible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. Discussion: The extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment.

BackgroundAI-based radiomics has demonstrated promising diagnostic performance for pancreatic cystic neoplasms, yet clinical translation remains limited. Whether this reflects insufficient model performance or structural limitations of the evidence base remains unclear. MethodsWe performed a systematic review and diagnostic test accuracy meta-analysis of AI-based radiomics in pancreatic cyst (2015-2025), addressing two clinically relevant tasks (Q1: cyst type differentiation/Q2: malignancy or high-grade dysplasia prediction). Training and validation datasets were synthesized independently using hierarchical models. Study evaluation extended beyond diagnostic performance to a four-dimensional framework integrating RQS 2.0, METRICS, TRIPOD+AI and PROBAST+AI explicitly contrasting pooled diagnostic performance with reporting quality, methodological rigor, and risk of bias. The review was pre-registered (PROSPERO) and conducted according to PRISMA 2020. ResultsTwenty-nine studies were included (Q1: n = 15; Q2: n = 14), predominantly retrospective and single center. Training-based analyses showed high apparent diagnostic performance for Q1 (pooled sensitivity/specificity: 0.89 [95% CI, 0.85-0.92]/ 0.90 [0.85-0.93]), but there was substantial heterogeneity ({tau}{superscript 2} = 0.56/0.78; {rho} = 0.38). Validation-based performance remained high (0.86 [0.82-0.89]/ 0.88 [0.81-0.93]), while heterogeneity persisted and prediction regions exceeded confidence regions. Training-based analyses demonstrated similarly high apparent performance (0.88 [0.79-0.95]/0.89 [0.81-0.94]) for Q2, with pronounced heterogeneity ({tau}{superscript 2} = 1.98/1.61; {rho} = 0.63). Validation-based performance was slightly lower, yet still clinically comparable (0.82 [0.75-0.89]/0.86 [0.80-0.91]), and heterogeneity persisted ({tau}{superscript 2} = 0.71/0.43; {rho} = 0.15). Across both tasks, high diagnostic accuracy occurred alongside incomplete reporting, limited validation and an elevated risk of bias. ConclusionAI-based radiomics for pancreatic cysts has reached a structural performance plateau. Further improvements in diagnostic accuracy alone are insufficient to achieve clinical translation and must be accompanied by a paradigm shift from performance-driven model development toward decision-anchored study designs, robust validation strategies, transparent reporting standard, and clinically integrated evaluation frameworks. SummaryAlthough pancreatic cystic lesions are increasingly being detected, imaging-based decision-making remains limited, particularly regarding differentiating between cyst types and stratifying malignancy risk. In this PRISMA-compliant and PROSPERO-registered systematic review and meta-analysis of diagnostic tests, we evaluated the use of AI-based radiomics for these two tasks, as well as its contextualized performance. In addition, a four-dimensional framework was employed to conduct the evaluation, incorporating diagnostic accuracy, reporting quality, risk of bias, and radiomics maturity. Across studies published between 2015 and 2025, the pooled diagnostic performance was consistently high, with only modest declines observed from the training to the validation stage. Nevertheless, considerable heterogeneity between studies and limited transportability remained evident. Multidimensional evaluation indicated a systematic dissociation between reported performance and methodological robustness, characterized by incomplete reporting, restricted validation, and an elevated risk of bias. These limitations were consistent across both clinical questions and were not resolved by increasing model complexity. The findings of this meta-analysis suggest that the structural performance of AI-based radiomics for pancreatic cysts has plateaued. To progress towards clinical translation, it is necessary to employ study designs anchored in decision-making processes, robust multi-center validation, and transparent, reproducible evaluation frameworks. This is preferred to further optimization of model architecture alone.

Rationale and ObjectivesContrast-enhanced (CE) MRI provides clear corticomedullary contrast for renal compartment delineation but may be contraindicated or undesirable in routine practice. We aimed to enable automated extraction of renal imaging biomarkers from routine non-contrast-enhanced (NCE) T1-weighted MRI by transferring CE-derived compartment labels. Materials and MethodsThis retrospective single-center study (January 2017 to December 2021) included 200 participants with paired arterial-phase CE and NCE T1-weighted MRI. Cortex, medulla, and sinus were manually segmented on CE MRI and rigidly transferred to NCE MRI to provide voxel-level reference labels. A hierarchical 3D Deep Neural Patchworks model was trained on 100 examinations (90 training/10 validation) and evaluated on an independent test set of 100 examinations using the transferred CE masks on NCE as reference. Performance was assessed using Dice similarity of segmentations and biomarker agreement using volumes and surface areas (Pearson/Spearman, MAE, Lins CCC, and Bland-Altman). ResultsWhole-kidney segmentation Dice was 0.950 (left) and 0.953 (right). Total kidney volume showed high agreement with minimal bias (MAE 8.76 mL, 2.5% of mean; CCC 0.983; bias -1.56 mL; 95% limits of agreement -28.81 to 25.69 mL). Cortex volume was modestly overestimated and medulla volume underestimated, shifting predicted compartment fractions toward cortex (74.7% vs. 72,1% in ground truth; medulla 21.5% vs. 24.3%; sinus 3.8% vs. 3.6%. Sinus volume maintained high concordance despite higher Dice dispersion. Surface area was systematically underestimated with low concordance. ConclusionCE-supervised knowledge transfer enables accurate, well-calibrated kidney volumetry from routine NCE MRI and supports contrast-free renal biomarker extraction. Surface area estimation remains challenging. Take-home MessagesO_LICE-supervised label transfer enables accurate, well-calibrated contrast-free kidney volumetry on routine non-contrast T1-weighted MRI. C_LIO_LICompartment volumetry is feasible but shows systematic cortex overestimation and medulla underestimation; surface area remains non-interchangeable due to boundary uncertainty. C_LI