Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Probabilistic Presurgical Language Functional MRI Atlas of Brain Tumor Patients.

BACKGROUND AND PURPOSE: Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy-subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and silent word generation language paradigms using a large sample of patients with brain tumors. MATERIALS AND METHODS: The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. 317 patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI dataset of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap map in Montreal Neurological Institute standard space. Wilcoxon signed-rank test was used to determine significant difference in the maximum Dice coefficient for our atlas compared to a meta-analysis-based template with respect to expert-delineated primary language area activations. RESULTS: Probabilities of activating left anterior primary language area and left posterior primary language area in temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in left anterior primary language area (0.351 and 0.326, respectively, P < .05) and left posterior primary language area in temporal lobe (0.274 and 0.244, respectively, P < .005). CONCLUSIONS: Brain tumor patient-and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients than the meta-analysis-based template. ABBREVIATIONS: SENT = sentence completion, LETT = letter fluency, CAT = category fluency, PLA = primary language area, aPLA = anterior PLA, pPLAT = posterior PLA in the temporal lobe, pPLAP = posterior PLA in the parietal lobe, SMA = supplementary motor area, DLPFC = dorsolateral prefrontal cortex, BTLA = basal temporal language area.

Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety.

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

Evidence of varicella zoster virus (VZV) reactivation in children with arterial ischemic stroke: Results of the VIPS II Study.

Background: Varicella zoster virus (VZV) has been associated with focal cerebral arteriopathy (FCA) and arterial ischemic stroke (AIS) in childhood. The Vascular effects of Infection in Pediatric Stroke (VIPS) II study aimed to examine this relationship in the modern era when most children in North America and Australia receive VZV vaccination with live, attenuated virus. Methods: This 22-center prospective cohort study enrolled 205 children (28 days-18 years) with AIS (2017-2022), collected baseline [hyperacute (≤72 hours; n=194) and acute (4-6 days; n=181)] and convalescent (1-6 weeks; n=74) serum samples. Sites enrolled 95 stroke-free controls with single serum samples. A virology research laboratory measured VZV IgM and IgG titers by an in-house enzyme-linked immunosorbent assay (ELISA). Baseline IgG seropositivity indicated prior exposure (vaccination/infection) and elevated IgM titers indicated recent reactivation. Results: Median (IQR) age was 11.6 (5.5-15.6) years for cases and 11.8 (6.8-15.3) years for controls. Baseline serologies indicated prior VZV exposure in 198 cases (97%) and all controls. Parents of cases reported VZV vaccination in 160 (78%) and remote chicken pox in three (1.4%). Twenty cases (9.8%) and three controls (3.1%) had serologic evidence of recent VZV reactivation (p=0.06); all had remote VZV exposure (vaccination in 19 cases and all controls) and all were asymptomatic. Recent VZV reactivation was seen in similar proportions in arteriopathic, cardioembolic, and idiopathic stroke. Of 32 cases of FCA, 4 (12.5%) had recent VZV reactivation, versus no cases of arterial dissection (n=10) or moyamoya (n=16). Conclusions: Serologic evidence of recent VZV reactivation (≈1-6 weeks prior to stroke) was present in one in 10 cases of childhood AIS, including those without arteriopathy. Clinically silent VZV reactivation may be a childhood stroke trigger despite widespread vaccination. These cases could represent waning immunity with reactivation of either vaccine virus or wild-type virus after an unrecognized secondary VZV infection.

Carotid artery calcium score: Definition, classification, application, and limits.

INTRODUCTION: In the current paper, the "carotid artery calcium score" method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery. MODEL AND DEFINITION: The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor ("or voxel size") and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS). RESULTS: In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, p value = .0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, p value = .005), whereas no statistically significant difference was found in the distribution of CACS by gender (p = .148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found. CONCLUSION: In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed.

The Los Angeles motor scale (LAMS) is independently associated with CT perfusion collateral status markers.

BACKGROUND AND AIM: The Los Angeles Motor Scale (LAMS) is an objective tool that has been used to rapidly assess and predict the presence of large vessel occlusion (LVO) in the pre-hospital setting successfully in several studies. However, studies assessing the relationship between LAMS score and CT perfusion collateral status (CS) markers such as cerebral blood volume (CBV) index, and hypoperfusion intensity ratio (HIR) are sparse. Our study therefore aims to assess the association of admission LAMS score with established CTP CS markers CBV Index and HIR in AIS-LVO cases. MATERIALS AND METHODS: In this prospectively collected, retrospectively reviewed analysis, inclusion criteria were as follows: a) CT angiography (CTA) confirmed anterior circulation LVO from 9/1/2017 to 10/01/2023, and b) diagnostic CT perfusion (CTP). Logistic regression analysis was performed to assess the relationship between admission LAMS with CTP CS markers HIR and CBV Index. p ≤ 0.05 was considered significant. RESULTS: In total, 285 consecutive patients (median age = 69 years; 56 % female) met our inclusion criteria. Multivariable logistic regression analysis adjusting for sex, age, ASPECTS, tPA, premorbid mRS, admission NIH stroke scale, prior history of TIA, stroke, atrial fibrillation, diabetes mellitus, hyperlipidemia, coronary artery disease and hypertension, admission LAMS was found to be independently associated with CBV Index (adjusted OR:0.82, p < 0.01), and HIR (adjusted OR:0.59, p < 0.05). CONCLUSION: LAMS is independently associated with CTP CS markers, CBV index and HIR. This finding suggests that LAMS may also provide an indirect estimate of CS.

CT Perfusion Derived rCBV < 42% Lesion Volume Is Independently Associated with Followup FLAIR Infarct Volume in Anterior Circulation Large Vessel Occlusion.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman's rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Imaging Features of Primary Intracranial Sarcoma with DICER1 Mutation: A Multicenter Case Series.

Primary intracranial sarcoma, DICER1-mutant, is a rare, recently described entity in the fifth edition of the WHO Classification of CNS Tumors. Given the entity's rarity and recent description, imaging data on primary intracranial sarcoma, DICER1-mutant, remains scarce. In this multicenter case series, we present detailed multimodality imaging features of primary intracranial sarcoma, DICER1-mutant, with emphasis on the appearance of the entity on MR imaging. In total, 8 patients were included. In all 8 patients, the lesion demonstrated blood products on T1WI. In 7 patients, susceptibility-weighted imaging was obtained and demonstrated blood products. Primary intracranial sarcoma, DICER1-mutant, is a CNS neoplasm that primarily affects pediatric and young adult patients. In the present case series, we explore potential imaging findings that are helpful in suggesting this diagnosis. In younger patients, the presence of a cortical lesion with intralesional blood products on SWI and T1-weighted MR imaging, with or without extra-axial blood products, should prompt the inclusion of this entity in the differential diagnosis.

Academic Neuroradiology: 2023 Update on Turn-Around Time, Financial Recruitment and Retention Strategies.

The ASNR Neuroradiology Division Chief Working Group's 2023 survey, with responses from 62 division chiefs, provides insights into turn-around times, faculty recruitment, moonlighting opportunities, and academic funds.In emergency cases, 61% aim for a turn-around time of less than 45-60 minutes, with two-thirds meeting this expectation more than 75% of the time. For inpatient CT and MRI scans, 54% achieve a turn-around time of 4-8 hours, with three quarters meeting this expectation at least 50% of the time. Outpatient scans have an expected turn-around time of 24-48 hours, which is met in 50% of cases.Faculty recruitment strategies included 35% offering sign-on bonuses, with a median of $30,000. Additionally, 23% provided bonuses to fellows during fellowship to retain them in the practice upon completion of their fellowship. Internal moonlighting opportunities for faculty were offered by 70% of divisions, with a median pay of $250 per hour.The median annual academic fund for a full-time neuroradiology faculty member was $6,000, typically excluding license fees but including ACR and ABR membership, leaving $4,000 for professional expenses.This survey calls for further dialogue on adapting and innovating academic institutions to meet evolving needs in neuroradiology.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). RESULTS: 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. CONCLUSIONS: To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Association between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study.

Background: The cumulative effect of body mass index (BMI) on brain health remains ill-defined. The effects of overweight on brain health across different age groups need clarification. We analyzed the effect of cumulative BMI on neuroimaging features of brain health in adults of different ages. Methods: This study was based on a multicenter, community-based cohort study. We modeled the trajectories of BMI over 16 years to evaluate cumulative exposure. Multimodality neuroimaging data were collected once for volumetric measurements of the brain macrostructure, white matter hyperintensity (WMH), and brain microstructure. We used a generalized linear model to evaluate the association between cumulative BMI and neuroimaging features. Two-sample Mendelian randomization analysis was performed using summary level of BMI genetic data from 681,275 individuals and neuroimaging genetic data from 33,224 individuals to analyze the causal relationships. Results: Clinical and neuroimaging data were obtained from 1,074 adults (25 to 83 years). For adults aged under 45 years, brain volume differences in participants with a cumulative BMI of >26.2 kg/m2 corresponded to 12.0 years [95% confidence interval (CI), 3.0 to 20.0] of brain aging. Differences in WMH were statistically substantial for participants aged over 60 years, with a 6.0-ml (95% CI, 1.5 to 10.5) larger volume. Genetic analysis indicated causal relationships between high BMI and smaller gray matter and higher fractional anisotropy in projection fibers. Conclusion: High cumulative BMI is associated with smaller brain volume, larger volume of white matter lesions, and abnormal microstructural integrity. Adults younger than 45 years are suggested to maintain their BMI below 26.2 kg/m2 for better brain health. Trial Registration: This study was registered on clinicaltrials.gov (Clinical Indicators and Brain Image Data: A Cohort Study Based on Kailuan Cohort; No. NCT05453877; https://clinicaltrials.gov/ct2/show/NCT05453877).

Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study.

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models. METHODS: Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented. RESULTS: A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome. CONCLUSIONS: Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.

Accuracy of Financial Disclosures in Radiology Journals.

PURPOSE: The accuracy and completeness of self-disclosures of the value of industry payments by authors publishing in radiology journals are not well known. The aim of this study was to assess the accuracy of financial disclosures by US authors in five prominent radiology journals. METHODS: Financial disclosures provided by US-based authors in five prominent radiology journals from original research and review articles published in 2021 were reviewed. For each author, payment reports were extracted from the Open Payments Database (OPD) in the previous 36 months related to general, research, and ownership payments. Each author was analyzed individually to determine if the reported disclosures matched results from the OPD. RESULTS: A total of 4,076 authorships, including 3,406 unique authors, were selected from 643 articles across the five journals; 1,388 (1,032 unique authors) received industry payments within the previous 36 months, with a median total amount received per authorship of $6,650 (interquartile range, $355-$87,725). Sixty-one authors (4.4%) disclosed all industry relationships, 205 (14.8%) disclosed some of the OPD-reported relationships, and 1,122 (80.8%) failed to disclose any relationships. Undisclosed payments totaled $186,578,350, representing 67.2% of all payments. Radiology had the highest proportion of authorships disclosing some or all OPD-reported relationships (32.3%), compared with the Journal of Vascular and Interventional Radiology (18.2%), the American Journal of Neuroradiology (17.3%), JACR (13.1%), and the American Journal of Roentgenology (10.3%). CONCLUSIONS: Financial relationships with industry are common among US physician authors in prominent radiology journals, and nondisclosure rates are high.

The Relative Cerebral Blood Volume (rCBV) < 42% Is Independently Associated with Collateral Status in Anterior Circulation Large Vessel Occlusion.

Background: The pretreatment CT perfusion (CTP) marker the relative cerebral blood volume (rCBV) < 42% lesion volume has recently been shown to predict 90-day functional outcomes; however, studies assessing correlations of the rCBV < 42% lesion volume with other outcomes remain sparse. Here, we aim to assess the relationship between the rCBV < 42% lesion volume and the reference standard digital subtraction angiography (DSA)-derived American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) collateral score, hereby referred as the DSA CS. Methods: In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 1 September 2017 and 1 October 2023. Group differences were assessed using the Student's t test, Mann-Whitney U test and Chi-Square test. Spearman's rank correlation and logistic regression analyses were used to assess associations between rCBV < 42% and DSA CS. Results: In total, 222 patients (median age: 69 years, 56.3% female) met our inclusion criteria. In the multivariable logistic regression analysis, taking into account age, sex, race, hypertension, hyperlipidemia, diabetes, atrial fibrillation, prior stroke or transient ischemic attack, the admission National Institute of Health stroke scale, the premorbid modified Rankin score, the Alberta stroke program early CT score (ASPECTS), and segment occlusion, the rCBV < 42% lesion volume (adjusted OR: 0.98, p < 0.05) was independently associated with the DSA CS. Conclusion: The rCBV < 42% lesion volume is independently associated with the DSA CS.

Prolonged venous transit on perfusion imaging is associated with higher odds of mortality in successfully reperfused patients with large vessel occlusion stroke.

BACKGROUND: Poor venous outflow (VO) profiles are associated with unfavorable outcomes in patients with acute ischemic stroke caused by large vessel occlusion (AIS-LVO), despite achieving successful reperfusion. The objective of this study is to assess the association between mortality and prolonged venous transit (PVT), a novel visual qualitative VO marker on CT perfusion (CTP) time to maximum (Tmax) maps. METHODS: We performed a retrospective analysis of prospectively collected data from consecutive adult patients with AIS-LVO with successful reperfusion (modified Thrombolysis in Cerebral Infarction 2b/2c/3). PVT+ was defined as Tmax ≥10 s timing on CTP Tmax maps in at least one of the following: superior sagittal sinus (proximal venous drainage) and/or torcula (deep venous drainage). PVT- was defined as lacking this in both regions. The primary outcome was mortality at 90 days. In a 1:1 propensity score-matched cohort, regressions were performed to determine the effect of PVT on 90-day mortality. RESULTS: In 127 patients of median (IQR) age 71 (64-81) years, mortality occurred in a significantly greater proportion of PVT+ patients than PVT- patients (32.5% vs 12.6%, P=0.01). This significant difference persisted after matching (P=0.03). PVT+ was associated with a significantly increased likelihood of 90-day mortality (OR 1.22 (95% CI 1.02 to 1.46), P=0.03) in the matched cohort. CONCLUSIONS: PVT+ was significantly associated with 90-day mortality despite successful reperfusion therapy in patients with AIS-LVO. PVT is a simple VO profile marker with potential as an adjunctive metric during acute evaluation of AIS-LVO patients. Future studies will expand our understanding of using PVT in the evaluation of patients with AIS-LVO.

A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes.

BACKGROUND AND PURPOSE: Predicting long-term clinical outcome in acute ischemic stroke is beneficial for prognosis, clinical trial design, resource management, and patient expectations. This study used a deep learning-based predictive model (DLPD) to predict 90-day mRS outcomes and compared its predictions with those made by physicians. MATERIALS AND METHODS: A previously developed DLPD that incorporated DWI and clinical data from the acute period was used to predict 90-day mRS outcomes in 80 consecutive patients with acute ischemic stroke from a single-center registry. We assessed the predictions of the model alongside those of 5 physicians (2 stroke neurologists and 3 neuroradiologists provided with the same imaging and clinical information). The primary analysis was the agreement between the ordinal mRS predictions of the model or physician and the ground truth using the Gwet Agreement Coefficient. We also evaluated the ability to identify unfavorable outcomes (mRS >2) using the area under the curve, sensitivity, and specificity. Noninferiority analyses were undertaken using limits of 0.1 for the Gwet Agreement Coefficient and 0.05 for the area under the curve analysis. The accuracy of prediction was also assessed using the mean absolute error for prediction, percentage of predictions ±1 categories away from the ground truth (±1 accuracy [ACC]), and percentage of exact predictions (ACC). RESULTS: To predict the specific mRS score, the DLPD yielded a Gwet Agreement Coefficient score of 0.79 (95% CI, 0.71-0.86), surpassing the physicians' score of 0.76 (95% CI, 0.67-0.84), and was noninferior to the readers (P < .001). For identifying unfavorable outcome, the model achieved an area under the curve of 0.81 (95% CI, 0.72-0.89), again noninferior to the readers' area under the curve of 0.79 (95% CI, 0.69-0.87) (P < .005). The mean absolute error, ±1ACC, and ACC were 0.89, 81%, and 36% for the DLPD. CONCLUSIONS: A deep learning method using acute clinical and imaging data for long-term functional outcome prediction in patients with acute ischemic stroke, the DLPD, was noninferior to that of clinical readers.