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Background: Noncontrast computed tomography hypodensities are a validated predictor of hematoma expansion (HE) in intracerebral hemorrhage and a possible alternative to the computed tomography angiography (CTA) spot sign but their added value to available prediction models remains unclear. We investigated whether the inclusion of hypodensities improves prediction of HE and compared their added value over the spot sign. Methods: Retrospective analysis of patients admitted for primary spontaneous intracerebral hemorrhage at the following 8 university hospitals in Boston, US (1994–2015, prospective), Hamilton, Canada (2010–2016, retrospective), Berlin, Germany (2014–2019, retrospective), Chongqing, China (2011–2015, retrospective), Pavia, Italy (2017–2019, prospective), Ferrara, Italy (2010–2019, retrospective), Brescia, Italy (2020–2021, retrospective), and Bologna, Italy (2015–2019, retrospective). Predictors of HE (hematoma growth >6 mL and/or >33% from baseline to follow-up imaging) were explored with logistic regression. We compared the discrimination of a simple prediction model for HE based on 4 predictors (antitplatelet and anticoagulant treatment, baseline intracerebral hemorrhage volume, and onset-to-imaging time) before and after the inclusion of noncontrast computed tomography hypodensities, using receiver operating characteristic curve and De Long test for area under the curve comparison. Results: A total of 2465 subjects were included, of whom 664 (26.9%) had HE and 1085 (44.0%) had hypodensities. Hypodensities were independently associated with HE after adjustment for confounders in logistic regression (odds ratio, 3.11 [95% CI, 2.55–3.80]; P P =0.025). In the subgroup of patients with a CTA available (n=895, 36.3%), the added value of hypodensities remained statistically significant (area under the curve, 0.68 [95% CI, 0.64–0.73] versus 0.74 [95% CI, 0.70–0.78]; P =0.041) whereas the addition of the CTA spot sign did not provide significant discrimination improvement (area under the curve, 0.74 [95% CI, 0.70–0.78]). Conclusions: Noncontrast computed tomography hypodensities provided a significant added value in the prediction of HE and appear a valuable alternative to the CTA spot sign. Our findings might inform future studies and suggest the possibility to stratify the risk of HE with good discrimination without CTA.

Background: Intravenous tPA (tissue-type plasminogen activator) is often administered before endovascular thrombectomy (EVT). Recent studies have questioned whether tPA is necessary given the high rates of arterial recanalization achieved by EVT, but whether tPA impacts venous outflow (VO) is unknown. We investigated whether tPA improves VO profiles on baseline computed tomography (CT) angiography (CTA) images before EVT. Methods: Retrospective multicenter cohort study of patients with acute ischemic stroke due to large vessel occlusion undergoing EVT triage. Included patients underwent CT, CTA, and CT perfusion before EVT. VO profiles were determined by opacification of the vein of Labbé, sphenoparietal sinus, and superficial middle cerebral vein on CTA as 0, not visible; 1, moderate opacification; and 2, full. Pial arterial collaterals were graded on CTA, and tissue-level collaterals were assessed on CT perfusion using the hypoperfusion intensity ratio. Clinical and demographic data were determined from the electronic medical record. Using multivariable regression analysis, we determined the correlation between tPA administration and favorable VO profiles. Results: Seven hundred seventeen patients met inclusion criteria. Three hundred sixty-five patients received tPA (tPA+), while 352 patients were not treated with tPA (tPA−). Fewer tPA+ patients had atrial fibrillation (n=128 [35%] versus n=156 [44%]; P =0.012) and anticoagulants/antiplatelet treatment before acute ischemic stroke due to large vessel occlusion onset (n=130 [36%] versus n=178 [52%]; P P P Conclusions: In patients with acute ischemic stroke due to large vessel occlusion undergoing thrombectomy triage, tPA administration was strongly associated with the presence of favorable VO profiles.

In ischemic posterior circulation stroke, the utilization of standardized image scores is not established in daily clinical practice. We aimed to test a novel imaging score that combines the collateral status with the rating of the posterior circulation Acute Stroke Prognosis Early CT score (pcASPECTS). We hypothesized that this score (pcASCO) predicts functional outcome and malignant cerebellar edema (MCE).Ischemic stroke patients with acute BAO who received multimodal-CT and underwent thrombectomy on admission at two comprehensive stroke centers were analyzed. The posterior circulation collateral score by van der Hoeven et al was added to the pcASPECTS to define pcASCO as a 20-point score. Multivariable logistic regression analyses were performed to predict functional independence at day 90, assessed using modified Rankin Scale scores, and occurrence of MCE in follow-up CT using the established Jauss scale score as endpoints.A total of 118 patients were included, of which 84 (71%) underwent successful thrombectomy. Based on receiver operating characteristic curve analysis, pcASCO ≥ 14 classified functional independence with higher discriminative power (AUC: 0.83, 95%CI: 0.71-0.91) than pcASPECTS (AUC: 0.74). In multivariable logistic regression analysis, pcASCO was significantly and independently associated with functional independence (aOR: 1.91, 95%CI: 1.25-2.92, p = 0.003), and MCE (aOR: 0.71, 95%CI: 0.53-0.95, p = 0.02).The pcASCO could serve as a simple and feasible imaging tool to assess BAO stroke patients on admission and might be tested as a complementary tool to select patients for thrombectomy in uncertain situations, or to predict clinical outcome.• The neurological assessment of basilar artery occlusion stroke patients can be challenging and there are yet no validated imaging scores established in daily clinical practice. • The pcASCO combines the rating of early ischemic changes with the status of the intracranial posterior circulation collaterals. • The pcASCO showed high diagnostic accuracy to predict functional outcome and malignant cerebellar edema and could serve as a simple and feasible imaging tool to support treatment selection in uncertain situations, or to predict clinical outcome.

Purpose Evidence regarding the effect of mechanical thrombectomy (MT) of basilar artery occlusion (BAO) stroke is yet sparse. As successful recanalization has been suggested as major determinant of outcome, the early identification of modifiable factors associated with successful recanalization could be of importance to improve functional outcome. Hyperglycemia has been associated with enhanced thrombin generation and unfavorably altered clot features. Objective We hypothesized that serum baseline glucose is associated with likelihood of vessel recanalization mediated by collateral quality and clot burden in BAO stroke. Methods BAO stroke patients who received multimodal CT on admission were analyzed. The association of vessel recanalization defined using modified Thrombolysis in cerebral infarction scale (mTICI) scores 2b-3, and baseline imaging and clinical parameters were tested in logistic regression analyses. Collateral quality and clot burden were evaluated using the Basilar Artery on CT-Angiography (BATMAN) score. Results Out of 117 BAO patients, 91 patients (78%) underwent MT. In 70 patients (77%), successful recanalization could be achieved (mTICI 2b/3). In multivariable logistic regression analysis, only a higher BGL (aOR 0.97, 95% CI 0.96–0.99, p = 0.03) and higher BATMAN score (aOR 1.77, 95% CI 1.11–2.82, p = 0.02) were independently associated with vessel recanalization. Application of alteplase, or time from symptom onset-imaging revealed no independent association with recanalization status. Conclusion Higher BGL was significantly associated with reduced likelihood for recanalization success besides BATMAN score as a measure of collateral quality and clot burden. BGL could be tested as a modifiable parameter to increase likelihood for recanalization in BAO stroke, aiming to improve functional outcome.

ObjectivesDeepBleed is the first publicly available deep neural network model for the 3D segmentation of acute intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) on non-enhanced CT scans (NECT). The aim of this study was to evaluate the generalizability in an independent heterogenous ICH cohort and to improve the prediction accuracy by retraining the model.MethodsThis retrospective study included patients from three European stroke centers diagnosed with acute spontaneous ICH and IVH on NECT between January 2017 and June 2020. Patients were divided into a training-, validation- and test cohort according to the initial study. Model performance was evaluated using metrics of dice score (DSC), sensitivity, and positive predictive values (PPV) in the original model (OM) and the retrained model (RM) for each ICH location. Students’ t-test was used to compare the DSC between the two models. A multivariate linear regression model was used to identify variables associated with the DSC. Pearson correlation coefficients (r) were calculated to evaluate the volumetric agreement with the manual reference (ground truth: GT). Intraclass correlation coefficients (ICC) were calculated to evaluate segmentation agreement with the GT compared to expert raters.ResultsIn total, 1040 patients were included. Segmentations of the OM had a median DSC, sensitivity, and PPV of 0.84, 0.79, and 0.93, compared to 0.83, 0.80, and 0.91 in the RM, adjusted p-values > 0.05. Performance metrics for infratentorial ICH improved from a median DSC of 0.71 for brainstem and 0.48 for cerebellar ICH in the OM to 0.77 and 0.79 in the RM. ICH volume and location were significantly associated with the DSC, p-values < 0.05. Volumetric measurements showed strong agreement with the GT (r > 0.90), p-value >0.05. Agreement of the automated segmentations with the GT were excellent (ICC ≥ 0.9, p-values ConclusionsDeepBleed demonstrated an overall good generalization in an independent validation cohort and location specific variances improved significantly after model retraining. Segmentations and volume measurements showed a strong agreement with the manual reference; however, the quality of segmentations was lower compared to human expert raters. This is the first publicly available external validation of the open-source DeepBleed network for spontaneous ICH introduced by Sharrock et al.

Background Coronavirus disease 2019 (COVID-19) is an infection which can affect the central nervous system. In this study, we sought to investigate associations between neuroimaging findings with clinical, demographic, blood and cerebrospinal fluid (CSF) parameters, pre-existing conditions and the severity of acute COVID-19. Materials and methods Retrospective multicenter data retrieval from 10 university medical centers in Germany, Switzerland and Austria between February 2020 and September 2021. We included patients with COVID-19, acute neurological symptoms and cranial imaging. We collected demographics, neurological symptoms, COVID-19 severity, results of cranial imaging, blood and CSF parameters during the hospital stay. Results 442 patients could be included. COVID-19 severity was mild in 124 (28.1%) patients (moderate n = 134/30.3%, severe n = 43/9.7%, critical n = 141/31.9%). 220 patients (49.8%) presented with respiratory symptoms, 167 (37.8%) presented with neurological symptoms first. Acute ischemic stroke (AIS) was detected in 70 (15.8%), intracranial hemorrhage (IH) in 48 (10.9%) patients. Typical risk factors were associated with AIS; extracorporeal membrane oxygenation therapy and invasive ventilation with IH. No association was found between the severity of COVID-19 or blood/CSF parameters and the occurrence of AIS or IH. Discussion AIS was the most common finding on cranial imaging. IH was more prevalent than expected but a less common finding than AIS. Patients with IH had a distinct clinical profile compared to patients with AIS. There was no association between AIS or IH and the severity of COVID-19. A considerable proportion of patients presented with neurological symptoms first. Laboratory parameters have limited value as a screening tool.

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.

Background In basilar artery occlusion stroke, the impact of the collateral circulation on infarct progression in the context of endovascular treatment is yet poorly studied. Aim This study investigates the impact of the posterior circulation collateral score (PCCS) on functional outcome according to the extent of early ischemic changes and treatment. We hypothesized that the presence of collaterals, quantified by the PCCS, mediates the effect of endovascular treatment on functional outcome in patients with acute basilar artery occlusion. Methods In this multicenter observational study, patients with basilar artery occlusion and admission computed tomography were analyzed. At baseline, Posterior circulation Acute Stroke Prognosis Early Computed Tomography score (pcASPECTS) was assessed and PCCS was quantified using an established 10-point grading system. Logistic regression analyses were performed to identify factors associated with good functional outcome (modified Rankin Scale scores 0–2 at day 90). Results A total of 151 patients were included, of which 112 patients (74%) underwent endovascular treatment. In patients with a better PCCS (>5), the rate of good outcome was significantly higher (55% vs. 11%; p = 0.001). After adjusting for PCCS, vessel recanalization was significantly associated with improved functional outcome (aOR: 4.53, 95%CI: 1.25–16.4, p = 0.02), while there was no association between recanalization status and outcome in univariable analysis. Patients with low pcASPECTS generally showed very poor outcomes (mean modified Rankin Scale score 5.3, 95%CI: 4.9–5.8). Conclusion PCCS modified the effect of recanalization on functional outcome, particularly in patients with less pronounced ischemic changes in admission computed tomography. These results should be validated to improve patient selection for endovascular treatment in basilar artery occlusion, particularly in uncertain indications, or to triage patients at risk for very poor outcomes.

Objective: To identify facilitators and barriers and derive concrete measures towards better workplace integration of migrants working in the German healthcare sector. Design: Two-centre cross-sectional quantitative online survey of experiences of discrimination among healthcare professionals with a migration history in two large German university hospitals. Participants: 251 participants fully completed the questionnaires. Main outcome measures: Experiences of discrimination and perception of inequality. Results: Fifty-five percent of migrant health workers had had at least some command of German before arriving in Germany. Members of all professional groups surveyed expressed experiences of discrimination related to language, nationality, race/ethnicity, and sex/gender. The proportions of staff with experiences of discrimination by peers differed significantly among occupational roles, with nurses and technologists having the most experiences of discrimination. The perception of inequality was reported more frequently than experiences of discrimination and had a negative impact on workplace satisfaction. Specifically, the compulsion to compete was a frequent feeling stated by participants. Conclusion: The mechanisms of discrimination and structural inequality revealed by our survey could inform specific measures, for example at the management level, to increase workplace satisfaction and attract migrant health workers in the long term.

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value

BackgroundThrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition.MethodsThe analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation.ResultsTest set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity.ConclusionsMachine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies.

Background and purposeThe impact of the cerebral collateral circulation on lesion progression and clinical outcome in ischaemic stroke is well established. Moreover, collateral status modifies the effect of endovascular treatment and was therefore used to select patients for therapy in prior trials. The purpose of this study was to quantify the effect of vessel recanalisation on lesion pathophysiology and clinical outcome in patients with a poor collateral profile.Materials and methods129 patients who had an ischaemic stroke with large vessel occlusion in the anterior circulation and a collateral score (CS) of 0–2 were included. Collateral profile was defined using an established 5-point scoring system in CT angiography. Lesion progression was determined using quantitative lesion water uptake measurements on admission and follow-up CT (FCT), and clinical outcome was assessed using modified Rankin Scale (mRS) scores after 90 days.ResultsOedema formation in FCT was significantly lower in patients with vessel recanalisation compared with patients with persistent vessel occlusion (mean 19.5%, 95% CI: 17% to 22% vs mean 27%, 95% CI: 25% to 29%; pConclusionAlthough poor collaterals are known to be associated with poor outcome, endovascular recanalisation was still associated with significant oedema reduction and comparably better outcome in this patient group. Patients with poor collaterals should not generally be excluded from thrombectomy.

Objectives Triage of patients with basilar artery occlusion for additional imaging diagnostics, therapy planning, and initial outcome prediction requires assessment of early ischemic changes in early hyperacute non-contrast computed tomography (NCCT) scans. However, accuracy of visual evaluation is impaired by inter- and intra-reader variability, artifacts in the posterior fossa and limited sensitivity for subtle density shifts. We propose a machine learning approach for detecting early ischemic changes in pc-ASPECTS regions (Posterior circulation Alberta Stroke Program Early CT Score) based on admission NCCTs. Methods The retrospective study includes 552 pc-ASPECTS regions (144 with infarctions in follow-up NCCTs) extracted from pre-therapeutic early hyperacute scans of 69 patients with basilar artery occlusion that later underwent successful recanalization. We evaluated 1218 quantitative image features utilizing random forest algorithms with fivefold cross-validation for the ability to detect early ischemic changes in hyperacute images that lead to definitive infarctions in follow-up imaging. Classifier performance was compared to conventional readings of two neuroradiologists. Results Receiver operating characteristic area under the curves for detection of early ischemic changes were 0.70 (95% CI [0.64; 0.75]) for cerebellum to 0.82 (95% CI [0.77; 0.86]) for thalamus. Predictive performance of the classifier was significantly higher compared to visual reading for thalamus, midbrain, and pons (P value Conclusions Quantitative features of early hyperacute NCCTs can be used to detect early ischemic changes in pc-ASPECTS regions. The classifier performance was higher or equal to results of human raters. The proposed approach could facilitate reproducible analysis in research and may allow standardized assessments for outcome prediction and therapy planning in clinical routine.

Purpose: This study aimed at retrospectively evaluating full-body computed tomography (CT) examinations for the prevalence of cerebrovascular events in patients with suspected sepsis treated in the intensive care unit (ICU). Methods: All full-body CT examinations, i.e., both cranial CT (cCT) and body CT including chest, abdomen and pelvis, for focus search in septic patients over a 12-months period were identified from three ICUs, using full-text search. From this retrospective cohort, we fully analyzed 278 cCT examinations for the occurrence of acute cerebral findings. All acute cerebrovascular events were independently reviewed by two blinded readers. Clinical and laboratory findings were extracted. The data were statistically analyzed using contingency tests. Results: In our population of patients with suspected sepsis, 10.8% (n = 30/278) were identified to have major cerebral events, including 7.2% (n = 20/278) major cerebrovascular events and 4.3% (n = 12/278) generalized parenchymal damage. 13.4% (n = 22/163) of patients with a severe coma as compared with non-severe coma, 4.4% (n = 3/68), showed a major cerebral event (p = 0.04). Patients referred from the cardiology/nephrology ICU ward showed major cerebral events in 16.3% (n = 22/135), as compared with 4.9% (n = 3/61) in patients from pulmonary ICU and 6.1% (n = 5/82) major cerebral events with surgical referral (p = 0.02). Conclusion: Our study provides further evidence that septic patients may suffer from cerebral events with relevance to their prognosis. Severe coma and the referring ward were associated with acute cerebral conditions. Full-body CT has the advantage of both detecting of septic foci and possibly identifying ischemic or hemorrhagic stroke in this vulnerable patient population.

Background: Hematoma expansion (HE) is an appealing therapeutic target in intracerebral hemorrhage (ICH) and non-contrast computed tomography (NCCT) features are promising predictors of HE. Aims: We investigated whether onset-to-CT time influences the diagnostic performance of NCCT markers for HE. Methods: Retrospective multicentre analysis of patients with primary ICH. The following NCCT markers were analyzed: hypodensities, heterogeneous density, blend sign, and irregular shape. HE was defined as growth ⩾6 mL and/or ⩾33%. We calculated the sensitivity, specificity, positive, and negative predictive values (PPVs and NPVs) of NCCT markers for HE, stratified by onset-to-CT time (6 h). Results: We included 1135 patients (median age 69, 53% males), of whom 307 (27%) experienced HE. Overall hypodensities had the highest sensitivity (0.68) and blend sign the highest specificity (0.87) for HE. Hypodensities were more common and had higher sensitivity (0.80) in patients with imaging within 2 h. The same result was observed for heterogeneous density, whereas irregular shape had a similar prevalence across time strata and higher sensitivity (0.79) beyond 6 h from onset. The frequency of blend sign increased with longer onset-to-CT time, whereas its specificity declined after 6 h from onset. Conclusion: The diagnostic performance of NCCT markers is influenced by imaging time. Hypodensities identified four out of five patients with HE within 2 h from onset, whereas irregular shape performed better in late presenters. Our findings may improve the use of NCCT markers in future studies and trials targeting HE.

BackgroundRecent studies found that favorable venous outflow (VO) profiles are associated with higher reperfusion rates after mechanical thrombectomy (MT) in patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO). Fewer retrieval attempts and first-pass revascularization during MT lead to better functional outcomes.ObjectiveTo examine the hypothesis that favorable VO profiles assessed on baseline CT angiography (CTA) images correlate with successful vessel reperfusion after the first retrieval attempt and fewer retrieval attempts.MethodsA multicenter retrospective cohort study of patients with AIS-LVO treated by MT. Baseline CTA was used to determine the cortical vein opacification score (COVES). Favorable VO was defined as COVES ≥3. Primary outcomes were successful with excellent vessel reperfusion status, defined as Thrombolysis in Cerebral Infarction (TICI) 2b/3 and 2c/3 after first retrieval attempt.Results617 patients were included in this study, of whom 205 (33.2%) had first pass reperfusion. In univariate analysis, ordinal COVES (p=0.011) values were significantly higher in patients with first pass than in those with non-first pass reperfusion, while the number of patients exhibiting favorable pial arterial collaterals using the Maas scale on CTA did not differ (p=0.243). In multivariable logistic regression analysis, higher COVES were independently associated with TICI 2b/3 (OR=1.25, 95% CI 1.1 to 1.42; p=0.001) and TICI 2c/3 (OR=1.2, 95% CI 1.04 to 1.36; p=0.011) reperfusion after one retrieval attempt, controlling for penumbra volume and time from symptom onset to vessel reperfusion.ConclusionsFavorable VO, classified as higher COVES, is independently associated with successful and excellent first pass reperfusion in patients with AIS-LVO treated by endovascular thrombectomy.

Background Ischemic water uptake in acute stroke is a reliable indicator of lesion age. Nevertheless, inter-individually varying edema progression has been observed and elevated water uptake has recently been described as predictor of malignant infarction. Aims We hypothesized that early-elevated lesion water uptake indicates accelerated “tissue clock” desynchronized with “time clock” and therefore predicts poor clinical outcome despite successful recanalization. Methods Acute middle cerebral artery stroke patients with multimodal admission-CT who received successful thrombectomy (TICI 2b/3) were analyzed. Net water uptake (NWU), a quantitative imaging biomarker of ischemic edema, was determined in admission-CT and tested as predictor of clinical outcome using modified Rankin Scale (mRS) after 90 days. A binary outcome was defined for mRS 0–4 and mRS 5–6. Results Seventy-two patients were included. The mean NWU (SD) in patients with mRS 0–4 was lower compared to patients with mRS 5–6 (5.0% vs. 12.1%; p Conclusions Quantitative NWU may serve as an indicator of “tissue clock” and pronounced early brain edema with elevated NWU might suggest a desynchronized “tissue clock” with real “time clock” and therefore predict futile recanalization with poor clinical outcome.

Background and Purpose— The introduction of stent retrievers allows for a complete extraction and histological analysis of human clots. Initial studies describe an impact of preinterventional clot migration on technical and clinical success of thrombectomy and an association with worse clinical outcome. We, therefore, performed a large study of consecutive patients whose clots have been histologically analyzed and tried to determine whether clot composition influences the rates of clot migration and thereby interventional and clinical outcome. Methods— We retrospectively evaluated data of a prospectively collected cohort of 198 consecutive patients with occlusion of the middle cerebral artery. We investigated the relationship between clot histology expressed as percentage of the main components (fibrin, erythrocytes [red blood cell], and white blood cells), the preinterventional computed tomography attenuation of the emboli, embolus length, and clot migration. We defined a binary outcome (clot migration versus no clot migration). The impact of embolus composition and computed tomography features was assessed in univariable and multivariable logistic regression models. Results— One hundred sixty-three patients fulfilled the inclusion criteria, of which 36 (22.1%) showed a clot migration. Patients with proven clot migration had significantly higher levels of red blood cells than patients without clot migration (median 50% versus 26%, P P P P Conclusions— Erythrocyte-rich clots and a shorter embolus length are independent reliable predictors for clot migration. This finding is clinically important as clot migration might have a negative impact on technical and clinical outcomes of patients suffering from emergent vessel occlusions of the middle cerebral artery undergoing endovascular therapy.

The aim of the study is to compare coronal spectrally fat-suppressed 2D turbo spin-echo (TSE) with 2D short-tau inversion-recovery (STIR) sequences for the detection of optic nerve hyperintensities in patients with acute optic nerve neuritis (ON). A retrospective review of patients with suspected unilateral ON and pathological visual evoked potentials, who received coronal TSE and STIR sequences with similar fast and clinically feasible acquisition times in addition to our standard imaging protocol. All images were evaluated and compared concerning the presence of optic nerve lesions, lesion lengths, and signal intensities in different anatomical parts of the optic nerves and CNR measures. A summary confidence score (CS) was calculated based on each reader’s subjective confidence regarding the scoring items. Interobserver agreements regarding the detection of optic nerve lesions were excellent for both sequences (TSE, κ = 0.89 and STIR, κ = 0.80). Greater extensions (17.4 ± 6.3 mm vs. 14.1 ± 5.8 mm), as well as higher numbers of optic nerve lesions in symptomatic nerves, were detected on TSE (49/52) compared with STIR (45/52) sequences (both p

Infarct growth from the early ischemic core to the total infarct lesion volume (LV) is often used as an outcome variable of treatment effects, but can be overestimated due to vasogenic edema. The purpose of this study was (1) to assess two components of early lesion growth by distinguishing between water uptake and true net infarct growth and (2) to investigate potential treatment effects on edema-corrected net lesion growth. Sixty-two M1-MCA-stroke patients with acute multimodal and follow-up CT (FCT) were included. Ischemic lesion growth was calculated by subtracting the initial CTP-derived ischemic core volume from the LV in the FCT. To determine edema-corrected net lesion growth, net water uptake of the ischemic lesion on FCT was quantified and subtracted from the volume of uncorrected lesion growth. The mean lesion growth without edema correction was 20.4 mL (95% CI: 8.2–32.5 mL). The mean net lesion growth after edema correction was 7.3 mL (95% CI: −2.1–16.7 mL; p

Futile recanalization with poor clinical outcome after endovascular treatment of acute ischemic stroke is poorly understood. Recently, vessel recanalization has been associated with reduced ischemic brain edema in patients with good clinical outcome. As edema volume (EV) may be quantified in computed tomography (CT), we hypothesized that higher EV after revascularization predicts futile recanalization with poor outcome.In this observational study, 67 ischemic stroke patients with M1 middle cerebral artery occlusion fulfilled all inclusion criteria and were analyzed. All patients received successful endovascular recanalization (thrombolysis in cerebral infarction scale 2b/3) and subsequent follow-up CT 24 hours later. Edema volume within the infarct lesion was calculated in follow-up CT applying lesion water uptake quantification and was used to predict clinical outcome (Modified Rankin Scale [mRS] after 90 days) compared with infarct volume.The median EV after thrombectomy was 1.6 mL (interquartile range, 0.2-4.2 mL) in patients with mRS 0 to 4 and 8.6 mL (interquartile range, 2.0-49.8 mL) in patients with mRS 5 to 6 (P = 0.0008). In regression analysis, an EV increase of 1 mL was associated with an 8.0% increased likelihood of poor outcome (95% confidence interval, 2.8%-15.4%; P = 0.008). Based on univariate receiver operating characteristic curve analysis, absolute EV over 4.2 mL predicted poor outcome (mRS 5-6) with good discriminative power (area under curve, 0.74; 95% confidence interval, 0.62-0.84; specificity, 77%; sensitivity, 68%). In comparison, the area under curve for infarct volume was 0.68.Elevated EV after endovascular thrombectomy was associated with poor clinical outcome and may indicate futile recanalization.

Background: Early differentiation of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) can be difficult in initial radiological evaluation, especially for extensive ICHs. The aim of this study was to evaluate the potential of a machine learning-based prediction of etiology for acute ICHs based on quantitative radiomic image features extracted from initial non-contrast-enhanced computed tomography (NECT) brain scans.Methods: The analysis included NECT brain scans from 77 patients with acute ICH (n = 50 non-neoplastic, n = 27 neoplastic). Radiomic features including shape, histogram, and texture markers were extracted from non-, wavelet-, and log-sigma-filtered images using regions of interest of ICH and perihematomal edema (PHE). Six thousand and ninety quantitative predictors were evaluated utilizing random forest algorithms with five-fold model-external cross-validation. Model stability was assessed through comparative analysis of 10 randomly drawn cross-validation sets. Classifier performance was compared with predictions of two radiologists employing the Matthews correlation coefficient (MCC).Results: The receiver operating characteristic (ROC) area under the curve (AUC) of the test sets for predicting neoplastic vs. non-neoplastic ICHs was 0.89 [95% CI (0.70; 0.99); P < 0.001], and specificities and sensitivities reached >80%. Compared to the radiologists' predictions, the machine learning algorithm yielded equal or superior results for all evaluated metrics. The MCC of the proposed algorithm at its optimal operating point (0.69) was significantly higher than the MCC of the radiologist readers (0.54); P = 0.01.Conclusion: Evaluating quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting non-neoplastic vs. neoplastic ICHs. Utilized in the clinical routine, the proposed approach could improve patient care at low risk and costs.

Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Malignant cerebellar edema (MCE) is a life-threatening complication of ischemic posterior circulation stroke that requires timely diagnosis and management. Yet, there is no established imaging biomarker that may serve as predictor of MCE. Early edematous water uptake can be determined using quantitative lesion water uptake, but this biomarker has only been applied in anterior circulation strokes.To test the hypothesis that lesion water uptake in early posterior circulation stroke predicts MCE.A total 179 patients with posterior circulation stroke and multimodal admission CT were included. A total of 35 (19.5%) patients developed MCE defined by using an established 10-point scale in follow-up CT, of which ≥4 points are considered malignant. Posterior circulation net water uptake (pcNWU) was quantified in admission CT based on CT densitometry and compared with posterior circulation Acute Stroke Prognosis Early CT Score (pc-ASPECTS) as predictor of MCE using receiver operating curve (ROC) analysis and logistic regression analysis.Acute pcNWU within the early ischemic lesion was 24.6% (±8.4) for malignant and 7.2% (±7.4) for nonmalignant infarctions, respectively (P .0001). Based on ROC analysis, pcNWU above 14.9% identified MCE with high discriminative power (area under the curve: 0.94; 95% CI: 0.89-0.97). Early pcNWU (odds ratio [OR]: 1.28; 95% CI: 1.15-1.42, P .0001) and pc-ASPECTS (OR: 0.71, 95% CI: 0.53-0.95, P = .02) were associated with MCE, adjusted for age and recanalization status.Quantitative pcNWU in early posterior circulation stroke is an important marker for MCE. Besides pc-ASPECTS, lesion water uptake measurements may further support identifying patients at risk for MCE at an early stage indicating stricter monitoring and consideration for further therapeutic measures.

Background: Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes 1 . We hypothesized that machine learning algorithms could automatically analyze non-contrast computed tomography (NECT) of the head and predict clinical outcome of ICH patients 2 . Methods: 300 NECTs with acute spontaneous ICH between 2014-2019 were retrospectively included from the database at a tertiary university hospital. A binary outcome was defined as Modified Ranking Scale (mRS) 0-3 (good outcome) and mRS 4-6 (bad outcome) at discharge. Radiomic features including shape, histogram and texture markers were extracted from non- , wavelet- and log-sigma-filtered images using regions of interest of ICH. The quantitative predictors were evaluated utilizing random forest algorithms with 5-fold model-external cross-validation. Results: The model achieved an area under the ROC curve of 0.81 (95% CI [0.077; 0.86]; P Discussion: In conclusion, quantitative features of acute NECT images in a machine learning algorithm provided high discriminatory power in predicting functional outcome. In clinical routine, this proposed approach could allow early triage of high-risk patients for poor outcome. Indication of source:1 Qureshi, A. I. et al. Intracerebral haemorrhage. Lancet. 2009. 2 Mohammad R. Arbabshirani et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine. 2018.

Introduction In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. Materials and methods Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric. Results The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities. Conclusion Spatial features should be integrated to improve lesion outcome prediction using machine learning models.

Intrauterine growth restriction (IUGR) is a risk factor for neonatal chronic lung disease (CLD) characterized by reduced alveoli and perturbed matrix remodeling. Previously, our group showed an activation of myofibroblasts and matrix remodeling in rat lungs after IUGR. Because growth hormone (GH) and insulin-like growth factor I (IGF-I) regulate development and growth, we queried 1) whether GH/IGF-I signaling is dysregulated in lungs after IUGR and 2) whether GH/IGF-I signaling is linked to neonatal lung myofibroblast function. IUGR was induced in Wistar rats by isocaloric low-protein diet during gestation. Lungs were obtained at embryonic day (E) 21, postnatal day (P) 3, P12, and P23. Murine embryonic fibroblasts (MEF) or primary neonatal myofibroblasts from rat lungs of control (pnFCo) and IUGR (pnFIUGR) were used for cell culture studies. In the intrauterine phase (E21), we found a reduction in GH receptor (GH-R), Stat5 signaling and IGF-I expression in lungs after IUGR. In the postnatal phase (P3–P23), catchup growth after IUGR was linked to increased GH mRNA, GH-R protein, activation of proliferative Stat5/Akt signaling, cyclin D1 and PCNA in rat lungs. On P23, a thickening of the alveolar septae was related to increased vimentin and matrix deposition, indicating fibrosis. In cell culture studies, nutrient deprivation blocked GH-R/IGF-IR signaling and proliferation in MEFs; this was reversed by IGF-I. Proliferation and Stat5 activation were increased in pnFIUGR. IGF-I and GH induced proliferation and migration of pnFCo; only IGF-I had these effects on pnFIUGR. Thus, we show a novel mechanism by which the GH/IGF-I axis in lung myofibroblasts could account for structural lung changes after IUGR.

Background and Purpose— In acute ischemic stroke, mechanical thrombectomy allows flow restoration in the majority of cases. In case of an unsuccessful retrieval, little is known about how many retrieval attempts should be performed before stopping the procedure. This study assessed the recanalization rate and clinical outcome per retrieval maneuver. Methods— In this analysis, 330 patients with acute large vessel occlusion treated exclusively with stentrieval devices were included. Successful recanalization was defined as Thrombolysis in Cerebral Infarction 2b-3, a good clinical outcome was defined as modified Rankin Scale at 90 days of ≤2. Results— The median number of retrieval attempts was 1 (interquartile range, 1–2, maximum 8). Recanalization rates per retrieval attempt were highest for the first retrieval (46.8%) and lowest for the fifth retrieval (22.7%). After 3 retrieval attempts, 67.9% of patients were successfully recanalized. Patients with 1 to 3 retrieval attempts had higher rates of good clinical outcome (28.9% versus 7.4%; P =0.018). The number of passes was an independent negative predictor of good clinical outcome (adjusted odds ratio, 0.65; 95% CI, 0.435–0.970; P =0.035). Conclusions— Two-thirds of occlusions were successfully recanalized with up to 3 retrieval attempts. Further attempts had good recanalization rates, but the rate of favorable clinical outcome did not improve.

Background and Purpose— Poor collateral flow is associated with poor clinical outcome in acute ischemic stroke and may indicate futile recanalization after successful thrombectomy. Pronounced early formation of cerebral ischemic edema may be the link between poor collateral status and declined functional outcome, but this relationship has not been investigated yet. We hypothesized that collateral status is associated with early lesion water uptake as quantitative marker for edema progression. Methods— One hundred seventy-six patients with middle cerebral artery stroke who underwent mechanical thrombectomy were analyzed. Status of cerebral collateral circulation (collaterals status [CS]) was derived using an established 5-point scoring system in admission computed tomography angiography, and good collaterals were defined as CS 3 to 4. Ischemic brain edema dynamics were quantified using early edema progression rate (EPR). EPR was derived from quantitative lesion water uptake in admission computed tomography divided by time from symptom onset to imaging. Good clinical outcome was defined as modified Rankin Scale score 0 to 2 after 90 days. Results— The median EPR was 1.4% per hour (interquartile range, 0.5–3.5%) in patients with good collaterals, which was lower than the median EPR in patients with poor collaterals of 5.8% per hour (interquartile range, 2.1–5.9%; P P =0.002). A higher EPR was associated with reduced likelihood of good clinical outcome: odds ratio 0.87; (95% CI, 0.76–0.99; P =0.03). Conclusions— Patients with poor CS had significantly higher EPR, which was associated with worse clinical outcome. These patients might benefit from adjuvant antiedematous treatment.

Clinical outcome after endovascular thrombectomy in patients with acute ischemic stroke still varies significantly. Higher blood glucose levels (BGL) have been associated with worse clinical outcome, but the pathophysiological causes are not yet understood. We hypothesized that higher levels of BGL are associated with more pronounced ischemic brain edema and worse clinical outcome mediated by cerebral collateral circulation. 178 acute ischemic stroke patients who underwent mechanical thrombectomy were included. Early ischemic brain edema was determined using quantitative lesion water uptake on initial computed tomography (CT) and collateral status was assessed with an established 5-point scoring system in CT-angiography. Good clinical outcome was defined as functional independence (modified Rankin Scale [mRS] score 0–2). Multivariable logistic regression analysis was performed to predict functional independence and linear regression analyses to investigate the impact of BGL and collateral status on water uptake. The mean BGL at admission was significantly lower in patients with good outcome at 90 days (116.5 versus 138.5 mg/dl; p

Asymptomatic intracerebral hemorrhage (aICH) is a common phenomenon in patients with acute ischemic stroke (AIS) treated with endovascular thrombectomy (ET). However, the impact of aICH on the functional outcome remains widely unclear. In this study, we aimed at identifying predictors for aICH and analyzing its impact on functional outcome. Patients with AIS due to large artery occlusion in the anterior circulation treated with successful ET were enrolled in a tertiary stroke center. Patients with aICH or without intracerebral hemorrhage were included according to post-treatment CT performed within 72 h; 100 consecutive patients fulfilled the inclusion criteria and 30% classified with aICH. In logistic regression analysis, lower collateral score (OR 0.24; 95% CI 0.12–0.46, p