Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke.

OBJECTIVE: Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort. METHODS: A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics. RESULTS: Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration (p < 0.001). Both ARWMC (p < 0.05) and cortical atrophy (p < 0.001) were independent predictors of clinical outcome at 90 days when controlled for confounders in both regression analyses and led to a minor improvement of prediction accuracy in machine learning models (p < 0.001), with atrophy among the top-5 predictors. CONCLUSION: NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT. CLINICAL RELEVANCE STATEMENT: Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection. KEY POINTS: • Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning-based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.

BACKGROUND AND PURPOSE: This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke. METHODS: A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18-36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90. RESULTS: Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733-0.747) and an accuracy of 0.711 (95% CI, 0.705-0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740-0.755]; accuracy, 0.720 [95% CI, 0.714-0.727]; P=0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850-0.861]; accuracy, 0.804 [95% CI, 0.799-0.810]; P<0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%). CONCLUSIONS: Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial.

Background Relevance of antiangiogenic treatment with bevacizumab in patients with glioblastoma is controversial because progression-free survival benefit did not translate into an overall survival (OS) benefit in randomized phase III trials. Purpose To perform longitudinal characterization of intratumoral angiogenesis and oxygenation by using dynamic susceptibility contrast agent-enhanced (DSC) MRI and evaluate its potential for predicting outcome from administration of bevacizumab. Materials and Methods In this secondary analysis of the prospective randomized phase II/III European Organization for Research and Treatment of Cancer 26101 trial conducted between October 2011 and December 2015 in 596 patients with first recurrence of glioblastoma, the subset of patients with availability of anatomic MRI and DSC MRI at baseline and first follow-up was analyzed. Patients were allocated into those administered bevacizumab (hereafter, the BEV group; either bevacizumab monotherapy or bevacizumab with lomustine) and those not administered bevacizumab (hereafter, the non-BEV group with lomustine monotherapy). Contrast-enhanced tumor volume, noncontrast-enhanced T2 fluid-attenuated inversion recovery (FLAIR) signal abnormality volume, Gaussian-normalized relative cerebral blood volume (nrCBV), Gaussian-normalized relative blood flow (nrCBF), and tumor metabolic rate of oxygen (nTMRO2) was quantified. The predictive ability of these imaging parameters was assessed with multivariable Cox regression and formal interaction testing. Results A total of 254 of 596 patients were evaluated (mean age, 57 years ± 11; 155 men; 161 in the BEV group and 93 in non-BEV group). Progression-free survival was longer in the BEV group (3.7 months; 95% confidence interval [CI]: 3.0, 4.2) compared with the non-BEV group (2.5 months; 95% CI: 1.5, 2.9; P = .01), whereas OS was not different (P = .15). The nrCBV decreased for the BEV group (-16.3%; interquartile range [IQR], -39.5% to 12.0%; P = .01), but not for the non-BEV group (1.2%; IQR, -17.9% to 23.3%; P = .19) between baseline and first follow-up. An identical pattern was observed for both nrCBF and nTMRO2 values. Contrast-enhanced tumor and noncontrast-enhanced T2 FLAIR signal abnormality volumes decreased for the BEV group (-66% [IQR, -83% to -35%] and -33% [IQR, -71% to -5%], respectively; P < .001 for both), whereas they increased for the non-BEV group (30% [IQR, -17% to 98%], P = .001; and 10% [IQR, -13% to 82%], P = .02, respectively) between baseline and first follow-up. None of the assessed MRI parameters were predictive for OS in the BEV group. Conclusion Bevacizumab treatment decreased tumor volumes, angiogenesis, and oxygenation, thereby reflecting its effectiveness for extending progression-free survival; however, these parameters were not predictive of overall survival (OS), which highlighted the challenges of identifying patients that derive an OS benefit from bevacizumab. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Dillon in this issue.

Impact of slice thickness on clinical utility of automated Alberta Stroke Program Early Computed Tomography Scores.

OBJECTIVES: The clinical utility of electronically derived ASPECTS (e-ASPECTS) to quantify signs of acute ischemic infarction could be demonstrated in multiple studies. Here, we aim to clinically validate the impact of CT slice thickness (ST) on the performance of e-ASPECTS software. METHODS: A consecutive series of n = 258 patients (06/2016 and 01/2019) with middle cerebral artery occlusion and subsequent treatment with mechanical thrombectomy was analyzed. The e-ASPECTS score and acute infarct volumes were calculated from baseline non-contrast CT with a software using 1-mm slice thickness (ST) (defined as ground truth) and axial reconstructions with 2-10-mm ST and correlated with baseline stroke severity (NIHSS) as well as clinical outcome (mRS) using logistic regressions. RESULTS: In comparison with the ground truth, significant differences were seen in e-ASPECTS scores with ST > 6 mm (p ≤ 0.031) and infarct volumes with ST > 4 mm (p ≤ 0.001). There was a significant correlation of lower e-ASPECTS and higher acute infarct volumes with increasing baseline NIHSS values for all ST (p ≤ 0.001, respectively), with values derived from 1 mm yielding the highest correlation for both parameters (rho, - 0.38 and 0.31, respectively). Similarly, lower e-ASPECTS and higher acute infarct volumes from all ST were significantly associated with poor outcome after 90 days (p ≤ 0.05, respectively) with values derived from 1-mm ST yielding the highest effects for both parameters (OR, 0.69 [95% CI 0.50-0.88] and 1.27 [95% CI 1.10-1.50], respectively). CONCLUSIONS: The e-ASPECTS software generates robust values for e-ASPECTS and acute infarct volumes when using ST ≤ 4 mm with ST = 1 mm yielding the best performance for predicting baseline stroke severity and clinical outcome after 90 days. KEY POINTS: • Clinical utility of automatically derived ASPECTS from computed tomography scans was shown in patients with acute ischemic stroke and treatment with mechanical thrombectomy. • Thin slices (= 1 mm) had the highest clinical utility in comparison with thicker slices (2-10 mm) by having the strongest correlation with baseline stroke severity and independent effects on clinical outcome after 90 days. • Automatically calculated acute infarct volumes possess clinical utility beyond ASPECTS and should be considered in future studies.

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. METHODS: A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. RESULTS: The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. CONCLUSIONS: Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. KEY POINTS: • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Radiation exposure in endovascular stroke treatment of acute basilar artery occlusions-a matched-pair analysis.

PURPOSE: To determine the radiation exposure in endovascular stroke treatment (EST) of acute basilar artery occlusions (BAO) and compare it with radiation exposure of EST for embolic middle cerebral artery occlusions (MCAO). METHODS: In this retrospective analysis of an institutional review board-approved prospective stroke database of a comprehensive stroke center, we focused on radiation exposure (as per dose area product in Gy × cm2, median (IQR)), procedure time, and fluoroscopy time (in minutes, median [IQR]) in patients receiving EST for BAO. Patients who received EST for BAO were matched case by case with patients who received EST for MCAO according to number of thrombectomy attempts, target vessel reperfusion result, and thrombectomy technique. RESULTS: Overall 180 patients (n = 90 in each group) were included in this analysis. General anesthesia was conducted more often during EST of BAO (BAO: 75 (83.3%); MCAO: 18 (31.1%), p < 0.001). Procedure time (BAO: 31 (20-43); MCAO: 27 (18-38); p value 0.226) and fluoroscopy time (BAO: 29 (20-59); MCAO: 29 (17-49), p value 0.317) were comparable. Radiation exposure was significantly higher in patients receiving EST for BAO (BAO: 123.4 (78.7-204.2); MCAO: 94.3 (65.5-163.7), p value 0.046), which represents an increase by 23.7%. CONCLUSION: Endovascular stroke treatment of basilar artery occlusions is associated with a higher radiation exposure compared with treatment of middle cerebral artery occlusions.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING: Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

Automated brain extraction of multisequence MRI using artificial neural networks.

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

Renal Impairment Is Associated with Intracerebral Hemorrhage after Mechanical Thrombectomy in Vertebrobasilar Stroke.

BACKGROUND AND PURPOSE: Renal dysfunction (RD) is overall associated with unfavorable functional outcome and higher risk of mortality after acute ischemic stroke. Associations between RD and outcome in patients with acute vertebrobasilar stroke treated with thrombectomy have not been evaluated so far. MATERIALS AND METHODS: Consecutive patients with vertebrobasilar stroke treated with mechanical thrombectomy between October 2010 and July 2017 at our center were analyzed. RD was defined as glomerular filtration rate (GFR) < 60 mL/min/1.73 m2 at admission. Endpoints were (I) poor clinical outcome (modified Rankin Scale > 2) at 3 months, (II) 3-month mortality, and (III) intracerebral hemorrhage (ICH) after treatment. RESULTS: Overall, 106 patients were included. Median age was 73.0 years (interquartile range 62.0-80.0), and RD was present in 20.8%. Multivariate analysis revealed that RD was associated with a higher risk for any ICH (OR 3.54; 95% CI 1.09-11.49; p = 0.035). Stroke severity at onset predicted poor clinical outcome (OR 1.08; 95% CI 1.03-1.14; p = 0.003). Neither low GFR nor any ICH, but stroke severity (OR 1.08; 95% CI 1.03-1.14; p = 0.002) and poor recanalization results (OR 11.38; 95% CI 2.01-64.41; p = 0.006) were associated with a higher risk for mortality. CONCLUSIONS: Patients with RD and acute vertebrobasilar stroke should be thoroughly monitored to prevent ICH after thrombectomy. Our results support performing mechanical thrombectomy in acute stroke patients with large vessel occlusions of the posterior circulation, irrespective of their renal function.

Risk factors of intracranial hemorrhage after mechanical thrombectomy of anterior circulation ischemic stroke.

PURPOSE: Intracranial hemorrhage (ICH) is a potentially severe complication after mechanical thrombectomy (MT). Here, we investigated risk factors for the occurrence of any and symptomatic ICH after MT due to large-vessel occlusion of the anterior circulation. METHODS: Consecutive patients with acute ischemic anterior circulation stroke with large-vessel occlusion undergoing MT were analyzed. ICH was categorized according to the Heidelberg Bleeding Classification. Forty-three procedural and clinical parameters were analyzed using univariate tests and multivariate logistic regressions. RESULTS: Of 612 patients, any ICH was detected in 195 (31.9%), while 27 (4.4%) developed a symptomatic ICH. Infarct size > 1/3 of vascular territory in control imaging (OR 2.18, 95% CI 1.45-3.21), higher serum glucose levels (OR 1.23 for change of 15 units mg/dL, 95% CI 1.10-1.39), and higher thrombectomy maneuver count (OR 1.21, 95% CI 1.11-1.32) were significantly associated with a higher risk of developing any ICH compared to no ICH. Wake-up strokes (OR 3.99, 95% CI 1.38-11.60), transfer from an external clinic (OR 3.04, 95% CI 1.24-7.48), and higher serum glucose levels (OR 1.22 for change of 15 units mg/dL, 95% CI 1.05-1.42) were revealed as independent risk factors for development of symptomatic ICH compared to no symptomatic ICH. Patients with no infarct demarcation (OR 0.10, 95% CI 0.01-0.80) and complete recanalization (OR 0.57, 95% CI 0.37-0.86) showed a lower risk of developing any ICH. CONCLUSION: Wake-up strokes and patients who are treated within a drip-and-ship concept are especially vulnerable for symptomatic ICH, while complete recanalization, contrary to subtotal recanalization only, was revealed as a protective factor against ICH.

Classification of Bleeding Events: Comparison of ECASS III (European Cooperative Acute Stroke Study) and the New Heidelberg Bleeding Classification.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) after acute ischemic stroke treatments represents a feared complication with possible prognostic implications. In recent years, ICHs were commonly classified according to the ECASS (European Cooperative Acute Stroke Study). To improve the clinical applicability and relevance, the new Heidelberg Bleeding Classification (HBC) has been proposed in 2015. Here, we compared the ECASS and HBC classification with regard to observed events and prognostic relevance. METHODS: A retrospective analysis of a prospectively compiled database of patients with acute ischemic stroke in the anterior circulation who received mechanical thrombectomy between February 2011 and March 2016 was performed. Presence of ICH after mechanical thrombectomy was evaluated on postinterventional computed tomographic imaging. ICHs were specified according to both ECASS III and HBC classification and analyzed with regard to their symptoms and outcome. RESULTS: ICHs were observed in 156 of 768 patients (20.3%). Using ECASS III classification, 101 ICHs could be unambiguously assigned, of which 28 (27.7%; 3.6% of all treated patients) were symptomatic ICHs. Using HBC, 55 additional ICHs could be categorized. Of these total 156 ICHs, 29 (18.6%; 3.8% of all treated patients) were classified as symptomatic according to HBC. CONCLUSIONS: Classification of ICH by ECASS III and HBC criteria show distinct differences. These differences warrant special attention during interpretation and comparison of scientific publications.

Association of Carotid Artery Disease with Collateralization and Infarct Growth in Patients with Acute Middle Cerebral Artery Occlusion.

BACKGROUND AND PURPOSE: Collaterals are important in large vessel occlusions (LVO), but the role of carotid artery disease (CAD) in this context remains unclear. This study aimed to investigate the impact of CAD on intracranial collateralization and infarct growth after thrombectomy in LVO. MATERIALS AND METHODS: All patients who underwent thrombectomy due to M1 segment occlusion from 01/2015 to 12/2021 were retrospectively included. Internal carotid artery stenosis according to NASCET was assessed on the affected and nonaffected sides. Collaterals were assessed according to the Tan score. Infarct growth was quantified by comparing ASPECTS on follow-up imaging with baseline ASPECTS. RESULTS: In total, 709 patients were included, 118 (16.6%) of whom presented with CAD (defined as severe stenosis ≥70% or occlusion ipsilaterally), with 42 cases (5.9%) being contralateral. Good collateralization (Tan 3) was present in 56.5% of the patients with ipsilateral CAD and 69.1% of the patients with contralateral CAD. The ipsilateral stenosis grade was an independent predictor of good collateral supply (adjusted OR: 1.01; NASCET point, 95% CI: 1.00-1.01; P = .009), whereas the contralateral stenosis grade was not (P = .34). Patients with ipsilateral stenosis of ≥70% showed less infarct growth (median ASPECTS decay: 1; IQR: 0-2) compared with patients with 0%-69% stenosis (median: 2; IQR: 1-3) (P = .005). However, baseline ASPECTS was significantly lower in patients with stenosis of 70%-100% (P < .001). The results of a multivariate analysis revealed that increasing ipsilateral stenosis grade (adjusted OR: 1.0; 95% CI: 0.99-1.00; P = .004) and good collateralization (adjusted OR: 0.5; 95% CI: 0.4-0.62; P < .001) were associated with less infarct growth. CONCLUSIONS: CAD of the ipsilateral ICA is an independent predictor of good collateral supply. Patients with CAD tend to have larger baseline infarct size but less infarct growth.

Sine Spin flat detector CT can improve cerebral soft tissue imaging: a retrospective in vivo study.

BACKGROUND: Flat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging within the angiography suite. Novel technical developments such as the Sine Spin FDCT (S-FDCT) may provide an improved cerebral soft tissue contrast. This study investigates the effect of S-FDCT on the differentiation between gray and white matter compared to conventional FDCT (C-FDCT) and multidetector computed tomography (MDCT). METHODS: A retrospective analysis of a prospectively maintained patient database was performed, including patients who underwent mechanical thrombectomy in our institution and received S-FDCT or C-FDCT as well as MDCT. Differentiation between gray and white matter on the contralateral hemisphere to the ischemic stroke was analyzed quantitatively by contrast-to-noise ratio (CNR) and qualitatively (5-point ordinal scale). RESULTS: In a cohort of 109 patients, MDCT demonstrated the best differentiation between gray and white matter compared to both FDCT techniques (p ≤ 0.001). Comparing both generations of FDCT, S-FDCT provided better visibility of the basal ganglia (p = 0.045) and the supratentorial cortex (p = 0.044) compared to C-FDCT both in quantitative and qualitative analyses. Median CNR were as follows: S-FDCT 2.41 (interquartile range [IQR] 1.66-3.21), C-FDCT 0.96 (0.46-1.70), MDCT 3.43 (2.83-4.17). For basal ganglia, median score and IQR were as follows: S-FDCT 2.00 (2.00-3.00), C-FDCT 1.50 (1.00-2.00), MDCT 5.00 (4.00-5.00). CONCLUSIONS: The novel S-FDCT improves the periinterventional imaging quality of cerebral soft tissue compared to C-FDCT. Thus, it may improve the diagnosis of complications within the angiography suite. MDCT provides the best option for x-ray-based imaging of the brain tissue. RELEVANCE STATEMENT: Flat detector computed tomography is a promising technique for cerebral soft tissue imaging, while the novel Sine Spin flat detector computed tomography technique improves imaging quality compared to conventional flat detector computed tomography and thus may facilitate periinterventional diagnosis of gray and white matter. KEY POINTS: • Flat detector computed tomography (FDCT) is frequently applied for periinterventional brain imaging. • The potential of novel Sine Spin FDCT (S-FDCT) is unknown so far. • S-FDCT improves the visibility of cerebral soft tissue compared to conventional FDCT. • Multidetector computed tomography is superior to both FDCT techniques. • S-FDCT may facilitate the evaluation of brain parenchyma within the angiography suite.

Deep Learning-based Assessment of Internal Carotid Artery Anatomy to Predict Difficult Intracranial Access in Endovascular Recanalization of Acute Ischemic Stroke.

BACKGROUND: Endovascular thrombectomy (EVT) duration is an important predictor for neurological outcome. Recently it was shown that an angle of ≤ 90° of the internal carotid artery (ICA) is predictive for longer EVT duration. As manual angle measurement is not trivial and time-consuming, deep learning (DL) could help identifying difficult EVT cases in advance. METHODS: We included 379 CT angiographies (CTA) of patients who underwent EVT between January 2016 and December 2020. Manual segmentation of 121 CTAs was performed for the aortic arch, common carotid artery (CCA) and ICA. These were used to train a nnUNet. The remaining 258 CTAs were segmented using the trained nnUNet with manual verification afterwards. Angles of left and right ICAs were measured resulting in two classes: acute angle ≤ 90° and > 90°. The segmentations together with angle measurements were used to train a convolutional neural network (CNN) determining the ICA angle. The performance was evaluated using Dice scores. The classification was evaluated using AUC and accuracy. Associations of ICA angle and procedural times was explored using median and Whitney­U test. RESULTS: Median EVT duration for cases with ICA angle > 90° was 48 min and with ≤ 90° was 64 min (p = 0.001). Segmentation evaluation showed Dice scores of 0.94 for the aorta and 0.86 for CCA/ICA, respectively. Evaluation of ICA angle determination resulted in an AUC of 0.92 and accuracy of 0.85. CONCLUSION: The association between ICA angle and EVT duration could be verified and a DL-based method for semi-automatic assessment with the potential for full automation was developed. More anatomical features of interest could be examined in a similar fashion.

Aortic Arch Variations and Supra-aortic Arterial Tortuosity in Stroke Patients Undergoing Thrombectomy : Retrospective Analysis of 1705 Cases.

PURPOSE: Unfavorable vascular anatomy can impede thrombectomy in patients with acute ischemic stroke. The aim of this study was to determine the prevalence of aortic arch types, aortic arch branching patterns and supra-aortic arterial tortuosity in stroke patients with large vessel occlusion. METHODS: Computed tomography (CT) and magnetic resonance (MR) images of all stroke patients in an institutional thrombectomy registry were retrospectively reviewed. Aortic arch types and branching patterns of all patients were determined. In patients with anterior circulation stroke, the prevalence of tortuosity (elongation, kinking or coiling) of the supra-aortic arteries of the affected side was additionally assessed. RESULTS: A total of 1705 aortic arches were evaluated. Frequency of aortic arch types I, II and III were 777 (45.6%), 585 (34.3%) and 340 (19.9%), respectively. In 1232 cases (72.3%), there was a normal branching pattern of the aortic arch. The brachiocephalic trunk and the left common carotid artery had a common origin in 258 cases (15.1%). In 209 cases (12.3%), the left common carotid artery arose from the brachiocephalic trunk. Of 1598 analyzed brachiocephalic trunks and/or common carotid arteries, 844 (52.8%) had no vessel tortuosity, 592 (37.0%) had elongation, 155 (9.7%) had kinking, and 7 (0.4%) had coiling. Of 1311 analyzed internal carotid arteries, 471 (35.9%) had no vessel tortuosity, 589 (44.9%) had elongation, 150 (11.4%) had kinking, and 101 (7.7%) had coiling. CONCLUSION: With 20%, type III aortic arches are found in a relevant proportion of stroke patients eligible for mechanical thrombectomy. Nearly half of the stroke patients present with supra-aortic arterial tortuosity, mostly arterial elongation.

Experimental investigation of transvenous embolization of arteriovenous malformations using different in vivo models.

BACKGROUND: Transvenous embolization (TVE) is an emerging technique for the endovascular treatment of cerebral arteriovenous malformations (AVMs). The aim of this study was to investigate two in vivo AVM models and to assess TVE techniques using these models. METHODS: Blood flow in the porcine rete mirabile (RM) was modified by either creating a carotid-jugular fistula or by placing a balloon guide catheter in the carotid artery. The RM was embolized with precipitating hydrophobic injectable liquid (PHIL) 25% via transarterial embolization (TAE; control group) and compared with TVE applying the transvenous retrograde pressure cooker technique and TVE using a Woven EndoBridge (WEB) device for flow control (n=6, respectively). The embolization extent (penetration of the RM), the number of events of reflux or embolization distal to the RM and the procedure times were assessed. RESULTS: The modified RM could be successfully used for embolization in all cases. There were no significant differences regarding the outcome parameters between the two AVM models (fistula or balloon). TVE using the pressure cooker technique led to a higher extent of embolization (median 98.8% vs 63.5%; p=0.008), a lower number of reflux or distal embolization events (p<0.001) and a shorter procedure time (p<0.001) compared with conventional TAE. TVE using a WEB device for flow control was technically feasible and achieved a moderate extent of embolization (median 83.8%). CONCLUSION: After surgical or endovascular modification, the porcine RM is a feasible in vivo AVM model for the investigation of TVE techniques. TVE using the pressure cooker technique is superior to conventional TAE in this experimental model.

Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT.

BACKGROUND: Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort. METHODS: A total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman's coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong's test. RESULTS: Patients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7-21.7) vs 18.8 mL (IQR 9.4-33.9), p<0.001). AIV was highly correlated with ASPECTS (rho=0.78, p<0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p<0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machine-learning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p>0.05). CONCLUSION: AIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.

Location-specific ASPECTS does not improve Outcome Prediction in Large Vessel Occlusion compared to Cumulative ASPECTS.

PURPOSE: Individual regions of the Alberta Stroke Programme Early CT Score (ASPECTS) may contribute differently to the clinical symptoms in large vessel occlusion (LVO). Here, we investigated whether the predictive performance on clinical outcome can be increased by considering specific ASPECTS subregions. METHODS: A consecutive series of patients with LVO affecting the middle cerebral artery territory and subsequent endovascular treatment (EVT) between January 2015 and July 2020 was analyzed, including affected ASPECTS regions. A multivariate logistic regression was performed to assess the individual impact of ASPECTS regions on good clinical outcome (defined as modified Rankin scale after 90 days of 0-2). Machine-learning-driven logistic regression models were trained (training = 70%, testing = 30%) to predict good clinical outcome using i) cumulative ASPECTS and ii) location-specific ASPECTS, and their performance compared using deLong's test. Furthermore, additional analyses using binarized as well as linear clinical outcomes using regression and machine-learning techniques were applied to thoroughly assess the potential predictive properties of individual ASPECTS regions and their combinations. RESULTS: Of 1109 patients (77.3 years ± 11.6, 43.8% male), 419 achieved a good clinical outcome and a median NIHSS after 24 h of 12 (interquartile range, IQR 4-21). Individual ASPECTS regions showed different impact on good clinical outcome in the multivariate logistic regression, with strongest effects for insula (odds ratio, OR 0.56, 95% confidence interval, CI 0.42-0.75) and M5 (OR 0.53, 95% CI 0.29-0.97) regions. Accuracy (ACC) in predicting good clinical outcome of the test set did not differ between when considering i) cumulative ASPECTS and ii) location-specific ASPECTS (ACC = 0.619, 95% CI 0.58-0.64 vs. ACC = 0.629, 95% CI 0.60-0.65; p = 0.933). CONCLUSION: Cumulative ASPECTS assessment in LVO remains a stable and reliable predictor for clinical outcome and is not inferior to a weighted (location-specific) ASPECTS assessment.

MR microscopy to assess clot composition following mechanical thrombectomy predicts recanalization and clinical outcome.

BACKGROUND: Mechanical thrombectomy (MT) is the standard of care for patients with a stroke and large vessel occlusion. Clot composition is not routinely assessed in clinical practice as no specific diagnostic value is attributed to it, and MT is performed in a standardized 'non-personalized' approach. Whether different clot compositions are associated with intrinsic likelihoods of recanalization success or treatment outcome is unknown. METHODS: We performed a prospective, non-randomized, single-center study and analyzed the clot composition in 60 consecutive patients with ischemic stroke undergoing MT. Clots were assessed by ex vivo multiparametric MRI at 9.4 T (MR microscopy), cone beam CT, and histopathology. Clot imaging was correlated with preinterventional CT and clinical data. RESULTS: MR microscopy showed red blood cell (RBC)-rich (21.7%), platelet-rich (white,38.3%) or mixed clots (40.0%) as distinct morphological entities, and MR microscopy had high accuracy of 95.4% to differentiate clots. Clot composition could be further stratified on preinterventional non-contrast head CT by quantification of the hyperdense artery sign. During MT, white clots required more passes to achieve final recanalization and were not amenable to contact aspiration compared with mixed and RBC-rich clots (maneuvers: 4.7 vs 3.1 and 1.2 passes, P<0.05 and P<0.001, respectively), whereas RBC-rich clots showed higher probability of first pass recanalization (76.9%) compared with white clots (17.4%). White clots were associated with poorer clinical outcome at discharge and 90 days after MT. CONCLUSION: Our study introduces MR microscopy to show that the hyperdense artery sign or MR relaxometry could guide interventional strategy. This could enable a personalized treatment approach to improve outcome of patients undergoing MT.