Follow-up Non-contrast CT Research Articles

A Deep Learning-Based Framework for Predicting Intracerebral Hemorrhage Hematoma Expansion Using Head Non-contrast CT Scan

Rationale and ObjectivesHematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical factor affecting patient outcomes, yet effective clinical tools for predicting HE are currently lacking. We aim to develop a fully automated framework based on deep learning for predicting HE using only clinical non-contrast CT (NCCT) scans. Materials and MethodsA large retrospective dataset (n=2,484) was collected from 84 centers, while a prospective dataset (n=500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 hours and 48 hours from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n=1,876) and a validation set (n=608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged. ResultsOur two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively. ConclusionOur framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.

Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.

Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT). Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm. A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case. To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.

Predictors of severe intracerebral hemorrhage expansion.

Severe hematoma expansion (sHE) has the strongest impact on intracerebral hemorrhage (ICH) outcome. We investigated the predictors of sHE. Retrospective analysis of ICH patients admitted at nine sites in Italy, Germany, China, and Canada. The following imaging features were analyzed: non-contrast CT (NCCT) hypodensities, heterogeneous density, blend sign, irregular shape, and CT angiography (CTA) spot sign. The outcome of interest was sHE, defined as volume increase >66% and/or >12.5 from baseline to follow-up NCCT. Predictors of sHE were explored with logistic regression. A total of 1472 patients were included (median age 73, 56.6% males) of whom 223 (15.2%) had sHE. Age (odds ratio (OR) per year, 95% confidence interval (CI), 1.02 (1.01-1.04)), Anticoagulant treatment (OR 3.00, 95% CI 2.09-4.31), Glasgow Coma Scale (OR 0.93, 95% CI 0.89-0.98), time from onset/last known well to imaging, (OR per h 0.96, 95% CI 0.93-0.99), and baseline ICH volume, (OR per mL 1.02, 95% CI 1.02-1.03) were independently associated with sHE. Ultra-early hematoma growth (baseline volume/baseline imaging time) was also a predictor of sHE (OR per mL/h 1.01, 95% CI 1.00-1.02). All NCCT and CTA imaging markers were also predictors of sHE. Amongst imaging features NCCT hypodensities had the highest sensitivity (0.79) whereas the CTA spot sign had the highest positive predictive value (0.51). sHE is common in the natural history of ICH and can be predicted with few clinical and imaging variables. These findings might inform clinical practice and future trials targeting active bleeding in ICH.

Role of intravenous alteplase on late lesion growth and clinical outcome after stroke treatment

Several acute ischemic stroke mechanisms that cause lesion growth continue after treatment which is detrimental to long-term clinical outcome. The potential role of intravenous alteplase treatment (IVT), a standard in stroke care, in cessing the physiological processes causing post-treatment lesion development is understudied. We analyzed patients from the MR CLEAN-NO IV trial with good quality 24-hour and 1-week follow-up Non-Contrast CT scans. We delineated hypo- and hyper-dense regions on the scans as lesion. We performed univariable logistic and linear regression to estimate the influence of IVT on the presence (growth > 0 ml) and extent of late lesion growth. The association between late lesion growth and mRS was assessed using ordinal logistic regression. Interaction analysis was performed to evaluate the influence of IVT on this association. Of the 63/116 were randomized to included patients, IVT. Median growth was 8.4(−0.88–26) ml. IVT was not significantly associated with the presence (OR: 1.24 (0.57–2.74, p = 0.59) or extent (β = 5.1(−8.8–19), p = 0.47) of growth. Late lesion growth was associated with worse clinical outcome (aOR: 0.85(0.76–0.95), p < 0.01; per 10 ml). IVT did not influence this association (p = 0.18). We did not find evidence that IVT influences late lesion growth or the relationship between growth and worse clinical outcome. Therapies to reduce lesion development are necessary.

Case of the Month from the Peter MacCallum Cancer Centre, Melbourne, Australia: combination of stereotactic ablative body radiotherapy and radical nephrectomy for synchronous bilateral primary renal cell carcinoma.

Case of the Month from the Peter MacCallum Cancer Centre, Melbourne, Australia: combination of stereotactic ablative body radiotherapy and radical nephrectomy for synchronous bilateral primary renal cell carcinoma.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks

ObjectivesThis study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks. MethodsA total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes. ResultsThe ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001). ConclusionsThe dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

Predicting the tissue outcome of acute ischemic stroke from acute 4D computed tomography perfusion imaging using temporal features and deep learning.

Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV; n = 43) or intra-arterial mechanical thrombectomy (IA; n = 102) with or without combined IV. Using the perfusion parameter maps as input, the proposed DCN (mean Dice: 0.287) outperformed the RDF (0.262) and simple Tmax-thresholding (0.249). The performance of the proposed DCN was approximately equal using features optimized from the deconvolved residual curves (0.286) compared to perfusion parameter maps (0.287), while using features optimized from the source concentration-time curves (0.296) provided the best tissue outcome predictions.

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

ObjectivesTo compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps.MethodsPatients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods—a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability.ResultsA total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification.ConclusionsMultivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.Key Points• Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions.• Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis.• A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.

Predictors of Early Neurological Improvement in Patients with Anterior Large Vessel Occlusion and Successful Reperfusion Following Endovascular Thrombectomy—Does CT Perfusion Imaging Matter?

PurposeWe aimed to investigate treatment effect of endovascular thrombectomy (EVT) on the change of National Institutes of Health Stroke Scale (NIHSS) scores in acute ischemic stroke (AIS) patients with anterior large vessel occlusion (LVO). Predictors of early neurological improvement (ENI) were assessed in those with successful reperfusion.MethodsData on stroke patients from January 2018 to December 2020 were retrospectively analyzed. Anterior LVO was defined as occlusion of internal carotid artery and/or M1/M2 branch of middle cerebral artery. A reduction of at least 8 NIHSS points at 24 h after EVT or NIHSS score ≤ 1 at discharge was defined as ENI. In patients with successful reperfusion (TICI score of 2b/3) and available CT perfusion (CTP) imaging, 20 variables were tested in a smoothed ridge regression for their association with ENI.ResultsOne hundred seventy two out of 211 patients had successful perfusion with 54 patients achieving ENI. Impact of successful EVT on reducing NIHSS score grew continuously on a daily basis up to the date of discharge. 105 out of 172 patients were included in final regression model. Short time from onset to admission and from groin-puncture to reperfusion, young age, low prestroke disability, high baseline CTP ASPECTS and high follow-up non-contrast CT (NCCT) ASPECTS were significantly associated with ENI. Neither baseline NCCT ASPECTS nor the volume of penumbra or ischemic core measured on CTP were associated with ENI.ConclusionCTP ASPECTS might better predict ENI than non-contrast CT at baseline in patients with successful reperfusion following EVT.

Evolution of Hypodensity on Non-Contrast CT in Correlation with Collaterals in Anterior Circulation Stroke with Successful Endovascular Reperfusion.

Introduction: The aim of the study was to assess the impact of collaterals on the evolution of hypodensity on non-contrast CT (NCCT) in anterior circulation stroke with reperfusion by mechanical thrombectomy (MT). Methods: We retrospectively included stroke patients with middle cerebral artery occlusion who were reperfused by MT in early and late time window. Artificial intelligence (AI)-based software was used to calculate of hypodensity volumes at baseline NCCT (V1) and at follow-up NCCT 24 h after MT (V2), along with the difference between the two volumes (V2-V1) and the follow-up (V2)/baseline (V1) volume ratio (V2/V1). The same software was used to classify collateral status by using a 4-point scale where the score of zero indicated no collaterals and the score of three represented contrast filling of all collaterals. The volumetric values were correlated with the collateral scores. Results: Collateral scores had significant negative correlation with V1 (p = 0.035), V2, V2− V1 and V2/V1 (p < 0.001). In cases with collateral score = 3, V2 was significantly smaller or absent compared to V1; in those with collateral score 2, V2 was slightly larger than V1, and in those with scores 1 and 0 V2 was significantly larger than V1. These relationships were observed in both early and late time windows. Conclusions: The collateral status determined the evolution of the baseline hypodensity on NCCT in patients with anterior circulation stroke who had MT reperfusion. Damage can be stable or reversible in patients with good collaterals while in those with poor collaterals tissues that initially appear normal will frequently appear as necrotic after 24 h. With good collaterals, it is stable or can be reversible while with poor collaterals, normal looking tissue frequently appears as necrotic in follow-up exam. Hence, acute hypodensity represents different states of the ischemic brain parenchyma.

ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps

Background: Acute ischemic stroke is one of the leading death causes. Delineating stoke infarct core in medical images plays a critical role in optimal stroke treatment selection. However, accurate estimation of infarct core still remains challenging because of 1) the large shape and location variation of infarct cores; 2) the complex relationships between perfusion parameters and final tissue outcome. Methods: We develop an encoder-decoder based semantic model, i.e., Ischemic Stroke Prediction Network (ISP-Net), to predict infarct core after thrombolysis treatment on CT perfusion (CTP) maps. Features of native CTP, CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume), MTT (Mean Transit Time), Tmax are generated and fused with five-path convolutions for comprehensive analysis. A multi-scale atrous convolution (MSAC) block is firstly put forward as the enriched high-level feature extractor in ISP-Net to improve prediction accuracy. A retrospective dataset which is collected from multiple stroke centers is used to evaluate the performance of ISP-Net. The gold standard infarct cores are delineated on the follow-up scans, i.e., non-contrast CT (NCCT) or MRI diffusion-weighted image (DWI). Results: In clinical dataset cross-validation, we achieve mean Dice Similarity Coefficient (DSC) of 0.801, precision of 81.3%, sensitivity of 79.5%, specificity of 99.5%, Area Under Curve (AUC) of 0.721. Our approach yields better outcomes than several advanced deep learning methods, i.e., Deeplab V3, U-Net++, CE-Net, X-Net and Non-local U-Net, demonstrating the promising performance in infarct core prediction. No significant difference of the prediction error is shown for the patients with follow-up NCCT and follow-up DWI (P >0.05). Conclusion: This study provides an approach for fast and accurate stroke infarct core estimation. We anticipate the prediction results of ISP-Net could offer assistance to the physicians in the thrombolysis or thrombectomy therapy selection.

Difference of Mean Hounsfield Units (dHU) between Follow-Up and Initial Noncontrast CT Scan Predicts 90-Day Poor Outcome in Spontaneous Supratentorial Acute Intracerebral Hemorrhage with Deep Convolutional Neural Networks

Difference of Mean Hounsfield Units (dHU) between Follow-Up and Initial Noncontrast CT Scan Predicts 90-Day Poor Outcome in Spontaneous Supratentorial Acute Intracerebral Hemorrhage with Deep Convolutional Neural Networks

Variability assessment of manual segmentations of ischemic lesion volume on 24-h non-contrast CT.

Infarct lesion volume (ILV) may serve as an imaging biomarker for clinical outcomes in the early post-treatment stage in patients with acute ischemic stroke. The aim of this study was to evaluate the inter- and intra-rater reliability of manual segmentation of ILV on follow-up non-contrast CT (NCCT) scans. Fifty patients from the Prove-IT study were randomly selected for this analysis. Three raters manually segmented ILV on 24-h NCCT scans, slice by slice, three times. The reference standard for ILV was generated by the Simultaneous Truth And Performance Level estimation (STAPLE) algorithm. Intra- and inter-rater reliability was evaluated, using metrics of intraclass correlation coefficient (ICC) regarding lesion volume and the Dice similarity coefficient (DSC). Median age of the 50 subjects included was 74.5years (interquartile range [IQR] 67-80), 54% were women, median baseline National Institutes of Health Stroke Scale was 18 (IQR 11-22), median baseline ASPECTS was 9 (IQR 6-10). The mean reference standard ILV was 92.5ml (standard deviation (SD) ± 100.9ml). The manually segmented ILV ranged from 88.2 ± 91.5 to 135.5 ± 119.9ml (means referring to the variation between readers, SD within readers). Inter-rater ICC was 0.83 (95%CI: 0.76-0.88); intra-rater ICC ranged from 0.85 (95%CI: 0.72-0.92) to 0.95 (95%CI: 0.91-0.97). The mean DSC among the three readers ranged from 65.5 ± 22.9 to 76.4 ± 17.1% and the mean overall DSC was 72.8 ± 23.0%. Manual ILV measurements on follow-up CT scans are reliable to measure the radiological outcome despite some variability.

Predicting Imaging Outcomes in Acute Stroke Therapy—Comparison of Magnetic Resonance Imaging and Computed Tomography

Abstract Background Imaging of acute stroke patients in emergency settings is critical for treatment decisions. Most commonly, CT with CTA is used worldwide for acute stroke. However, MRI may be advantageous in certain settings. With advancements in endovascular clot retrieval techniques, there is a need to identify and use the best possible imaging for the diagnosis and outcome prediction of hyperacute stroke. Methods This mixed retrospective and prospective observational study was conducted over 2 years in patients who underwent reperfusion therapies. Patients were included in this study if they had a baseline as well as follow-up noncontrast CT and diffusion-weighted imaging (DWI) MRI. We compared them for estimating final infarct size and outcomes after reperfusion therapy. Results A total of 86 patients were included in the study. Baseline DWI found new infarcts in 33 patients compared to baseline CT. Sensitivity and specificity of CT and DWI in predicting the final infarct size was 75.3% and 76.9% and 97.2% and 92.3%, respectively. A positive correlation of 51.2% and 84.4% was noted between b-CT Alberta stroke programme early CT score (ASPECTS) and b-DWI with 72 hours DWI ASPECTS, respectively (p &lt; 0.001). The positive predictive value of CT was 94.8% and DWI was 98.6%. None of the patients had reversible hyperintensities in the follow-up DWI. Conclusion MRI is more sensitive and specific than noncontrast CT in predicting final infarct volume. It predicts final outcomes better and could be an alternative if available in acute stroke settings.

The Role of Edema in Subacute Lesion Progression After Treatment of Acute Ischemic Stroke.

Background: Ischemic lesions commonly continue to progress even days after treatment, and this lesion growth is associated with unfavorable functional outcome in acute ischemic stroke patients. The aim of this study is to elucidate the role of edema in subacute lesion progression and its influence on unfavorable functional outcome by quantifying net water uptake.Methods: We included all 187 patients from the MR CLEAN trial who had high quality follow-up non-contrast CT at 24 h and 1 week. Using a CT densitometry-based method to calculate the net water uptake, we differentiated total ischemic lesion volume (TILV) into edema volume (EV) and edema-corrected infarct volume (ecIV). We calculated these volumes at 24 h and 1 week after stroke and determined their progression in the subacute period. We assessed the effect of 24-h lesion characteristics on EV and ecIV progression. We evaluated the influence of edema and edema-corrected infarct progression on favorable functional outcome after 90 days (modified Rankin Scale: 0–2) after correcting for potential confounders. Lastly, we compared these volumes between subgroups of patients with and without successful recanalization using the Mann–Whitney U-test.Results: Median TILV increased from 37 (IQR: 18–81) ml to 68 (IQR: 30–130) ml between 24 h and 1 week after stroke, while the net water uptake increased from 22 (IQR: 16–26)% to 27 (IQR: 22–32)%. The TILV progression of 20 (8.8–40) ml was mostly caused by ecIV with a median increase of 12 (2.4–21) ml vs. 6.5 (2.7–15) ml of EV progression. Larger TILV, EV, and ecIV volumes at 24 h were all associated with more edema and lesion progression. Edema progression was associated with unfavorable functional outcome [aOR: 0.53 (0.28–0.94) per 10 ml; p-value: 0.05], while edema-corrected infarct progression showed a similar, non-significant association [aOR: 0.80 (0.62–0.99); p-value: 0.06]. Lastly, edema progression was larger in patients without successful recanalization, whereas ecIV progression was comparable between the subgroups.Conclusion: EV increases in evolving ischemic lesions in the period between 1 day and 1 week after acute ischemic stroke. This progression is larger in patients without successful recanalization and is associated with unfavorable functional outcome. However, the extent of edema cannot explain the total expansion of ischemic lesions since edema-corrected infarct progression is larger than the edema progression.

Agreement and Accuracy of Ischemic Core Volume Evaluated by Three CT Perfusion Software Packages in Acute Ischemic Stroke

PurposeTo compare the ischemic core volume estimated by CT Perfusion 4D and Vue PACS with that estimated by RAPID software in acute ischemic stroke (AIS). Materials and methodsCT perfusion data from AIS patients were retrospectively post-processed with RAPID, CT Perfusion 4D and Vue PACS software. The Vue PACS application included three different settings: method A (Circular Singular Value Decomposition), method B (Oscillating index Singular Value Decomposition) and method C (Standard Singular Value Decomposition). Bland–Altman analysis, intraclass correlation coefficients (ICCs) and Kappa analysis were used to evaluate concordance between estimated ischemic core values. Final infarct volume (FIV) was measured by follow-up non-contrast CT or MRI 5-7 days after mechanical thrombectomy (MT) in patients with successful recanalization. ResultsA total of 82 patients were included in the study. Concordance with RAPID ranged from good (method B: ICC 0.780; method C: ICC 0.852) to excellent (CT perfusion 4D: ICC 0.950; method A: ICC 0.954). The limits of agreement (-32.3, 41.8 mL) were the narrowest with method A. For detecting core volumes ≤ 70 ml, method A and CT perfusion 4D showed almost perfect concordance with RAPID (CT perfusion 4D, kappa=0.87; method A, kappa=0.87), whereas methods B and C showed substantial concordance with RAPID (method B, kappa=0.77; method C, kappa =0.73). Thirty-two patients had good reperfusion after MT. RAPID showed the highest accuracy for predicting FIV, followed by method A. ConclusionCT perfusion 4D and Vue PACS method A showed excellent concordance with RAPID for quantifying ischemic core volume, which can be considered as alternatives in selecting patients for MT in clinical practice.

Relationship between chronic hyperglycemia and contrast extravasation in revascularization of symptomatic intracranial atherosclerotic stenosis: A retrospective single-center study

Background and purposeContrast extravasation is one of the most common perioperative complications in symptomatic intracranial atherosclerotic stenosis (ICAS) patients after percutaneous transluminal angioplasty and/or stenting (PTAS). This study aimed to investigate the correlations between the relevant serum biochemical indicators of carbohydrate metabolism and the occurrence of contrast extravasation. MethodsPatients’ demographic characteristics, vascular risk factors and laboratory examination data were collected. Blood routine test, blood biochemical examination and hormone level test within 1 week before surgery were measured in all enrolled subjects. Patients underwent non-contrast CT scans immediately after the endovascular procedure. Follow-up non-contrast CT scans were performed in the next 24 h and repeated as per clinical condition. Results104 patients who have undergone effective PTAS were involved in this study. 18 patients have identified as contrast extravasation and there was no obvious abnormality in another 86 cases. There were significant differences in the pre-operative HbA1c, fasting blood sugar and cortisol levels in the subjects regardless of gender between two groups (p < 0.001, p < 0.001 and p = 0.001, respectively). Furthermore, there were statistical differences in E2 and testosterone levels between two groups in both male population (p = 0.035 and p = 0.028, respectively) and female population (p = 0.036 and p = 0.003, respectively). Besides, the AUC value of HbA1c, fasting blood sugar and cortisol levels were all over 0.7 (0.858, 0.780 and 0.752, respectively). The highest AUC value of various combinations was obtained from the combination of HbA1c and cortisol level, which was 0.898. ConclusionsPatient with chronic hyperglycemia is closely related to contrast extravasation after PTAS. Specific mechanisms might be explored and regarded as promising candidates to prevent contrast extravasation.

A Detailed Analysis of Infarct Patterns and Volumes at 24-hour Noncontrast CT and Diffusion-weighted MRI in Acute Ischemic Stroke Due to Large Vessel Occlusion: Results from the ESCAPE-NA1 Trial.

Background The effect of infarct pattern on functional outcome in acute ischemic stroke is incompletely understood. Purpose To investigate the association of qualitative and quantitative infarct variables at 24-hour follow-up noncontrast CT and diffusion-weighted MRI with 90-day clinical outcome. Materials and Methods The Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke, or ESCAPE-NA1, randomized controlled trial enrolled patients with large-vessel-occlusion stroke undergoing mechanical thrombectomy from March 1, 2017, to August 12, 2019. In this post hoc analysis of the trial, qualitative infarct variables (predominantly gray [vs gray and white] matter involvement, corticospinal tract involvement, infarct structure [scattered vs territorial]) and total infarct volume were assessed at 24-hour follow-up noncontrast CT or diffusion-weighted MRI. White and gray matter infarct volumes were assessed in patients by using follow-up diffusion-weighted MRI. Infarct variables were compared between patients with and those without good outcome, defined as a modified Rankin Scale score of 0-2 at 90 days. The association of infarct variables with good outcome was determined with use of multivariable logistic regression. Separate regression models were used to report effect size estimates with adjustment for total infarct volume. Results Qualitative infarct variables were assessed in 1026 patients (mean age ± standard deviation, 69 years ± 13; 522 men) and quantitative infarct variables were assessed in a subgroup of 358 of 1026 patients (mean age, 67 years ± 13; 190 women). Patients with gray and white matter involvement (odds ratio [OR] after multivariable adjustment, 0.19; 95% CI: 0.14, 0.25; P < .001), corticospinal tract involvement (OR after multivariable adjustment, 0.06; 95% CI: 0.04, 0.10; P < .001), and territorial infarcts (OR after multivariable adjustment, 0.22; 95% CI: 0.14, 0.32; P < .001) were less likely to achieve good outcome, independent of total infarct volume. Conclusion Infarct confinement to the gray matter, corticospinal tract sparing, and scattered infarct structure at 24-hour noncontrast CT and diffusion-weighted MRI were highly predictive of good 90-day clinical outcome, independent of total infarct volume. Clinical trial registration no. NCT02930018 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Mossa-Basha in this issue.

Hypodensities within Hematoma is Time-Dependent and Predicts Outcome after Spontaneous Intracerebral Hemorrhage.

Non-contrast CT (NCCT) brain imaging biomarkers of hematoma expansion in intracerebral hemorrhage (ICH) has gained relevance in recent times. Though intra-hematoma hypodensities (IHH) can predict hematoma expansion and outcome, it is postulated to be time-dependent. To assess the differential prevalence of IHH in spontaneous ICH over time and assess its predictive valve in early hematoma expansion and functional outcome at 3 months. Patients with ICH within 48 h of stroke onset were included. Baseline clinical and demographic data were collected. Baseline NCCT brain was analyzed for hematoma volume, characterization of IHH, with 24-hours follow-up NCCT hematoma volume calculated for identification of hematoma expansion. Poor functional outcome was defined as mRS ≥3. Around 92 subjects were included in the study. IHH was found in 40%. Prevalence of IHH was higher in those with baseline NCCT performed within 3 h of symptom onset compared to those beyond 3 h (71% vs 29%, P = 0.002). The hematoma expansion was more common in patients with IHH compared to those without (54% vs 29%; P = 0.02). Multivariate analysis revealed the presence of IHH (rather than pattern or number) to be strongly associated with poor functional outcome at 3 months (OR 3.86; 95% CI: 1.11-13.42, P = 0.03). There is a decreasing prevalence of IHH as the time from symptom onset to NCCT increases. Nevertheless, its presence is significantly associated with hematoma expansion and predicted poor short-term functional outcomes in spontaneous ICH.

Prevalence of cognitive impairment in acute ischaemic stroke and use of Alberta Stroke Programme Early CT Score (ASPECTS) for early prediction of post-stroke cognitive impairment.

This study aims to assess the prevalence of post-stroke cognitive impairment, and to evaluate the correlation of ASPECTS with impaired cognition. 150 patients presenting with acute middle cerebral artery territory ischaemic stroke were included in this study. Risk factors of ischaemic stroke and the initial NIHSS were determined. An initial and a follow-up non-contrast CT brain were carried out after seven days which were assessed by ASPECTS. The prevalence of cognitive impairment was determined by MoCA during the follow up of patients after three months. Correlations of ASPECTS, NIHSS and MoCA were done by Spearman correlation. Multivariate logistic regression analysis was carried out for the independent variables of cognitive impairment. The prevalence of post-stroke cognitive impairment in this study, according to the threshold for cognitive impairment with a MoCA score of 25 or less, was 25.3% (38 patients). Significant positive correlations between ASPECTS and total MoCA test domains were found (r = 0.73 and P = 0.002). Logistic regression analysis demonstrated that the independent factors associated with cognitive impairment were older age, certain domains of the MoCA test like executive functions, memory, attention, language, NIHSS, HTN, and ASPECTS. There is a prevalence of cognitive impairment in about 25% of patients after three months of follow-up in cases with acute ischaemic stroke. ASPECTS is directly correlated with cognitive impairment, and may be considered as a biomarker of post-stroke cognitive impairment.