Early Hematoma Expansion Research Articles

Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images

To examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images. A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients' brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network. In the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network. The improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.

Higher Cerebral Blood Flow Predicts Early Hematoma Expansion in Patients With Intracerebral Hemorrhage: A Clinical Study.

The early hematoma expansion of intracerebral hemorrhage (ICH) indicates a poor prognosis. This paper studies the relationship between cerebral blood flow (CBF) around the hematoma and hematoma expansion (HE) in the acute stage of intracerebral hemorrhage. A total of 50 patients with supratentorial cerebral hemorrhage were enrolled in this study. They underwent baseline whole-brain CTP within 6 h after intracerebral hemorrhage, and non-contrast CT within 24 h. Absolute hematoma growth and relative hematoma growth were calculated, respectively. A relative growth of Hematoma volume >33% was considered to be hematoma expansion. The Ipsilateral peri-edema CBF and Ipsilateral edema CBF were calculated by CTP maps in patients with and without hematoma expansion, respectively. In this study the incidence of hematoma expansion in the early stage of supratentorial cerebral hemorrhage was 32%; The CBF of the hematoma expansion group was higher than that of the patients without hematoma expansion (23.5 ± 12.5 vs. 15.1 ± 7.4, P = 0.004). After adjusting for age, gender, Symptom onset to NCCT and Baseline hematoma volume, ipsilateral peri-edema CBF was still an independent risk factor for early HE (or = 1.095, 95% CI = 1.01–1.19, P = 0.024). Here, we concluded that higher cerebral blood flow predicts early hematoma expansion in patients with intracerebral hemorrhage.

Artificial Intelligence Can Effectively Predict Early Hematoma Expansion of Intracerebral Hemorrhage Analyzing Noncontrast Computed Tomography Image.

This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction. The baseline noncontrast computed tomography images within 6 h of intracerebral hemorrhage onset and the second noncontrast computed tomography performed at 24 ± 3 h from the onset were used to evaluate the prediction of intracerebral hemorrhage growth. In validation dataset 1, the AUC was 0.778 (95% CI, 0.768–0.786), the sensitivity was 0.818 (95% CI, 0.790–0.843), and the specificity was 0.601 (95% CI, 0.565–0.632). In validation dataset 2, the AUC was 0.780 (95% CI, 0.761–0.798), the sensitivity was 0.732 (95% CI, 0.682–0.788), and the specificity was 0.709 (95% CI, 0.658–0.759). The sensitivity of intracerebral hemorrhage hematoma expansion as predicted by an artificial intelligence imaging system was 89.3%, with a specificity of 77.8%, a positive predictive value of 55.6%, a negative predictive value of 95.9%, and a Yoden index of 0.671, which were much higher than those based on the manually labeled noncontrast computed tomography signs. Compared with the existing prediction methods through computed tomographic angiography (CTA) image features and noncontrast computed tomography image features analysis, the artificial intelligence model has higher specificity and sensitivity in the prediction of early hematoma enlargement in patients with intracerebral hemorrhage.

Feasibility of a combined swirl and blending sign on non-contrast computed tomography for predicting early hematoma expansion after spontaneous intracerebral hemorrhage.

For predicting the hematoma expansion of spontaneous intracerebral hemorrhage, spot and swirl signs have been investigated. However, the clinical effectiveness of these signs remains debatable. These signs do not consider the peripheral hypodense lesions, which may imply a greater chance of ongoing bleedings. We proposed a new combined swirl and blending sign and evaluated its clinical usefulness in predicting hematoma expansion in non-contrast computed tomography settings. A total of 201 patients who were diagnosed with spontaneous intracerebral hemorrhage were enrolled. Their clinical and radiologic data were retrospectively reviewed. Patients were classified into hematoma expansion (N.=51) and nonexpansion groups (N.=150), and multivariable logistic regression analyses were performed to identify the factors associated with hematoma expansion. In the hematoma expansion group, an average of 20 mL of volume increase was noted. In multivariate analyses, several factors, including higher systolic blood pressure (P=0.026), larger initial hematoma volume (0.002), spot sign (0.019) and combined swirl and blending sign (<0.001), were identified as reliable predictors of hematoma expansion. A swirl (P=0.396) or blending sign (P=0.124) alone was not identified as a significant predictor of hematoma growth. The sensitivity, specificity, and positive and negative predictive values of the combined swirl and blending sign were 31%, 97%, 80%, and 81%, respectively. A newly defined "combined swirl and blending sign" on non-contrast computed tomography was positively associated with an increased risk of hematoma expansion of spontaneous intracerebral hemorrhage and could be regarded as a reliable predictor in non-contrast computed tomography settings.

Neuroimaging of Acute Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) accounts for 10% to 20% of all strokes worldwide and is associated with high morbidity and mortality. Neuroimaging is clinically important for the rapid diagnosis of ICH and underlying etiologies, but also for identification of ICH expansion, often as-sociated with an increased risk for poor outcome. In this context, rapid assessment of early hema-toma expansion risk is both an opportunity for therapeutic intervention and a potential hazard for hematoma evacuation surgery. In this review, we provide an overview of the current literature surrounding the use of multimodal neuroimaging of ICH for etiological diagnosis, prediction of early hematoma expansion, and prognostication of neurological outcome. Specifically, we discuss standard imaging using computed tomography, the value of different vascular imaging modalities to identify underlying causes and present recent advances in magnetic resonance imaging and computed tomography perfusion.

A nomogram to predict early hematoma expansion of hypertensive cerebral hemorrhage.

Early hematoma expansion of hypertensive cerebral hemorrhage is affected by various factors. This study aimed to clarify the risk factors and develop a nomogram to predict early hematoma expansion.A retrospective analysis was carried out in patients with hypertensive cerebral hemorrhage admitted to our institution between January 1, 2012 and December 31, 2018; the patients were divided into 2 groups according to the presence of early hematoma expansion. Univariate and multivariate analyses were performed to analyze the risk factors of hematoma expansion. The nomogram was developed based on a multivariate logistic regression model, and the discriminative ability of the model was analyzed.A total of 477 patients with hypertensive cerebral hemorrhage and with a baseline hematoma volume <30 ml were included in our retrospective analysis. The hematoma expansion rate was 34.2% (163/477). After multivariate logistic regression, 9 variables (alcohol history, Glasgow coma scale score, total serum calcium, blood glucose, international normalized ratio, hematoma shape, hematoma density, volume of hematoma on initial computed tomography scan, and presence of intraventricular hemorrhage) identified as independent predictors of hematoma expansion were used to generate the nomogram. The area under the receiver operating characteristic curve of the nomogram was 0.883 (95% confidence interval 0.851–0.914), and the cutoff score was −0.19 with sensitivity of 75.5% and specificity of 87.3%.The nomogram can accurately predict the risk of early hematoma expansion.

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.

Supratentorial intracerebral hemorrhage volume and other CT variables predict the neurological pupil index

ObjectivesThere is growing interest in the ability of automated infrared pupillometry to assess severity of neurological illness. We studied the correlation between computed tomography (CT) indicators of intracerebral hemorrhage (ICH) severity with objective measures of the pupillary light reflex (PLR), and hypothesized that hemorrhage volume would predict the Neurological Pupil index™ (NPi™), an indicator of pupillary reactivity. MethodsThis study examined data from patients with supratentorial ICH who underwent serial pupillometer evaluations. CT images were examined to determine the location and laterality of the hemorrhage, along with hematoma volume (using the simplified ABC/2 method), midline shift, hydrocephalus score, and modified Graeb score (indicating interventricular hemorrhage). Demographics were examined with standard measures of central tendency, hypotheses with logistic regression, categorical data with Fisher’s Exact X2, and multivariate modeling with constructed MAX-R models. ResultsData were gathered from 44 subjects. ICH volume exhibited the strongest correlation with NPi (ipsilateral [r2 = 0.48, p < 0.0001, contralateral [(r2 = 0.39, p < 0.0001]). Horizontal midline shift of the septum pellucidum also correlated with NPi (ipsilateral [r2 = 0.25, p = 0.0006], contralateral [r2 = 0.15, p = 0.0106]), as did shift of the pineal gland (ipsilateral [r2 = 0.21, p = 0.0017], contralateral[r2 = 0.11, p = 0.0328]). ICH volume was the most predictive of abnormal NPi (AUC = 0.85 for ipsilateral and 0.88 for contralateral NPi), and multivariate modeling identified additional independent predictors of NPi. ConclusionICH volume and shift of midline structures correlate with NPi, and abnormalities in NPi can be predicted by hematoma volume and other CT indicators of ICH severity. Future studies should explore the role of NPi in detecting early hematoma expansion and worsening midline shift.

Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage.

ObjectiveTo determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH).Materials and MethodsWe retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power.ResultsThe AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively.ConclusionNCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

Absolute hypodensity sign by noncontrast computed tomography as a reliable predictor for early hematoma expansion

ObjectiveEarly hematoma expansion in intracerebral hemorrhage (ICH) patients is strongly associated with poor outcome and potentially preventable if high-risk patients are timely identified. We aimed to investigate whether a novel sign detected by noncontrast computed tomography (NCCT) named absolute hypodensity sign (AHS) was an appropriate candidate for hematoma expansion prediction. Materials and MethodsSpontaneous ICH patients all underwent baseline NCCT scan within 6 h after ICH onset and the follow-up NCCT scan within 24 h after initial CT scan. AHS was defined as a hypoattenuating area with a distinct margin within the hematoma, the minimal density of which should be ≤30 Hounsfeld units. We used univariate and multivariate logistic regression analyses for determining the association between hematoma expansion and the presence of AHS. We also compared the diagnostic efficacy with blend sign and black hole sign. ResultsA total of 348 ICH patients were included, 87 of which were found hematoma expansion. AHS was positive in 76 patients (21.8%) (κ = 0.88 for interrater reliability) and 63.2% of the patients with hematoma expansion were observed AHS. The multivariate logistic regression analysis demonstrated AHS was an independent predictor for hematoma expansion (odds ratio: 33.454, 95% confidence interval: 13.628–82.126, P < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AHS for hematoma expansion prediction were 63.2%, 92.0%, 72.4%, 88.2%, 84.8%, respectively, which were higher than blend sign and black hole sign except the specificity of black hole sign. ConclusionOur findings indicate AHS is a reliable predictor for early hematoma expansion due to its feasibility and convenience for clinical practice.

High plasma levels of trimethylamine N-oxide are associated with poor outcome in intracerebral hemorrhage patients.

As a gut microbiota-dependent metabolite, trimethylamine N-oxide (TMAO) has been implicated in cardiovascular diseases. We aimed to investigate the relationship between the clinical outcomes and plasma TMAO concentrations in patients with acute intracerebral hemorrhage. From January 2019 to October 2019, we prospectively enrolled intracerebral hemorrhage patients diagnosed within 6h of symptoms onset. Plasma TMAO levels was measured for all patients within 24h after admission. The primary outcome was functional outcome at 3months. Patients were dichotomized as good (modified Rankin scale 0-3) and poor (modified Rankin scale 4-6). Secondary outcome included early neurological deterioration (END) and hematoma enlargement (HE). There were 307 patients (57.7% male) with a mean age of 66.8years included in the study. The median TMAO levels was 3.2μmol/L. END, HE, and 3-month poor outcome were detected in 59 (19.2%), 54 (17.6%), and 139 (45.3%) patients, respectively. After adjusting for potential confounders, the odds ratio for the highest quartile of TMAO compared with the lowest quartile was 3.65 (95% confidence interval, 1.43-9.30) for 3-month poor outcome. Furthermore, multiple-adjusted spline regression model showed a linear association between TMAO levels and poor outcome at 3months (P = 0.013 for linearity). Similar significant findings were observed when functional outcome was analyzed by continuous mRS score. No association was found between TMAO levels and END and HE. The present study demonstrated that increased TMAO levels were independently correlated with 3-month function outcome after intracerebral hemorrhage.

Blend Sign Is a Strong Predictor of the Extent of Early Hematoma Expansion in Spontaneous Intracerebral Hemorrhage.

Background and Purpose: It is unclear which imaging marker is optimal for predicting the extent of hematoma expansion (EHE). We aimed to compare the usefulness of the blend sign (BS) with that of other non-contrast computed tomography (NCCT) markers for predicting the EHE in patients with spontaneous intracerebral hemorrhage (sICH).Methods: Patients with sICH admitted to our Neurology Emergency Department between September 2013 and January 2019 were enrolled. The EHE was calculated as the absolute increase in hematoma volume between baseline and follow-up CT (within 72 h). The EHE was categorized into four groups: “no growth,” “minimal change” (≤5.1 ml), “moderate change” (5.1–12.5 ml), and “massive change” (>12.5 ml). Univariate and multivariate analyses were performed to investigate the relationship between the NCCT markers [BS, black hole sign (BHS), satellite sign, and island sign] and the EHE.Results: A total of 1,111 sICH patients were included (median age: 60 years; 66.5% males). Multiple linear regression analysis showed that the presence of the BS and BHS was independently associated with the EHE, after adjusting for confounders (P < 0.001 and P = 0.003, respectively). The presence of the BS and BHS was positively correlated with growth category (r = 0.285 and r = 0.199, both Ps < 0.001). The BS demonstrated a better predictive performance for the EHE than did the BHS [area under the curve (AUC): 0.67 vs. 0.57; both Ps < 0.001].Conclusions: In patients with acute sICH, the BS showed a better performance in predicting the EHE compared with other NCCT markers. This imaging marker may help identify patients at a high risk of significant hematoma expansion and may facilitate its early management.

Can a Non-Contrast CT Scan of Brain Predict Hematoma Expansion in Acute Intracerebral Hemorrhage?

A 52-year-old male presented with acute intracerebral hemorrhage. The non-contrast CT scan of brain was suggestive of the “CT blend sign” which predicts early hematoma expansion and poor prognosis. Despite appropriate management, hematoma growth was seen in repeat CT scan of brain. Other signs which predict hematoma growth are swirl sign, black hole sign, and fluid levels on non-contrast CT scan and spot sign on contrast-enhanced CT scan.

Abstract TP332: Multi-Institutional Validation of the NAG Scale for Predicting Hematoma Expansion in Acute Intracerebral Hemorrhage Patients

Background and Purpose: Early hematoma expansion (HE) is seen in about 30% of intracerebral hemorrhage (ICH) patients, but detecting those patients with high risk of HE is challenging. The NAG scale was previously published as the simple predictive scale for HE in acute ICH patients. Multi-institutional validation for utility of the scale was the aim of this study. Methods: We retrospectively reviewed consecutive primary ICH patients, who were admitted between September 2016 and December 2018 to Jikei University Hospital or Kashiwa Hospital, Japan. NAG scale is consist of 3 factors based on examination on admission; NIHSS ≥10, Anticoagulant agents use, Glucose ≥133 mg/dl, with 1 point assigned for each parameter. Patients received an initial non-contrast computed tomography (CT) scan within 24 hours from symptom onset, and underwent follow-up CT scans at 6 hours, 24 hours, and 7 days after admission. The HE was defined as an increment in hemorrhage volume &gt;33% or an absolute increase &gt;6 mL on follow-up CT scans. Poor prognosis was defined as modified Rankin Scale 4-6 at discharge. We performed logistic regression analysis and receiver operating characteristic curves to determine discrimination ability of the score. Results: A total of 142 patients (96 men; median age 64 years; median NIHSS 11) were included in our study, and HE was observed in 38 patients (27%). Higher NAG sores were related to HE (P&lt;0.001), poor prognosis (P&lt;0.001), and in-hospital death (P&lt;0.001). The C statistic was 0.72 (95% confidence interval [CI], 0.63-0.82) for HE, 0.67 (95% CI, 0.58-0.76) for poor prognosis, and 0.85 (95% CI, 0.74-0.95) for in-hospital death. Multivariate logistic regression analysis with known risk factors showed the NAG scale was the independent factor for HE (Odds ratio, 2.95; 95% CI, 1.57-5.52; P = 0.001). Conclusion: Multi-institutional validation of the NAG scale showed good discrimination.

Predictive values of island signs and blend signs on non-contrast CT in early hematoma enlargement of spontaneous intracerebral hemorrhage

Objective To explore the predictive values of island signs and blend signs on non-contrast CT in early hematoma enlargement of spontaneous intracerebral hemorrhage (sICH). Methods A total of 84 patients with sICH were admitted to Department of Neurosurgery, Wenjiang District People′s Hospital of Chengdu from January 2015 to December 2017 and consecutively enrolled into this study. The time from onset to the first CT examination was ≤6 h, and the second CT examination time was ≤24 h. According to the results of those 2 examinations, the patients were divided into the early hematoma enlargement group (35 cases) and the hematoma non-enlargement group (49 cases). The effects of CT island signs and blend signs on early hematoma enlargement and their diagnostic efficacy for early hematoma enlargement were analyzed. Results Non-contrast CT scan showed that 60.0% (21/35) in the early hematoma enlargement group had island sign, and 71.4% (25/35) had blend sign. Compared with 14.3% (7/49) and 24.5% (12/49) in non-enlargement group, those differences were statistically significant (both P<0.01). Multivariate logistic regression analysis revealed that both island sign (OR=12.720, 95% CI: 3.501-46.218, P<0.01) and blend sign (OR=10.793, 95% CI: 3.172-36.727, P=0.01) were independent predictors of early hematoma enlargement. The sensitivity, specificity, positive predictive value, negative predictive value and Youden index of island sign for early hematoma enlargement were 60.0%, 85.7%, 75.0%, 75.0% and 45.7% respectively. The sensitivity, specificity, positive predictive value, negative predictive value and Youden index of blend sign for early hematoma enlargement were 71.4%, 75.5%, 67.6%, 78.7% and 46.9% respectively. The sensitivity, specificity, positive predictive value, negative predictive value and Youden index of island sign combined with blend sign for early hematoma enlargement were 91.4%, 63.3%, 64.0%, 91.2% and 54.7% respectively. Conclusions Island sign and blend sign are independent predictors of early enlargement of hematoma. The diagnostic performance with the combination of island sign and blend sign seems higher than the that of either single indicator. Key words: Spontaneous intracerebral hemorrhage; X-ray computed tomography; Hematoma enlargement; Island sign; Blend sign

Hypodensities within hematoma is time dependent and predicts outcome after spontaneous intracerebral hemorrhage

NCCT brain imaging biomarkers of hematoma expansion in ICH has gained relevance in recent times. Though the intra-hematoma hypodensities can predict hematoma expansion and outcome, it is postulated to be time dependent. To assess the differential prevalence of intra-hematoma hypodensity in spontaneous ICH over time from performance of NCCT from symptom onset and assess its predictive valve in early hematoma expansion and functional outcome at 3 months. Patients with ICH within 48 hours of stroke onset were included. Baseline clinical and demographic data was collected. Baseline NCCT Brain was analysed for hematoma volume, characterisation of intra-hematoma hypodensity, with 24 hour follow up NCCT hematoma volume calculated for identification of hematoma expansion. Poor functional outcome was defined as mRS> 3. 92 subjects were included in the study. Intra-hematoma hypodensity was found in 40%. Prevalence of intra-hematoma hypodensity was higher in those with baseline NCCT performed within 3 hours of symptom onset compared to those beyond 3 hours. 54% of patients with intra-hematoma hypodensity had hematoma expansion compared to 29% without (P = 0.02). Multivariate analysis revealed presence of intra-hematoma hypodensity (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 intra-hematoma hypodensity as time from symptom onset to NCCT increases. Nevertheless the presence of intra-hematoma hypodensity is significantly associated with hematoma expansion and predicted poor short term functional outcomes in spontaneous ICH.

External validation of the nag scale for predicting hematoma expansion in patients with acute intracerebral hemorrhage

Early hematoma expansion (HE) is seen in about 30% of intracerebral hemorrhage (ICH) patients, but detecting high risk factors for HE is challenging. The NAG scale was one of the proposing factors for HE in patients with acute ICH. The aim of this study is to validate the scale. Between January 2016 and December 2018, we retrospectively reviewed consecutive series of patients with primary ICH, who were diagnosed by an initial non-contrast computed tomography (CT) scan within 24 h after onset. Patients underwent follow-up CT scans at 6 h, 24 h, and 7 days after admission, and the HE was defined as an increasing rate in hematoma volume >33% or an absolute volume of increment >6 mL on follow-up CT scans, compared with initial CT scans. Poor prognosis was defined as modified Rankin Scale 4–6 at discharge. Logistic regression analysis and receiver operating characteristic curves were used to determine discrimination ability of the score. A total of 82 patients (51 men; median age 67 years) were analysed in our study, and HE was included in 25 patients (30%). Higher NAG sores were related to HE, poor prognosis, and in-hospital death. The C statistic was 0.77 (95% confidence interval [CI], 0.66–0.88) for HE, 0.77 (95% CI, 0.67–0.87) for poor prognosis, and 0.80 (95% CI, 0.68–0.92) for in-hospital death. Multivariate logistic regression analysis with known risk factors showed the NAG scale was the sole predictor for HE. External validation of the NAG scale showed good discrimination.

Early hematoma expansion in primary intracerebral hemorrhage: incidence and predictors

BackgroundHematoma expansion (HE) is the leading cause of early neurological deterioration, poor functional outcome, and increased mortality in patients with spontaneous intracerebral hemorrhage (S-ICH). The study aimed to estimate the risks and predictors of early HE in patients with S-ICH and the effect of this HE on patient’s survival and functional outcome.MethodsThis study was carried out on 72 patients with S-ICH submitted to baseline non-contrast brain CT (NCCT) and CT angiography for determination of hematoma site, size, border irregularity, blend sign, and spot sign score (SSS). Rescan was done 48 h after stroke onset or on clinical deterioration to resize the HV and diagnose HE. Modified Rankin Scale (MRS) was done 3 months after stroke onset to assess the effect of HE on patients’ physical dependence.ResultsHE occurred in 28/72 (38.9%) of included patients. Risks of HE included old age, smoking, elevated baseline mean arterial blood pressure, and high admission modified National Institute of Health stroke scale. NCCT predictors of HE included large volume, irregular border, and presence of blend sign. The presence of spot sign in early CTA is more accurate than NCCT predictors with 54%, 91%, 79%, and 75% for sensitivity, specificity, positive predictive value, and negative predictive value, respectively.ConclusionsHE is a major cause of early clinical deterioration, increased mortality, and poor functional outcome. Early CTA for detection of spot sign is indicated in patients with large volume, irregular border, and/or blend sign in NCCT.

Hematoma Expansion within 24 hours of Hypertensive Intracerebral Hemorrhage and Its Association with Signs on Nonenhanced Computed Tomography

To explore the correlation between hematoma expansion within 24 hours of hypertensive intracerebral hemorrhage and signs on nonenhanced computed tomography(NECT). Methods The clinical data and CT images of 185 patients with hypertensive intracerebral hemorrhage were retrospectively analyzed.The differences in CT parameters were compared between the expansion group and the unexpanded group.Binary logistic regression analysis was performed on the indicators with statistical significance between the two groups to identify the potential correlation between CT parameters and hematoma expansion.The roles of blend sign,lobulation sign,and black hole sign in predicting early hematoma expansion were assessed. Results The CT quantitative data including initial volume,maximum diameter,minimum diameter,maximum CT value,mean CT value,difference between maximum diameter and minimum diameter showed no significant difference between these two group(all P>0.05).CT qualitative data including blend sign,lobulation sign,and black hole sign were significantly different(P<0.05)but the differences became not significant after the hematoma broke into the ventricles.Binary logistic regression analysis showed that blend sign,lobulation sign,and black hole sign were independent risk factors for early hematoma expansion,with sensitivity,specificity,positive predictive value,and negative predictive value of the combined signs and the early hematoma expansion calculated by the four-fold table method being 78.4%,59.0%,42.1%,and 87.8%,respectively,and the Youden index was 0.374.Its Youden index was closer to 1 than the blend sign,the black hole sign,and the lobulation sign. Conclusion The blend sign,lobulation sign,and black hole sign in NECT can be used to predict hematoma expansion within 24 hours after hypertensive cerebral hemorrhage.

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

To develop a radiomics model for predicting hematoma expansion in patients with intracerebral hemorrhage (ICH) and to compare its predictive performance with a conventional radiological feature-based model. We retrospectively analyzed 251 consecutive patients with acute ICH. Two radiologists independently assessed baseline noncontrast computed tomography (NCCT) images. For each radiologist, a radiological model was constructed from radiological variables; a radiomics score model was constructed from high-dimensional quantitative features extracted from NCCT images; and a combined model was constructed using both radiological variables and radiomics score. Development of models was constructed in a primary cohort (n = 177). We then validated the results in an independent validation cohort (n = 74). The primary outcome was hematoma expansion. We compared the three models for predicting hematoma expansion. Predictive performance was assessed with the receiver operating characteristic (ROC) curve analysis. In the primary cohort, combined model and radiomics model showed greater AUCs than radiological model for both readers (all p < .05). In the validation cohort, combined model and radiomics model showed greater AUCs, sensitivities, and accuracies than radiological model for reader 2 (all p < .05). Combined model showed greater AUC than radiomics model for reader 1 only in the primary cohort (p = .03). Performance of three models was comparable between reader 1 and reader 2 in both cohorts (all p > .05). NCCT-based radiomics model showed high predictive performance and outperformed radiological model in the prediction of early hematoma expansion in ICH patients. • Radiomics model showed better performance for prediction of hematoma expansion in patients with intracerebral hemorrhage than radiological feature-based model. • Hematomas which expanded in follow-up NCCT tended to be larger in baseline volume, more irregular in shape, more heterogeneous in composition, and coarser in texture. • A radiomics model provides a convenient and objective tool for prediction of hematoma expansion that helps to define subsets of patients who would benefit from anti-expansion therapy.