Blend Sign on Computed Tomography

Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage

INTRODUCTION

Perihematomal edema (PHE) represents secondary brain injury and a potential treatment target in intracerebral hemorrhage (ICH). However, studies differ on optimal PHE volume metrics as prognostic factor(s) after spontaneous, non-traumatic ICH. This study examines associations of baseline and 24-h PHE shape features with 3-month outcomes.

PATIENTS AND METHODS

We included 796 patients from a multicentric trial dataset and manually segmented ICH and PHE on baseline and follow-up CTs, extracting 14 shape features. We explored the association of baseline, follow-up, difference (baseline/follow-up) and temporal rate (difference/time gap) of PHE shape changes with 3-month modified Rankin Score (mRS) - using Spearman correlation. Then, using multivariable analysis, we determined if PHE shape features independently predict outcome adjusting for patients' age, sex, NIH stroke scale (NIHSS), Glasgow Coma Scale (GCS), and hematoma volume.

RESULTS

Baseline PHE maximum diameters across various planes, main axes, volume, surface, and sphericity correlated with 3-month mRS adjusting for multiple comparisons. The 24-h difference and temporal change rates of these features had significant association with outcome - but not the 24-h absolute values. In multivariable regression, baseline PHE shape sphericity (OR = 2.04, CI = 1.71-2.43) and volume (OR = 0.99, CI = 0. 98-1.0), alongside admission NIHSS (OR = 0.86, CI = 0.83-0.88), hematoma volume (OR = 0.99, CI = 0. 99-1.0), and age (OR = 0.96, CI = 0.95-0.97) were independent predictors of favorable outcomes.

CONCLUSION

In acute ICH patients, PHE shape sphericity at baseline emerged as an independent prognostic factor, with a less spherical (more irregular) shape associated with worse outcome. The PHE shape features absolute values over the first 24 h provide no added prognostic value to baseline metrics.

Is the frequency of imaging markers still a predictor for revised intracerebral hemorrhage expansion?

INTRODUCTION

Frequency of imaging markers (FIM) has been described as a novel predictor for hematoma expansion after intracerebral hemorrhage (ICH). A revised definition of hematoma expansion that incorporates intraventricular hemorrhage (IVH) growth, that is, revised hematoma expansion (RHE), has also been proposed. Nevertheless, the associations between FIM and IVH growth or RHE remains unexplored. The objective of this study was to assess the influence and performance of the FIM on two types.

MATERIALS AND METHODS

Patient selection and variables were based on our published protocol. FIM was defined as the ratio of the number of imaging markers to the onset-to-neuroimaging time. The association between FIM and two definitions was tested by multivariate analysis. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the FIM on two definitions were also evaluated.

RESULTS

There were 303 (20.36%) and 583 (39.18%) subjects in the IVH growth and RHE, respectively. Multivariate analysis demonstrated that FIM was associated with both IVH growth and RHE (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.60-2.39; OR = 15.01, 95% CI = 10.51-21.43, respectively). The optimal cutoff points for FIM to predict IVH growth and RHE were 0.63 and 0.62, with AUC, sensitivity, specificity, PPV, and NPV of 0.66, 0.50, 0.78, 0.36, and 0.86 versus 0.80, 0.60, 0.93, 0.84, and 0.78, respectively.

DISCUSSION AND CONCLUSION

FIM was not only a predictor of IVH growth, but also of RHE. These findings may have important clinical implications for decision-making based on risk stratification of patients with ICH.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm

OBJECTIVE

The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hence, our objective was to examine the predictive value of clinical factors and CT image markers for HE within the initial 24 hours using machine learning algorithms.

METHODS

Four machine learning algorithms, including extreme gradient boosting (XGBoost), support vector machine, random forest, and logistic regression, were employed to assess the predictive efficacy of HE within every 6-hour interval during the first 24 hours post-ICH. The area under the receiver operating characteristic curves was utilized to appraise predictive performance across various time periods within the initial 24 hours.

RESULTS

A total of 604 patients were included, with 326 being male, and 112 experiencing hematoma expansion (HE). The findings from machine learning algorithms revealed that computed tomography (CT) image markers, baseline hematoma volume, and other factors could accurately predict HE. Among these algorithms, XGBoost demonstrated the most robust predictive model results. XGBoost's accuracy at different time intervals was 0.89, 0.82, 0.87, and 0.94, accompanied by F1-scores of 0.89, 0.80, 0.87, and 0.93, respectively. The corresponding area under the curve was 0.96, affirming the precision of the predictive capability.

CONCLUSIONS

Computed tomography (CT) imaging markers and clinical factors could effectively predict HE within the initial 24 hours across various time periods by machine learning algorithms. In the expansive landscape of big data and multimodal cerebral hemorrhage, machine learning held significant potential within the realm of neuroscience.

Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison

BACKGROUND

Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear.

METHODS

The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE.

RESULTS

We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models.

CONCLUSIONS

NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Added Value of Frequency of Imaging Markers for Prediction of Outcome After Intracerebral Hemorrhage: A Secondary Analysis of Existing Data

BACKGROUND

Frequency of imaging markers (FIM) has been identified as an independent predictor of hematoma expansion in patients with intracerebral hemorrhage (ICH), but its impact on clinical outcome of ICH is yet to be determined. The aim of the present study was to investigate this association.

METHODS

This study was a secondary analysis of our prior research. The data for this study were derived from six retrospective cohorts of ICH from January 2018 to August 2022. All consecutive study participants were examined within 6 h of stroke onset on neuroimaging. FIM was defined as the ratio of the number of imaging markers on noncontrast head tomography (i.e., hypodensities, blend sign, and island sign) to onset-to-neuroimaging time. The primary poor outcome was defined as a modified Rankin Scale score of 3-6 at 3 months.

RESULTS

A total of 1253 patients with ICH were included for final analysis. Among those with available follow-up results, 713 (56.90%) exhibited a poor neurologic outcome at 3 months. In a univariate analysis, FIM was associated with poor prognosis (odds ratio 4.36; 95% confidence interval 3.31-5.74; p < 0.001). After adjustment for age, Glasgow Coma Scale score, systolic blood pressure, hematoma volume, and intraventricular hemorrhage, FIM was still an independent predictor of worse prognosis (odds ratio 3.26; 95% confidence interval 2.37-4.48; p < 0.001). Based on receiver operating characteristic curve analysis, a cutoff value of 0.28 for FIM was associated with 0.69 sensitivity, 0.66 specificity, 0.73 positive predictive value, 0.62 negative predictive value, and 0.71 area under the curve for the diagnosis of poor outcome.

CONCLUSIONS

The metric of FIM is associated with 3-month poor outcome after ICH. The novel indicator that helps identify patients who are likely within the 6-h time window at risk for worse outcome would be a valuable addition to the clinical management of ICH.

Value of Image Biomarkers Based on Dual-Energy Computed Tomography Angiography Material Separation Technique in Predicting Early Hematoma Expansion in Spontaneous Intracerebral Hemorrhage

OBJECTIVE

This study aims to investigate the connection between the leakage sign (LS) and hematoma expansion (HE) in cases of spontaneous intracerebral hemorrhage. The investigation employs dual-energy computed tomography angiography (DECTA).

METHODS

A prospective cohort study was conducted, in which clinical and DECTA imaging data were collected from intracerebral hemorrhage patients within 6 hours of onset between January 2021 and June 2023. Exposure factors included DE-LS and traditional imaging biomarkers. The occurrence of HE on computed tomography rescanned within 24 hours was the observed outcome. Exposed and confounding factors were considered in both univariate and multivariate regression analyses based on the results. Logistic and adjusted Poisson regressions were employed, and odds ratios (ORs) and relative risks (RRs) were calculated with 95% confidence intervals.

RESULTS

The study enrolled a total of 90 patients, of whom 32 cases manifested HE, while 58 cases did not exhibit HE. Univariate analysis revealed statistically significant differences in parameters such as admission diastolic blood pressure, C-reactive protein, Glasgow Coma Scale, baseline hematoma volume, and imaging biomarkers like DE-spot sign and DE-LS. The OR value of DE-LS was determined as 48.21, with an RR value of 7.51. Multivariate adjusted Poisson regression analysis demonstrated that DE-LS was a robust independent predictor (RR = 4.11, 95% confidence interval: 1.49-11.35; P < 0.001).

CONCLUSIONS

DECTA-based DE-LS stands out as an independent predictor of HE. The utilization of RR values over OR values is endorsed when assessing the risk of HE prediction.

CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign

Background and purpose

This study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign.

Materials and methods

A retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC).

Results

For the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041).

Conclusion

Radiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

The frequency of imaging markers adjusted for time since symptom onset in intracerebral hemorrhage: A novel predictor for hematoma expansion

BACKGROUND

Hematoma expansion (HE) is common in patients with intracerebral hemorrhage (ICH) and associated with a worse outcome. Imaging makers and shorter time from symptom onset are both associated with HE, but prognostic scores based on these parameters individually have not been satisfactory. We hypothesized that a score including both imaging markers of expansion, and time of onset, would improve prediction.

METHODS

Patients with supratentorial ICH within 6 h after onset were consecutively recruited from six centers between January 2018 and August 2022. Three markers were used: hypodensities, the blend sign, and the island sign. We first defined frequency of imaging markers (FIM) as the relationship between the number of imaging markers and onset-to-CT time (OCT). The time-adjusted FIM was defined as the ratio of the number of imaging markers to the onset-to-initial imaging time. Multivariate analysis was performed to determine the relationship between FIM and HE. Receiver operating curve analysis was used to identify potential threshold values of FIM that optimally predict HE. In addition, the sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and the area under the curve (AUC) of the optimal cut-off in predicting HE were calculated.

RESULTS

In total, 1488 patients were eligible for inclusion, of whom 418 had incident HE. Multivariate analysis showed that age, male sex, baseline Glasgow Coma Scale score, presence of intraventricular hemorrhage, and FIM were independent predictors of HE (odds ratio (OR) = 0.98, 95% confidence interval (CI) = 0.97-0.99; OR = 1.73, 95% CI = 1.28-2.35; OR = 0.87, 95% CI = 0.83-0.92; OR = 0.42, 95% CI = 0.28-0.62; OR = 7.82, 95% CI = 5.86-10.42, respectively). The optimal cut-off point for FIM in predicting HE was 0.63, with sensitivity, specificity, PPV, NPV, and AUC values of 0.69, 0.89, 0.71, 0.88, and 0.83, respectively.

CONCLUSION

The FIM adjusted for time since symptom onset is a significant predictor of HE. Its use may allow improved prediction of those patients with ICH who develop HE, and the score may be clinically applicable in the management of patients with ICH.

Association of non-contrast CT markers with long-term functional outcome in deep intracerebral hemorrhage

Objective

Hematoma expansion (HE) is the most important therapeutic target during acute care of patients with intracerebral hemorrhage (ICH). Imaging biomarkers such as non-contrast CT (NCCT) markers have been associated with increasing risk for HE. The aim of the present study was to evaluate the influence of NCCT markers with functional long-term outcome and with HE in patients with deep (basal ganglia and thalamus) ICH who represent an important subgroup of patients at the highest risk for functional deterioration with HE due to the eloquence of the affected brain region.

Methods

From our prospective institutional database, all patients maximally treated with deep ICH were included and retrospectively analyzed. NCCT markers were recorded at diagnostic imaging, ICH volume characteristics were volumetrically evaluated, and all patients received follow-up imaging within 0-48 h. We explored associations of NCCT makers with unfavorable functional outcome, defined as modified Rankin scale 4-6, after 12 months and with HE. Bias and confounding were addressed by multivariable regression modeling.

Results

In 322 patients with deep ICH, NCCT markers were distributed as follows: irregular shape: 69.6%, heterogenous density: 55.9%, hypodensities: 52.5%, island sign: 19.3%, black hole sign: 11.5%, and blend sign: 4.7%. Upon multivariable regression analyses, independent associations were documented with the functional outcome for irregular shape (aOR: 2.73, 95%CI: 1.42-5.22, p = 0.002), heterogenous density (aOR: 2.62, 95%CI: 1.40-4.90, p = 0.003) and island sign (aOR: 2.54, 95%CI: 1.05-6.14, p = 0.038), and with HE for heterogenous density (aOR: 5.01, 95%CI: 1.93-13.05, p = 0.001) and hypodensities (aOR: 3.75, 95%CI: 1.63-8.62, p = 0.002).

Conclusion

NCCT markers are frequent in deep ICH patients and provide important clinical implications. Specifically, markers defined by diverging intra-hematomal densities provided associations with a 5-times higher risk for HE and a 2.5-times higher likelihood for unfavorable functional long-term outcome. Hence, these markers allow the identification of patients with deep ICH at high risk for clinical deterioration due to HE.

Hematoma Enlargement After Intracerebral Hemorrhage: A Bibliometric Analysis

OBJECTIVE

To conduct a quantitative analysis of published studies on hematoma enlargement after intracerebral hemorrhage.

METHODS

Studies on hematoma enlargement after cerebral hemorrhage were retrieved from the Web of Science database on June 30, 2023. Microsoft Excel, VOSviewer, and CiteSpace software were used for bibliometric analysis and visualization, focusing on the quantitative characteristics of the literature.

RESULTS

A total of 444 articles were published in 161 journals, with 2161 authors from 41 countries and 717 institutions. The most published authors, countries, and institutions were Goldstein, the USA, and Massachusetts General Hospital. Stroke published the most studies, but the average citation number per year of Lancet Neurology far exceeded that of other journals. The research field of hematoma enlargement is mainly divided into 3 focuses, including mechanisms, identification (computed tomography signs, predictive models), and treatment (hemostasis, antihypertensive therapy). Most bursts in publication number have been since 2010, where the highest burst was from research on spot signs, and the latest burst focused on tranexamic acid. Treatment using tranexamic acid based on different computed tomography signs is a focus of current research, but the effectiveness still requires further exploration.

CONCLUSIONS

This bibliometric analysis analyzed the research framework and hotspots on hematoma enlargement after cerebral hemorrhage, which can help researchers better understand this field and provide potential suggestions for collaborations and research.

Clinical Significance of Stiffness during Endoscopic Surgery for Intracerebral Hemorrhage: A Retrospective Study

Studies regarding hematoma stiffness and removal difficulty are scarce. This study explored the association between hematoma stiffness and surgical results of endoscopic hematoma removal for intracerebral hemorrhage. It also aimed to clarify factors associated with hematoma stiffness. We classified intracerebral hematoma as either soft or firm stiffness by retrospectively evaluating operative videos by two neurosurgeons. The interobserver reliability of the classification was assessed by calculating the κ values. We investigated the relationship between hematoma stiffness and surgical results. Favorable hematoma removal (FHR) was defined as a residual hematoma volume of ≤15 mL or removal rate of ≥70%. Furthermore, we compared the background characteristics, imaging findings, and laboratory data between the two groups. Forty patients were included in this study. The mean baseline hematoma volume was 69.9 mL (range, 41.3-97.6 mL). FHR was accomplished in 35 cases (87.5%). Thirty-four patients (85%) were in the soft hematoma group (group S). Six patients (15%) were in the firm hematoma group (group F). Classification of hematoma stiffness demonstrated an excellent degree of interobserver agreement (κ score = 0.91). Patients in group S had a high FHR rate (p = 0.018) and short endoscopic procedure times (p = 0.00034). The island sign was present in group S (p = 0.030). Patients in group F had significantly high fibrinogen levels (p = 0.049) and low serum total calcium (p = 0.032), hemoglobin (p = 0.041), and hematocrit (p = 0.011) levels. Hematoma stiffness during endoscopic surgery for intracerebral hemorrhage correlates with surgical results, including the endoscopic procedure time and accomplishing rate of FHR.

Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage

PURPOSE

Rapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction.

METHODS

Original data was retrospectively obtained from three major prospective clinical trials ["Spot Sign" Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set.

RESULTS

317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE.

CONCLUSION

NCCT-based radiological and radiomics features can improve HE prediction when added to clinical features.

Non-contrast computed tomography features predict intraventricular hemorrhage growth

OBJECTIVES

Non-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth.

METHODS

Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models.

RESULTS

A total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16-2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48-2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91-3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion.

CONCLUSIONS

NCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies.

CLINICAL RELEVANCE STATEMENT

Non-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies.

KEY POINTS

• NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences. • The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion. • Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.

Blend Sign and Haemorrhage Location and Volume Predict Late Recurrence and Mortality in Intracerebral Haemorrhage Patients

BACKGROUND

Studies on risk factors for primary intracerebral haemorrhage (ICH) focus on short-term predictive values of distinct clinical parameters or computed tomography (CT) markers and disregard the others. We, therefore, studied independent predictive values of demographic, clinical, and CT markers regarding ICH expansion, late ICH recurrence, and late mortality.

METHODS

In a retrospective study of 288 patients with primary ICH, ICH localization (158 lobar, 81 deep, and 49 cerebellar), volume, blend sign, spot sign, finger-like projections, and subarachnoid haemorrhages were evaluated. ICH localization-specific differences for demographic (age, sex), clinical parameters (vascular risk factors, antiplatelet, and anticoagulation therapy), and CT markers were evaluated using logistic regression. We applied Cox proportional hazards modelling using these parameters to predict risk factors for ICH expansion, late ICH recurrence, and late mortality.

RESULTS

The blend sign in lobar ICH relates to increased risk of ICH expansion (HR2.3), late ICH recurrence (HR2.3), and mortality (HR1.6). Age, conditions requiring antiplatelet medication, deep ICH localization, volume, and blend sign represented the most important independent factors impacting overall mortality.

CONCLUSIONS

Blend sign at baseline ICH is a manifestation of underlying detrimental vascular processes that signal increased ICH expansion risk, although is also indicative of long-term risks for late recurrent ICH and late mortality.

Derivation and validation of three intracerebral hemorrhage expansion scores using different CT modalities

OBJECTIVES

To derivate and validate three scores for the prediction of intracerebral hemorrhage (ICH) expansion depending on the use of non-contrast CT (NCCT), single-phase CTA, or multiphase CTA markers of hematoma expansion, and to evaluate the added value of single-phase and multiphase CTA over NCCT.

METHODS

After prospectively deriving NCCT, single-phase CTA, and multiphase CTA hematoma expansion scores in 156 patients with ICH < 6 h, we validated them in 120 different patients. Discrimination and calibration of the three scores was assessed. Primary outcome was substantial hematoma expansion > 6 mL or > 33% at 24 h.

RESULTS

The evaluation of single-phase and multiphase CTA markers gave a steadily increase in discrimination for substantial hematoma expansion over NCCT markers. The C-index (95% confidence interval) in derivation and validation cohorts was 0.69 (0.58-0.80) and 0.59 (0.46-0.72) for NCCT score, significantly lower than 0.75 ([0.64-0.87], p = 0.038) and 0.72 ([0.59-0.84], p = 0.016) for single-phase CTA score, and than 0.79 ([0.68-0.89], p = 0.033) and 0.73 ([0.62-0.85], p = 0.031) for multiphase CTA score, respectively. The three scores showed good calibration in both derivation and validation cohorts: NCCT (χ2 statistic 0.389, p = 0.533; and χ2 statistic 0.352, p = 0.553), single-phase CTA (χ2 statistic 2.052, p = 0.359; and χ2 statistic 2.230, p = 0.328), and multiphase CTA (χ2 statistic 0.559, p = 0.455; and χ2 statistic 0.020, p = 0.887) scores, respectively.

CONCLUSION

This study shows the added prognostic value of more advanced CT modalities in acute ICH evaluation. NCCT, single-phase CTA, and multiphase CTA scores may help to refine the selection of patients at risk of expansion in different decision-making scenarios.

KEY POINTS

• This study shows the added prognostic value of more advanced CT modalities in acute intracerebral hemorrhage evaluation. • The evaluation of single-phase and multiphase CTA markers provides a steadily increase in discrimination for intracerebral hemorrhage expansion over non-contrast CT markers. • Non-contrast CT, single-phase CTA, and multiphase CTA scores may help clinicians and researchers to refine the selection of patients at risk of intracerebral hemorrhage expansion in different decision-making scenarios.

Predictor of the Postoperative Swelling After Craniotomy for Spontaneous Intracerebral Hemorrhage: Sphericity Index as a Novel Parameter

Objective

Spontaneous intracerebral hemorrhage is a serious type of stroke with high mortality and disability rates. Surgical treatment options vary; however, predicting edema aggravation is crucial when choosing the optimal approach. We propose using the sphericity index, a measure of roundness, to predict the aggravation of edema and guide surgical decisions.

Methods

We analyzed 56 cases of craniotomy and hematoma evacuation to investigate the correlation between the sphericity index and patient outcomes, including the need for salvage decompressive craniectomy (DC).

Results

The patients included 35 (62.5%) men and 21 (37.5%) women, with a median age of 62.5 years. The basal ganglia was the most common location of hemorrhage (50.0%). The mean hematoma volume was 86.3 cc, with 10 (17.9%) instances of hematoma expansion. Cerebral herniation was observed in 44 (78.6%) patients, intraventricular hemorrhage in 34 (60.7%), and spot signs in 9 (16.1%). Salvage DC was performed in 13 (23.6%) patients to relieve intracranial pressure. The median follow-up duration was 6 months, with a mortality rate of 12.5%. The sphericity index was significantly correlated with delayed swelling and hematoma expansion but not salvage DC.

Conclusions

The sphericity index is a promising predictor of delayed swelling and hematoma expansion that may aid in the development of surgical guidelines and medication strategies. Further large-scale studies are required to explore these aspects and establish comprehensive guidelines.

Using Radiomics and Convolutional Neural Networks for the Prediction of Hematoma Expansion After Intracerebral Hemorrhage

Background

Hematoma enlargement (HE) is a common complication following acute intracerebral hemorrhage (ICH) and is associated with early deterioration and unfavorable clinical outcomes. This study aimed to evaluate the predictive performance of a computed tomography (CT) based model that utilizes deep learning features in identifying HE.

Methods

A total of 408 patients were retrospectively enrolled between January 2015 and December 2020 from our institution. We designed an automatic model that could mask the hematoma area and fusion features of radiomics, clinical data, and convolutional neural network (CNN) in a hybrid model. We assessed the model's performance by using confusion matrix metrics (CM), the area under the receiver operating characteristics curve (AUC), and other statistical indicators.

Results

After automated masking, 408 patients were randomly divided into two cohorts with 204 patients in the training set and 204 patients in the validation set. The first cohort trained the CNN model, from which we then extracted radiomics, clinical data, and CNN features for the second validation cohort. After feature selection by K-highest score, a support vector machines (SVM) model classification was used to predict HE. Our hybrid model exhibited a high AUC of 0.949, and 0.95 of precision, 0.83 of recall, and 0.94 of average precision (AP). The CM found that only 5 cases were misidentified by the model.

Conclusion

The automatic hybrid model we developed is an end-to-end method and can assist in clinical decision-making, thereby facilitating personalized treatment for patients with ICH.

Clinical characteristics of spontaneous intracranial basal ganglia hemorrhage and risk factors for hematoma expansion in the plateaus of China

Background and purpose

The clinical features of intracranial cerebral hemorrhage (ICH) and the risk factors for hematoma expansion (HE) have been extensively studied. However, few studies have been performed in patients who live on a plateau. The natural habituation and genetic adaptation have resulted in differences in disease characteristics. The purpose of this study was to investigate the differences and consistency of clinical and imaging characteristics of patients in the plateaus of China compared with the plains, and to analyze the risk factors for HE of intracranial hemorrhage in the plateau patients.

Methods

From January 2020 to August 2022, we undertook a retrospective analysis of 479 patients with first-episode spontaneous intracranial basal ganglia hemorrhage in Tianjin and Xining City. The clinical and radiologic data during hospitalization were analyzed. Univariate and multivariate logistic regression analyzes were used to assess the risk factors for HE.

Results

HE occurred in 31 plateau (36.0%) and 53 plain (24.2%) ICH patients, and HE was more likely to occur in the plateau patients compared with the plain (p = 0.037). The NCCT images of plateau patients also showed heterogeneity of hematoma imaging signs, and the incidence of blend signs (23.3% vs. 11.0%, p = 0.043) and black hole signs (24.4% vs. 13.2%, p = 0.018) was significantly higher than in the plain. Baseline hematoma volume, black hole sign, island sign, blend sign, and PLT and HB level were associated with HE in the plateau. Baseline hematoma volume and the heterogeneity of hematoma imaging signs were independent predictors of HE in both the plain and plateau.

Conclusion

Compared with the plain, ICH patients in the plateau were more prone to HE. The patients showed the same heterogeneous signs on the NCCT images as in the plain, and also had predictive value for HE.

Comparison of different noncontrast computed tomographic markers for predicting early perihematomal edema expansion in patients with intracerebral hemorrhage

OBJECTIVES

Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion.

METHODS

ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan within 36 h between July 2011 and March 2017 were included in this study. The predictive value of hypodensity, satellite sign, heterogeneous density, irregular shape, blend sign, black hole sign, island sign and expansion-prone hematoma for early perihematomal edema expansion were assessed, separately.

RESULTS

214 patients were included in our final analysis. After adjusting for ICH characteristics, hypodensity, blend sign, island sign and expansion-prone hematoma are still predictors of early perihematomal edema expansion in multivariable logistics regression analysis (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of expansion-prone hematoma was significantly larger than the area under the ROC curve of hypodensity, blend sign and island sign in predicting PHE expansion (P = 0.003, P < 0.001 and P = 0.002, respectively).

CONCLUSION

Compared with single NCCT imaging markers, expansion-prone hematoma seems to be optimal predictor for early PHE expansion than any single NCCT imaging marker.

Research advances in imaging markers for predicting hematoma expansion in intracerebral hemorrhage: a narrative review

Introduction

Stroke is a major global health concern and is ranked as the second leading cause of death worldwide, with the third highest incidence of disability. Intracerebral hemorrhage (ICH) is a devastating form of stroke that is responsible for a significant proportion of stroke-related morbidity and mortality worldwide. Hematoma expansion (HE), which occurs in up to one-third of ICH patients, is a strong predictor of poor prognosis and can be potentially preventable if high-risk patients are identified early. In this review, we provide a comprehensive summary of previous research in this area and highlight the potential use of imaging markers for future research studies.

Recent advances

Imaging markers have been developed in recent years to aid in the early detection of HE and guide clinical decision-making. These markers have been found to be effective in predicting HE in ICH patients and include specific manifestations on Computed Tomography (CT) and CT Angiography (CTA), such as the spot sign, leakage sign, spot-tail sign, island sign, satellite sign, iodine sign, blend sign, swirl sign, black hole sign, and hypodensities. The use of imaging markers holds great promise for improving the management and outcomes of ICH patients.

Conclusion

The management of ICH presents a significant challenge, and identifying high-risk patients for HE is crucial to improving outcomes. The use of imaging markers for HE prediction can aid in the rapid identification of such patients and may serve as potential targets for anti-HE therapies in the acute phase of ICH. Therefore, further research is needed to establish the reliability and validity of these markers in identifying high-risk patients and guiding appropriate treatment decisions.

Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study

BACKGROUND

Hematoma expansion (HE) is a significant predictor of poor outcomes in patients with intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) markers in ICH are promising predictors of HE. We aimed to determine the association of the NCCT markers with HE by using different temporal HE definitions.

METHODS

We utilized Risa-MIS-ICH trial data (risk stratification and minimally invasive surgery in acute intracerebral hemorrhage). We defined four HE types based on the time to baseline CT (BCT) and the time to follow-up CT (FCT). Hematoma volume was measured by software with a semi-automatic edge detection tool. HE was defined as a follow-up CT hematoma volume increase of >6 mL or a 33% hematoma volume increase relative to the baseline CT. Multivariable regression analyses were used to determine the HE parameters. The prediction potential of indicators for HE was evaluated using receiver-operating characteristic analysis.

RESULTS

The study enrolled 158 patients in total. The time to baseline CT was independently associated with HE in one type (odds ratio (OR) 0.234, 95% confidence interval (CI) 0.077-0.712, p = 0.011), and the blend sign was independently associated with HE in two types (OR, 6.203-6.985, both p < 0.05). Heterogeneous density was independently associated with HE in all types (OR, 6.465-88.445, all p < 0.05) and was the optimal type for prediction, with an area under the curve of 0.674 (p = 0.004), a sensitivity of 38.9%, and specificity of 96.0%.

CONCLUSION

In specific subtypes, the time to baseline CT, blend sign, and heterogeneous density were independently associated with HE. The association between NCCT markers and HE is influenced by the temporal definition of HE. Heterogeneous density is a stable and robust predictor of HE in different subtypes of hematoma expansion.

Potential of Satellite Sign for Prediction of Hematoma Expansion in Small Spontaneous Hematoma within 7 Days' Follow-Up

Background Hematoma expansion (HE) is the most important modifiable predictor that can change the clinical outcome of intracerebral hemorrhage (ICH) patients. The study aimed to investigate the potential of satellite sign for prediction of HE in spontaneous ICH patients who had follow-up non-contrast computed tomography (NCCT) within 7 days after the initial CT scan.

Methods We retrospectively reviewed data and NCCT from 142 ICH patients who were treated at our hospital at Bangkok, Thailand. All included patients were treated conservatively, had baseline NCCT within 12 hours after symptom onset, and had follow-up NCCT within 168 hours after baseline NCCT. HE was initially estimated by two radiologists, and then by image analysis software. Association between satellite sign and HE was evaluated.

Results HE occurred in 45 patients (31.7%). Patients with HE had significantly higher activated partial thromboplastin time (p = 0.001) and baseline hematoma volume (p = 0.001). The prevalence of satellite sign was 43.7%, and it was significantly independently associated with HE (p = 0.021). The sensitivity, specificity, and accuracy of satellite sign for predicting HE was 57.8, 62.9, and 61.3%, respectively. From image analysis software, the cutoff of greater than 9% relative growth in hematoma volume on follow-up NCCT had the highest association with satellite sign (p = 0.024), with a sensitivity of 55%, specificity of 64.6%, and accuracy of 60.5%.

Conclusion Satellite sign, a new NCCT predictor, was found to be significantly associated with HE in Thai population. With different context of Thai population, HE was found in smaller baseline hematoma volume. Satellite sign was found more common in lobar hematoma. Further studies to validate satellite sign for predicting HE and to identify an optimal cutoff in Thai population that is correlated with clinical outcomes are warranted.

Outcome Prediction of Spontaneous Supratentorial Intracerebral Hemorrhage after Surgical Treatment Based on Non-Contrast Computed Tomography: A Multicenter Study

This study aims to explore the value of a machine learning (ML) model based on radiomics features and clinical features in predicting the outcome of spontaneous supratentorial intracerebral hemorrhage (sICH) 90 days after surgery. A total of 348 patients with sICH underwent craniotomy evacuation of hematoma from three medical centers. One hundred and eight radiomics features were extracted from sICH lesions on baseline CT. Radiomics features were screened using 12 feature selection algorithms. Clinical features included age, gender, admission Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), midline shift (MLS), and deep ICH. Nine ML models were constructed based on clinical feature, and clinical features + radiomics features, respectively. Grid search was performed on different combinations of feature selection and ML model for parameter tuning. The averaged receiver operating characteristics (ROC) area under curve (AUC) was calculated and the model with the largest AUC was selected. It was then tested using multicenter data. The combination of lasso regression feature selection and logistic regression model based on clinical features + radiomics features had the best performance (AUC: 0.87). The best model predicted an AUC of 0.85 (95%CI, 0.75-0.94) on the internal test set and 0.81 (95%CI, 0.64-0.99) and 0.83 (95%CI, 0.68-0.97) on the two external test sets, respectively. Twenty-two radiomics features were selected by lasso regression. The second-order feature gray level non-uniformity normalized was the most important radiomics feature. Age is the feature with the greatest contribution to prediction. The combination of clinical features and radiomics features using logistic regression models can improve the outcome prediction of patients with sICH 90 days after surgery.

Prior knowledge-based precise diagnosis of blend sign from head computed tomography

Introduction

Automated diagnosis of intracranial hemorrhage on head computed tomography (CT) plays a decisive role in clinical management. This paper presents a prior knowledge-based precise diagnosis of blend sign network from head CT scans.

Method

We employ the object detection task as an auxiliary task in addition to the classification task, which could incorporate the hemorrhage location as prior knowledge into the detection framework. The auxiliary task could help the model pay more attention to the regions with hemorrhage, which is beneficial for distinguishing the blend sign. Furthermore, we propose a self-knowledge distillation strategy to deal with inaccuracy annotations.

Results

In the experiment, we retrospectively collected 1749 anonymous non-contrast head CT scans from the First Affiliated Hospital of China Medical University. The dataset contains three categories: no intracranial hemorrhage (non-ICH), normal intracranial hemorrhage (normal ICH), and blend sign. The experimental results demonstrate that our method performs better than other methods.

Discussion

Our method has the potential to assist less-experienced head CT interpreters, reduce radiologists' workload, and improve efficiency in natural clinical settings.

Predicting Hemorrhage Progression in Deep Intracerebral Hemorrhage: A Multicenter Retrospective Cohort Study

BACKGROUND

Hemorrhage progression in deep intracerebral hemorrhage (ICH) involves not only the growth of parenchymal hematoma but also an increase in intraventricular hemorrhage (IVH). The search for methods that predict both the increased risk of parenchymal hematoma and IVH growth is warranted.

METHODS

We conducted a retrospective cohort study at multiple centers. Participants with deep ICH were enrolled from January 2018 to December 2021. Prediction models based on logistic regression analysis included clinical as well as routine radiographic and radiomics variables, separately or in combination. The performance of each model was evaluated using discrimination measures (e.g., area under the curve [AUC]). Evaluation of clinical utility was performed using decision curve analysis (DCA).

RESULTS

Overall, 647 individuals across 4 stroke centers were included. A total of 429 (66%) patients from 3 centers were assigned to the primary cohort and 218 (34%) from another center were placed in the validation cohort. Multivariate analysis showed that the Glasgow Coma Scale score, baseline ICH volume, IVH, blend sign, and radiomics score were associated with hemorrhage progression in the primary cohort. The clinical-radiomics model (AUC = 0.852 and 0.835) improved the prediction performance of hemorrhage progression compared to the Noncontrast computed tomography signs model (AUC = 0.666 and 0.618) in both the primary and validation cohorts, with similar results in the decision curve analysis curves.

CONCLUSIONS

The clinical-radiomics model outperformed the routine Noncontrast computed tomography signs model in predicting the progression of deep ICH. The clinical benefit of screening patients using this model may assist in risk stratification.

The "SALPARE study" of spontaneous intracerebral haemorrhage-part 2-early CT predictors of outcome in ICH: keeping it simple

BACKGROUND

The aim of this study was to investigate the prognostic role of hematoma characteristics and hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

This multicenter prospective cohort study enrolled consecutive adult patients with non-traumatic ICH admitted to three Italian academic hospitals (Salerno, Padova, Reggio Emilia) over a 2-year period. Early noncontrast CT (NCCT) features of the hematoma, including markers of HE, and 3-month outcome were recorded. Multivariable logistic regression analysis was performed to identify predictors of poor outcome.

RESULTS

A total of 682 patients were included in the study [mean age: 73 ± 14 years; 316 (46.3%) females]. Pontine and massive hemorrhage, intraventricular bleeding, baseline hematoma volume > 15 mL, blend sign, swirl sign, margin irregularity ≥ 4, density heterogeneity ≥ 3, hypodensity ≥ 1, island sign, satellite sign, and black hole sign were associated with a higher risk of mortality and disability. However, at multivariate analysis only initial hematoma volume (OR 29.71) proved to be an independent predictor of poor functional outcome at 3 months.

CONCLUSION

Simple hematoma volume measured on baseline CT best identifies patients with a worse outcome, while early NCCT markers of HE do not seem to add any clinically significant information.

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

Background

This meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage.

Methods

Throughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA).

Results

Ten articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers.

Conclusions

The NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].

The comprehensive comparison of imaging sign from CT angiography and noncontrast CT for predicting intracranial hemorrhage expansion: A comparative study

Expansion of intracranial hemorrhage (ICH) is an important predictor of poor clinical outcomes. Various imaging markers on non-contrast computed tomography (NCCT) or computed tomographic angiography (CTA) have been reported as predictors of ICH expansion. We aimed to compare the associations between various CT imaging markers and ICH expansion. Patients with spontaneous ICH who underwent initial NCCT, CTA, and subsequent NCCT between January 2016 and December 2019 were retrospectively identified. ICH expansion was defined as a volume increase of > 33% or > 6 mL. We analyzed the presence of imaging markers such as the black hole sign, blend sign, island sign, or swirl sign on initial NCCT or spot sign on CTA. An alternative free-response receiver operating characteristic curve analysis was performed using a 4-point scoring system based on the consensus of the reviewers. The predictive value of each marker was assessed using univariate and multivariate logistic regression analyses. A total of 250 patients, including 60 (24.0%) with ICH expansion, qualified for the analysis. Among the patients with spontaneous ICH, 118 (47.2%) presented with a black hole sign, 52 (20.8%) with a blend sign, 93 (37.2%) with an island sign, 79 (31.6%) with a swirl sign, and 56 (22.4%) with a spot sign. In univariate logistic regression, the initial ICH volume (P = .038), initial intraventricular hemorrhage (IVH) presence (P < .001), swirl sign (P < .001), and spot sign (P < .001) were associated with ICH expansion. Multivariate analysis confirmed that the presence of initial IVH (odds ratio, 4.111; P = .002) and spot sign (odds ratio, 109.5; P < .001) were independent predictors of ICH expansion. Initial ICH volume, IVH, swirl sign, and spot sign are associated with ICH expansion. The presence of spot signs and IVH were independent predictors of ICH expansion.

Diagnostic Accuracy and Reliability of Noncontrast Computed Tomography Markers for Acute Hematoma Expansion among Radiologists

BACKGROUND

Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience.

METHODS

Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters.

RESULTS

Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p-values < 0.05.

CONCLUSION

The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.

A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage

Purpose. To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis. Methods. A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH. Results. A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice. Conclusions. The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.

3D slicer-based calculation of hematoma irregularity index for predicting hematoma expansion in intracerebral hemorrhage

BACKGROUND

Irregular hematoma is considered as a risk sign of hematoma expansion. The aim of this study was to quantify hematoma irregularity with computed tomography based on 3D Slicer.

METHODS

Patients with spontaneous intracerebral hemorrhage who underwent an initial and subsequent non-contrast computed tomography (CT) at a single medical center between January 2019 to January 2020 were retrospectively identified. The Digital Imaging and Communication in Medicine (DICOM) standard images were loaded into the 3D Slicer, and the surface area (S) and volume (V) of hematoma were calculated. The hematoma irregularity index (HII) was defined as [Formula: see text]. Logistic regression analyses and receiver operating characteristic (ROC) curve analysis were performed to assess predictive performance of HII.

RESULTS

The enrolled patients were divided into those with hematoma enlargement (n = 36) and those without the enlargement (n = 57). HII in hematoma expansion group was 130.4 (125.1-140.0), and the index in non-enlarged hematoma group was 118.6 (113.5-122.3). There was significant difference in HII between the two groups (P < 0.01). Multivariate logistic regression analysis revealed that the HII was significantly associated with hematoma expansion before (odds ratio = 1.203, 95% confidence interval [CI], 1.115-1.298; P < 0.001) and after adjustment for age, hematoma volume, Glasgow Coma Scale score (odds ratio = 1.196, 95% CI, 1.102-1.298, P < 0.001). The area under the ROC curve was 0.86 (CI, 0.78-0.93, P < 0.01), and the best cutoff of HII for predicting hematoma growth was 123.8.

CONCLUSION

As a quantitative indicator of irregular hematoma, HII can be calculated using the 3D Slicer. And the HII was independently correlated with hematoma expansion.

Computed tomography in acute intracerebral hemorrhage: neuroimaging predictors of hematoma expansion and outcome

Intracerebral hemorrhage (ICH) accounts for 10–20% of all strokes worldwide and is associated with serious outcomes, including a 30-day mortality rate of up to 40%. Neuroimaging is pivotal in diagnosing ICH as early detection and determination of underlying cause, and risk for expansion/rebleeding is essential in providing the correct treatment. Non-contrast computed tomography (NCCT) is the most used modality for detection of ICH, identification of prognostic markers and measurements of hematoma volume, all of which are of major importance to predict outcome. The strongest predictors of 30-day mortality and functional outcome for ICH patients are baseline hematoma volume and hematoma expansion. Even so, exact hematoma measurement is rare in clinical routine practice, primarily due to a lack of tools available for fast, effective, and reliable volumetric tools. In this educational review, we discuss neuroimaging findings for ICH from NCCT images, and their prognostic value, as well as the use of semi-automatic and fully automated hematoma volumetric methods and assessment of hematoma expansion in prognostic studies.

The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage

Background: Among patients undergoing head computed tomography (CT) scans within 3 h of spontaneous intracerebral hemorrhage (sICH), 28% to 38% have hematoma expansion (HE) on follow-up CT. This study aimed to predict HE using radiomics analysis and investigate the impact of intraventricular hemorrhage (IVH) compared with the conventional approach based on intraparenchymal hemorrhage (IPH) alone. Methods: This retrospective study enrolled 127 patients with baseline and follow-up non-contrast CT (NCCT) within 4~72 h of sICH. IPH and IVH were outlined separately for performing radiomics analysis. HE was defined as an absolute hematoma growth > 6 mL or percentage growth > 33% of either IPH (HEP) or a combination of IPH and IVH (HEP+V) at follow-up. Radiomic features were extracted using PyRadiomics, and then the support vector machine (SVM) was used to build the classification model. For each case, a radiomics score was generated to indicate the probability of HE. Results: There were 57 (44.9%) HEP and 70 (55.1%) non-HEP based on IPH alone, and 58 (45.7%) HEP+V and 69 (54.3%) non-HEP+V based on IPH + IVH. The majority (>94%) of HE patients had poor early outcomes (death or modified Rankin Scale > 3 at discharge). The radiomics model built using baseline IPH to predict HEP (RMP) showed 76.4% accuracy and 0.73 area under the ROC curve (AUC). The other model using IPH + IVH to predict HEP+V (RMP+V) had higher accuracy (81.9%) with AUC = 0.80, and this model could predict poor outcomes. The sensitivity/specificity of RMP and RMP+V for HE prediction were 71.9%/80.0% and 79.3%/84.1%, respectively. Conclusion: The proposed radiomics approach with additional IVH information can improve the accuracy in prediction of HE, which is associated with poor clinical outcomes. A reliable radiomics model may provide a robust tool to help manage ICH patients and to enroll high-risk ICH cases into anti-expansion or neuroprotection drug trials.

Predicting prognosis of primary pontine hemorrhage using CT image and deep learning

Prognosis of primary pontine hemorrhage (PPH) is important for treatment planning and patient management. However, only few clinical factors were reported to have prognostic value to PPH. Here, we propose a deep learning (DL) model that mines high-dimensional prognostic information from computed tomography (CT) images and combines clinical factors for predicting individualized prognosis of PPH. We proposed a multi-task DL model to learn high-dimensional CT features of hematoma and perihematomal areas for predicting the risk of 30-day mortality, 90-day mortality and 90-day functional outcome of PPH simultaneously. We further explored the combination of the DL model and clinical factors by building a combined model. All the models were trained in a training cohort (n = 219) and tested in an independent testing cohort (n = 35). The DL model achieved area under the curve (AUC) of 0.886, 0.886, and 0.759 in predicting 30-day mortality, 90-day mortality and 90-day functional outcome of PPH in the independent testing cohort, which improved over the previously reported new PPH score and the clinical model. When combining the DL model and clinical factors, the combined model achieved improved performance (AUC = 0.920, 0.941, and 0.894), indicating that DL model mines CT information that complements clinical factors. Through DL visualization technique, we found that the internal structure of hematoma and its expansion to perihematomal regions are important for predicting the prognosis of PPH. This DL model provides an easy-to-use way for predicting individualized prognosis of PPH by mining high-dimensional information from CT images, and showed improvement over clinical factors and present methods.

Intracerebral hemorrhage markers on non-contrast computed tomography as predictors of the dynamic spot sign on CT perfusion and associations with hematoma expansion and outcome

PURPOSE

To assess the association between non-contrast computed tomography (NCCT) hematoma markers and the dynamic spot sign on computed tomography perfusion (CTP), and their associations with hematoma expansion, clinical outcome, and in-hospital mortality.

METHODS

Patients who presented with intracerebral hemorrhage (ICH) to a stroke center over an 18-month period and underwent baseline NCCT and CTP, and a follow-up NCCT within 24 h after the baseline scan were included. The initial and follow-up hematoma volumes were calculated. Two raters independently assessed the baseline NCCT for hematoma markers and concurrently assessed the CTP for the dynamic spot sign. Univariate and multivariate logistic regression analyses were performed to assess the association between the hematoma markers and the dynamic spot sign, adjusting for known ICH expansion predictors.

RESULTS

Eighty-five patients were included in our study and 55 patients were suitable for expansion analysis. Heterogeneous density was the only NCCT hematoma marker to be associated with the dynamic spot sign after multivariate analysis (odds ratio, 58.61; 95% confidence interval, 9.13-376.05; P < 0.001). The dynamic spot sign was present in 22 patients (26%) and significantly predicted hematoma expansion (odds ratio, 36.6; 95% confidence interval, 2.51-534.2; P = 0.008). All patients with a spot sign had a swirl sign. A co-located hypodensity and spot sign was significantly associated with in-hospital mortality (odds ratio, 6.17; 95% confidence interval, 1.09-34.78; P = 0.039).

CONCLUSION

Heterogeneous density and swirl sign are associated with the dynamic spot sign. The dynamic spot sign is a stronger predictor than NCCT hematoma markers of significant hematoma expansion. A co-located spot sign and hypodensity predicts in-hospital mortality.

Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage

Background

Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.

Methods

We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.

Results

A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.

Conclusion

The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study

BACKGROUND

Accurate risk stratification of patients with intracerebral hemorrhage (ICH) could help refine adjuvant therapy selection and better understand the clinical course. We aimed to evaluate the value of radiomics features from hematomal and perihematomal edema areas for prognosis prediction and to develop a model combining clinical and radiomic features for accurate outcome prediction of patients with ICH.

METHODS

This multicenter study enrolled patients with ICH from January 2016 to November 2021. Their outcomes at 3 months were recorded based on the modified Rankin Scale (good, 0-3; poor, 4-6). Independent clinical and radiomic risk factors for poor outcome were identified through multivariate logistic regression analysis, and predictive models were developed. Model performance and clinical utility were evaluated in both internal and external cohorts.

RESULTS

Among the 1098 ICH patients evaluated (mean age, 60 ± 13 years), 703 (64 %) had poor outcomes. Age, hemorrhage volume and location, and Glasgow Coma Scale (GCS) were independently associated with outcomes. The area under the receiver operating characteristic curve (AUC) of the clinical model was 0.881 in the external validation cohort. Addition of the Rad-score (combined hematoma and perihematomal edema area) improved predictive accuracy and model performance (AUC, 0.893), net reclassification improvement, 0.140 (P < 0.001), and integrated discrimination improvement, 0.050 (P < 0.001).

CONCLUSIONS

The radiomics features of hematomal and perihematomal edema area have additional value in prognostic prediction; moreover, addition of radiomic features significantly improves model accuracy.

A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage

Background and purpose

Hematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.

Materials and methods

We retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.

Results

The combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).

Conclusion

The NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks.

Association of the stress hyperglycemia ratio and clinical outcomes in patients with stroke: A systematic review and meta-analysis

Objective

Stress hyperglycemia (SH) is common in patients with acute diseases, such as stroke and myocardial infarction. Stress hyperglycemia ratio (SHR) is calculated by glucose/glycated hemoglobin and has been widely used for evaluating SH. But whether SHR is associated with clinical outcomes in stroke patients remains unclear so far. Although many studies have shown that higher SHR means poor outcomes, there is still no absolute evidence that SHR plays a critical role in stroke patients. Hence, we performed a systematic review and meta-analysis aiming to investigate the association between SHR and clinical outcomes in stroke patients.

Methods

We performed a comprehensive literature search of the PubMed, Embase, Cochrane Library databases, Clinicaltrials.gov, and WHO-ICTRP. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we performed our study. The Newcastle-Ottawa Scale (NOS) tool was used to examine the potential bias of included studies. The endpoints including poor outcome, mortality, neurological deficit, hemorrhagic transformation (HT), and infectious complications were statistically analyzed.

Results

Sixteen retrospective studies met the eligibility criteria, and a number of 183,588 patients were included. Our meta-analysis demonstrated a significant increase in the incidence of poor outcome, according to assessment by the modified Rankin Scale (mRS) ≥ 3 points [odds ratio (OR) 2.53, 95% confidence interval (CI) 1.99–3.22, P < 0.00001, I² = 68%], mortality (OR 1.96, 95% CI 1.58–2.44, P < 0.00001, I² = 61%), neurological deficit (OR 1.99, 95% CI 1.47–2.70, P < 0.00001, I² = 75%), hemorrhagic transformation (HT) (OR 3.70, 95% CI 2.69–5.08, P < 0.00001, I² = 0%), and infectious complications [(Pneumonia) OR 2.06, 95% CI 1.57–2.72, P < 0.00001, I² = 24%; (Urinary tract infection) OR 2.53, 95% CI 1.45–4.42, P = 0.001, I² = 57%] in stroke patients with higher SHR. However, no significant influence was observed for recanalization rate (OR 0.86, 95% CI 0.54–1.38, P = 0.53, I² = 0%).

Conclusion

With or without diabetes, no matter whether undergoing intravenous thrombolysis or mechanical thrombectomy, higher SHR significantly increased the occurrence of poor outcomes, mortality, neurological deficit, HT, and infectious complications. The recanalization rate was not statistically significant between the two groups. More attention must be paid in clinical practice to SH. Future investigation should focus on the diagnostic value of SHR and the early control of hyperglycemia. Meanwhile, whether SHR could become a novel and promising target for early intervention is worthy of attention in further research. Besides, the influence of the dynamic change of glucose-to-HbA1c ratio, namely SHR, on intracerebral hemorrhage outcomes requires further investigation in future research. Although no randomized double-blind studies have been conducted, the available massive sample studies reflect the actual situation in the clinic and assist clinical decision makers.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42022345587.

Non-contrast CT markers of intracerebral hematoma expansion: a reliability study

OBJECTIVES

We evaluated whether clinicians agree in the detection of non-contrast CT markers of intracerebral hemorrhage (ICH) expansion.

METHODS

From our local dataset, we randomly sampled 60 patients diagnosed with spontaneous ICH. Fifteen physicians and trainees (Stroke Neurology, Interventional and Diagnostic Neuroradiology) were trained to identify six density (Barras density, black hole, blend, hypodensity, fluid level, swirl) and three shape (Barras shape, island, satellite) expansion markers, using standardized definitions. Thirteen raters performed a second assessment. Inter- and intra-rater agreement were measured using Gwet's AC₁, with a coefficient > 0.60 indicating substantial to almost perfect agreement.

RESULTS

Almost perfect inter-rater agreement was observed for the swirl (0.85, 95% CI: 0.78-0.90) and fluid level (0.84, 95% CI: 0.76-0.90) markers, while the hypodensity (0.67, 95% CI: 0.56-0.76) and blend (0.62, 95% CI: 0.51-0.71) markers showed substantial agreement. Inter-rater agreement was otherwise moderate, and comparable between density and shape markers. Inter-rater agreement was lower for the three markers that require the rater to identify one specific axial slice (Barras density, Barras shape, island: 0.46, 95% CI: 0.40-0.52 versus others: 0.60, 95% CI: 0.56-0.63). Inter-observer agreement did not differ when stratified for raters' experience, hematoma location, volume, or anticoagulation status. Intra-rater agreement was substantial to almost perfect for all but the black hole marker.

CONCLUSION

In a large sample of raters with different backgrounds and expertise levels, only four of nine non-contrast CT markers of ICH expansion showed substantial to almost perfect inter-rater agreement.

KEY POINTS

• In a sample of 15 raters and 60 patients, only four of nine non-contrast CT markers of ICH expansion showed substantial to almost perfect inter-rater agreement (Gwet's AC₁> 0.60). • Intra-rater agreement was substantial to almost perfect for eight of nine hematoma expansion markers. • Only the blend, fluid level, and swirl markers achieved substantial to almost perfect agreement across all three measures of reliability (inter-rater agreement, intra-rater agreement, agreement with the results of a reference reading).

Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage

To examine whether machine learning (ML) approach can be used to predict hematoma expansion in acute intracerebral hemorrhage (ICH) with accuracy and widespread applicability, we applied ML algorithms to multicenter clinical data and CT findings on admission. Patients with acute ICH from three hospitals (n = 351) and those from another hospital (n = 71) were retrospectively assigned to the development and validation cohorts, respectively. To develop ML predictive models, the k-nearest neighbors (k-NN) algorithm, logistic regression, support vector machines (SVMs), random forests, and XGBoost were applied to the patient data in the development cohort. The models were evaluated for their performance on the patient data in the validation cohort, which was compared with previous scoring methods, the BAT, BRAIN, and 9-point scores. The k-NN algorithm achieved the highest area under the receiver operating characteristic curve (AUC) of 0.790 among all ML models, and the sensitivity, specificity, and accuracy were 0.846, 0.733, and 0.775, respectively. The BRAIN score achieved the highest AUC of 0.676 among all previous scoring methods, which was lower than the k-NN algorithm (p = 0.016). We developed and validated ML predictive models of hematoma expansion in acute ICH. The models demonstrated good predictive ability, showing better performance than the previous scoring methods.

Various effects of nutritional status on clinical outcomes after intracerebral hemorrhage

Although the assessment of nutrition is essential for stroke patients, detailed associations between nutritional status at admission, subsequent complications, and clinical outcomes in patients with acute intracerebral hemorrhage (ICH) are unclear. We aimed to elucidate these associations using the Controlling Nutritional Status (CONUT) score. Consecutive patients with acute ICH were investigated. Nutritional status was evaluated using the CONUT score, calculated from the serum albumin level, lymphocyte count, and total cholesterol level. Subsequent complications, such as hemorrhage expansion (HE) during the acute stage and aspiration pneumonia during hospitalization, were evaluated. Poor outcome was defined as a modified Rankin Scale score of ≥ 3 at 3 months. Of the 721 patients, 49 had HE, 111 had aspiration pneumonia, and 409 had poor outcomes. Patients with HE had significantly lower total cholesterol levels than those without HE. Patients with aspiration pneumonia had significantly lower albumin levels, lower lymphocyte counts, and higher CONUT scores than those without aspiration pneumonia. Patients with poor outcomes had significantly lower albumin levels, lower lymphocyte counts, lower total cholesterol levels, and higher CONUT scores than those with good outcomes. Multivariable logistic analysis showed that higher CONUT scores were independently associated with poor outcome (odds ratio, 1.28; 95% confidence interval, 1.09-1.49; P = 0.002) after adjusting for baseline characteristics, HE, and aspiration pneumonia. Each component of CONUT was a useful predictor of subsequent complications. Malnutrition, determined using the CONUT score, was independently associated with poor outcomes in patients with ICH after adjusting for these complications.

Higher Cerebral Small Vessel Disease Burden in Patients With Small Intracerebral Hemorrhage

Objective

To investigate the association between cerebral small vessel disease (SVD) and hematoma volume in primary intracerebral hemorrhage (ICH).

Methods

Patients from a prospective ICH cohort were enrolled. Admission and follow-up CT scan within 72 h after onset were reviewed to calculate the final hematoma volume. We evaluated cortical superficial siderosis and the global SVD score, including white matter hyperintensities, lacunes, enlarged perivascular space, and cerebral microbleeds on MRI. We conducted the multivariate logistic regression analyses to explore the association between SVD markers and small ICH, as well as hematoma volume. Hematoma location was stratified into lobar and non-lobar for subgroup analysis.

Results

A total of 187 patients with primary ICH (mean age 62.4 ± 13.4 years, 67.9% male) were enrolled. 94 (50.2%) patients had small ICH. The multivariate logistic regression analysis showed an association between global SVD score and small ICH [adjusted odds ratio (aOR) 1.27, 95% CI 1.03–1.57, p = 0.027] and a trend of higher global SVD score towards non-lobar small ICH (aOR 1.23, 95% CI 0.95–1.58, p = 0.122). In the multivariate linear regression analysis, global SVD score was inversely related to hematoma volume of all ICH (β = −0.084, 95% CI −0.142 to −0.025, p = 0.005) and non-lobar ICH (β = −0.112, 95% CI −0.186 to −0.037, p = 0.004). Lacune (β = −0.245, 95% CI −0.487 to −0.004, p = 0.046) was associated with lower non-lobar ICH volume.

Conclusion

Global SVD score is associated with small ICH and inversely correlated with hematoma volume. This finding predominantly exists in non-lobar ICH.

Automatic and Efficient Prediction of Hematoma Expansion in Patients with Hypertensive Intracerebral Hemorrhage Using Deep Learning Based on CT Images

Patients with hypertensive intracerebral hemorrhage (ICH) have a high hematoma expansion (HE) incidence. Noninvasive prediction HE helps doctors take effective measures to prevent accidents. This study retrospectively analyzed 253 cases of hypertensive intraparenchymal hematoma. Baseline non-contrast-enhanced CT scans (NECTs) were collected at admission and compared with subsequent CTs to determine the presence of HE. An end-to-end deep learning method based on CT was proposed to automatically segment the hematoma region, region of interest (ROI) feature extraction, and HE prediction. A variety of algorithms were employed for comparison. U-Net with attention performs best in the task of segmenting hematomas, with the mean Intersection overUnion (mIoU) of 0.9025. ResNet-34 achieves the most robust generalization capability in HE prediction, with an area under the receiver operating characteristic curve (AUC) of 0.9267, an accuracy of 0.8827, and an F1 score of 0.8644. The proposed method is superior to other mainstream models, which will facilitate accurate, efficient, and automated HE prediction.

The Attenuation Value Within the Non-hypodense Region on Non-contrast Computed Tomography of Spontaneous Cerebral Hemorrhage: A Long-Neglected Predictor of Hematoma Expansion

Background and Purpose

The ability of attenuation value of the non-hypodense region of hematoma in non-contrast computed tomography (NCCT) for predicting hematoma expansion (HE) remains unclear. Our purpose is to explore this relationship.

Methods

Two cohorts of patients were collected for analysis. The region where we measured hematoma attenuation values was limited to the non-hypodense region that was not adjacent to the normal brain tissue on NCCT. The critical attenuation value was derived via receiver operating characteristic (ROC) curve analysis in the derivation cohort and its predictive ability was validated in the validation cohort. Independent relationships between predictors, such as critical attenuation value of the non-hypodense region and HE were analyzed using the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis.

Results

The results showed that the attenuation value <64 Hounsfield units (HU) was independently associated with HE [odds ratio (OR), 4.118; 95% confidential interval (CI), 1.897–9.129, p < 0.001] and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) for predicting HE were 36.11%, 81.71%, 1.97, 0.78, 44.8%, 75.7%, and 0.589, respectively.

Conclusions

Our research explored and validated the relationship between the attenuation value of the non-hypodense region of hematoma and HE. The attenuation value < 64 HU was an appropriate indicator of early HE.

Hemorrhage Enlargement Is More Frequent in the First 2 Hours: A Prehospital Mobile Stroke Unit Study

Background:

Hematoma enlargement (HE) after intracerebral hemorrhage (ICH) is a therapeutic target for improving outcomes. Hemostatic therapies to prevent HE may be more effective the earlier they are attempted. An understanding of HE in first 1 to 2 hours specifically in the prehospital setting would help guide future treatment interventions in this time frame and setting.

Methods:

Patients with spontaneous ICH within 4 hours of symptom onset were prospectively evaluated between May 2014 and April 2020 as a prespecified substudy within a multicenter trial of prehospital mobile stroke unit versus standard management. Baseline computed tomography scans obtained <1, 1 to 2, and 2 to 4 hours postsymptom onset on the mobile stroke unit in the prehospital setting were compared with computed tomography scans repeated 1 hour later and at 24 hours in the hospital. HE was defined as >6 mL if baseline ICH volume was < 20 mL and 33% increase if baseline volume >20 mL. The association between time from symptom onset to baseline computed tomography (hours) and HE was investigated using Wilcoxon rank-sum test when time was treated as a continuous variable and using Fisher exact test when time was categorized. Kruskal-Wallis and Wilcoxon rank-sum tests evaluated differences in baseline volumes and HE. Univariable and multivariable logistic regression analyses were conducted to identify factors associated with HE and variable selection was performed using cross-validated L1-regularized (Lasso regression). This study adhered to STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology) for cohort studies.

Results:

One hundred thirty-nine patients were included. There was no difference between baseline ICH volumes obtained <1 hour (n=43) versus 1 to 2 hour (n=51) versus >2 hours (n=45) from symptom onset (median [interquartile range], 13 mL [6–24] versus 14 mL [6–30] versus 12 mL [4–19]; P =0.65). However, within the same 3 time epochs, initial hematoma growth (volume/time from onset) was greater with earlier baseline scanning (median [interquartile range], 17 mL/hour [9–35] versus 9 mL/hour [5–23]) versus 4 mL/hour [2–7]; P <0.001). Forty-nine patients had repeat scans 1 hour after baseline imaging (median, 2.3 hours [interquartile range. 1.9–3.1] after symptom onset). Eight patients (16%) had HE during that 1-hour interval; all of these occurred in patients with baseline imaging within 2 hours of onset (5/18=28% with baseline imaging within 1 hour, 3/18=17% within 1–2 hour, 0/13=0% >2 hours; P =0.02). HE did not occur between the scans repeated at 1 hour and 24 hours. No association between baseline variables and HE was detected in multivariable analyses.

Conclusions:

HE in the next hour occurs in 28% of ICH patients with baseline imaging within the first hour after symptom onset, and in 17% of those with baseline imaging between 1 and 2 hours. These patients would be a target for ultraearly hemostatic intervention.

External validation study on the value of deep learning algorithm for the prediction of hematoma expansion from noncontrast CT scans

Background

Hematoma expansion is an independent predictor of patient outcome and mortality. The early diagnosis of hematoma expansion is crucial for selecting clinical treatment options. This study aims to explore the value of a deep learning algorithm for the prediction of hematoma expansion from non-contrast computed tomography (NCCT) scan through external validation.

Methods

102 NCCT images of hypertensive intracerebral hemorrhage (HICH) patients diagnosed in our hospital were retrospectively reviewed. The initial computed tomography (CT) scan images were evaluated by a commercial Artificial Intelligence (AI) software using deep learning algorithm and radiologists respectively to predict hematoma expansion and the corresponding sensitivity, specificity and accuracy of the two groups were calculated and compared. Comparisons were also conducted among gold standard hematoma expansion diagnosis time, AI software diagnosis time and doctors’ reading time.

Results

Among 102 HICH patients, the sensitivity, specificity, and accuracy of hematoma expansion prediction in the AI group were higher than those in the doctor group(80.0% vs 66.7%, 73.6% vs 58.3%, 75.5% vs 60.8%), with statistically significant difference (p < 0.05). The AI diagnosis time (2.8 ± 0.3 s) and the doctors’ diagnosis time (11.7 ± 0.3 s) were both significantly shorter than the gold standard diagnosis time (14.5 ± 8.8 h) (p < 0.05), AI diagnosis time was significantly shorter than that of doctors (p < 0.05).

Conclusions

Deep learning algorithm could effectively predict hematoma expansion at an early stage from the initial CT scan images of HICH patients after onset with high sensitivity and specificity and greatly shortened diagnosis time, which provides a new, accurate, easy-to-use and fast method for the early prediction of hematoma expansion.

Hematoma expansion caused by trapped cerebrospinal fluid in subacute phase intracerebral hemorrhage: A case report

Background:

Although hematoma expansion (HE) is caused by active bleeding in patients with intracranial hemorrhage in most cases, cerebrospinal fluid (CSF) trapped in the hematoma cavity is not a well-known cause of HE.

Case Description:

We present a case of subcortical hemorrhage in an 80-year-old woman who experienced neurological deterioration in the subacute phase because of HE caused by CSF pooling in the hematoma cavity. The patient was transferred to our hospital from a previous hospital for surgical treatment because the consciousness disturbance was likely caused by the perihematomal edema that occurred 4 days after onset. Head computed tomography (CT) at admission to our hospital showed a blend sign, and a part of the low-density area of the hematoma was enlarged compared with the CT at admission to the previous hospital. Although the hematoma was located adjacent to the lateral ventricle, no intraventricular hemorrhage was observed. Emergent hematoma evacuation was performed, and intraoperative findings indicated that the enlarged hematoma cavity was caused by CSF pooling. The patient’s postoperative course was uneventful. She was transferred to a rehabilitation hospital 16 days after admission to our hospital.

Conclusion:

Hematomas adjacent to the ventricle and showing a blend sign can expand in the subacute phase because of the trapped CSF.