The predictive accuracy of the black hole sign and the spot sign for hematoma expansion in patients with spontaneous intracerebral hemorrhage

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis

PURPOSE

Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage.

METHODS

Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies.

RESULTS

A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts.

CONCLUSIONS

Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography

OBJECTIVES

Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion.

METHODS

A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis.

RESULTS

The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore.

CONCLUSION

The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma.

CLINICAL RELEVANCE STATEMENT

Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion.

KEY POINTS

• We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.

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.

Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study

Prediction of the hematoma expansion (HE) of spontaneous basal ganglia hematoma (SBH) from the first non-contrast CT can result in better management, which has the potential of improving outcomes. This study has been designed to compare the performance of "Radiomics analysis," "radiology signs," and "clinical-laboratory data" for this task. We retrospectively reviewed the electronic medical records for clinical, demographic, and laboratory data in patients with SBH. CT images were reviewed for the presence of radiologic signs, including black-hole, blend, swirl, satellite, and island signs. Radiomic features from the SBH on the first brain CT were extracted, and the most predictive features were selected. Different machine learning models were developed based on clinical, laboratory, and radiology signs and selected Radiomic features to predict hematoma expansion (HE). The dataset used for this analysis included 116 patients with SBH. Among different models and different thresholds to define hematoma expansion (10%, 20%, 25%, 33%, 40%, and 50% volume enlargement thresholds), the Random Forest based on 10 selected Radiomic features achieved the best performance (for 25% hematoma enlargement) with an area under the curve (AUC) of 0.9 on the training dataset and 0.89 on the test dataset. The models based on clinical-laboratory and radiology signs had low performance (AUCs about 0.5-0.6).

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images

Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.

Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage

PURPOSE

To develop a non-contrast computed tomography-(CT)-based radiomics score for predicting the risk of hematoma early enlargement in spontaneous intracerebral hemorrhage.

METHODS

A total of 258 patients from a single-center database with acute spontaneous intracerebral parenchymal hemorrhage were collected. Radiomics software was explored to segment hematomas on baseline non-contrast CT images, and the texture features were extracted. Minimal Redundancy and Maximal Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO), were used to select optimized subset of features and radiomics score was calculated. The radiomics model (radiomics score-based), radiomics nomogram (radiomics score combined with clinical factors-based) and clinical model (clinical factors-based) were built in a training cohort and validated in a test cohort. The discrimination, calibration, and clinical usefulness of the models were evaluated. Finally, a subgroup analysis was performed to assess the predictive value of radiomics score in specific hemorrhage location.

RESULTS

Radiomics score was composed of 12 radiomics features. The radiomics model and radiomics nomogram both showed good performance in predicting hematoma enlargement (area under the curve, AUC 0.83 [0.71-0.95], AUC 0.82 [0.72, 0.93]), and were both better than clinical model (AUC 0.66 [0.54-0.79]). The radiomics model and radiomics nomogram showed satisfactory calibration and clinical usefulness for detecting hematoma enlargement. For subgroup analysis, radiomics score also showed good predictive value for hematoma enlargement in different locations (AUC were 0.828, 0.940, 0.836 and 0.904, respectively, for supratentorial, subtentorial, deep and lobes).

CONCLUSION

A radiomics score based on non-contrast CT may be considered as a potential biomarker for prediction of hematoma enlargement in patients with spontaneous intracerebral hemorrhage (SICH), and it presented a high incremental value to clinical factors for hematoma enlargement prediction.

Antiplatelet Medications and Biomarkers of Hemostasis May Explain the Association of Hematoma Appearance and Subsequent Hematoma Expansion After Intracerebral Hemorrhage

BACKGROUND

To test the hypothesis that appearances of intracranial hematomas on diagnostic computed tomography (CT) are not idiosyncratic and reflect a biologically plausible mechanism, we evaluated the association between hematoma appearance on CT, biomarkers of platelet activity, and antiplatelet or anticoagulant medication use prior to admission.

METHODS

We studied 330 consecutively identified patients from 2006 to 2019. Biomarkers of platelet activity (platelet aspirin assay) and medication history (aspirin, clopidogrel) were prospectively recorded on admission. A blinded interpreter recorded the presence of hematoma appearances from the diagnostic scan. Associations were tested with parametric or nonparametric statistics, as appropriate.

RESULTS

The black hole sign (101, 30%) was most prevalent, followed by the island sign (57, 17%) and blend sign (32, 10%). There was reduced platelet activity in patients with a black hole sign (511 [430-610] vs. 562 [472-628] aspirin reaction units, P = 0.01) or island sign (505 [434-574] vs. 559 [462-629] aspirin reaction units, P = 0.004). Clopidogrel use prior to admission was associated with the black hole sign (odds ratio 2.25, 95% confidence interval 1.02-4.98, P = 0.04).

CONCLUSIONS

In patients with acute intracerebral hemorrhage, hematoma appearances on CT are associated with biomarkers of platelet activity and clopidogrel use prior to admission. Appearances of intracranial hematomas on CT may reflect reduced hemostasis from antiplatelet medication use.

A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features

OBJECTIVE

To predict early intracerebral haemorrhage expansion (HE) by comprehensive evaluation of commonly used noncontrast computed tomography (NCCT) features.

METHODS

Two hundred eighty-eight patients who had a spontaneous intracerebral haemorrhage (ICH) were included. All of the patients had undergone baseline NCCT within 6 h after ICH symptom onset. Ten NCCT features were extracted. Univariate analysis and multivariable logistic regression analysis were used to select the features. Using the finally selected features, a logistic regression model was built with a training cohort (n = 202) and subsequently validated in an independent test cohort (n = 86). Additionally, stratification analysis was performed in cases with and without anticoagulant therapy.

RESULTS

HE was found in 78 patients (27.1%). The blend sign and black hole sign were finally selected. The logistic regression model built with the two features exhibited accuracies of 76.7% and 75.6%, specificities of 98.6% and 98.4%, and positive predictive values (PPVs) of 83.3% and 75.0% for the training and test cohorts, respectively. The model also showed specificities of 100% and 98.5% and PPVs of 100% and 76.9% for the anticoagulant and non-anticoagulant drug use groups, respectively. These performances were better than those of each of the separate features.

CONCLUSIONS

By comprehensive evaluation, the model comprising the blend sign and black hole sign showed good performance for predicting early intracerebral haemorrhage expansion, particularly for high specificity and PPV, regardless of the anticoagulant status.

KEY POINTS

• Early identification of patients who are more likely to have haematoma expansion is important for therapeutic intervention. • Many radiological features have been reported to correlate with intracerebral haemorrhage expansion. • By integrating only the blend sign and black hole sign, the logistic regression model showed good performance for predicting early intracerebral haemorrhage expansion.

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

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

New Prediction Models of Functional Outcome in Acute Intracerebral Hemorrhage: The dICH Score and uICH Score

Objectives: The original intracerebral hemorrhage (oICH) score is the severity score most commonly used in clinical intracerebral hemorrhage (ICH) research but may be influenced by hematoma expansion or intraventricular hemorrhage (IVH) growth in acute ICH. Here, we aimed to develop new clinical scores to improve the prediction of functional outcomes in patients with ICH.

Methods: Patients admitted to the First Affiliated Hospital of Chongqing Medical University with primary ICH were prospectively enrolled in this study. Hematoma volume was measured using a semiautomated, computer-assisted technique. The dynamic ICH (dICH) score was developed by incorporating hematoma expansion and IVH growth into the oICH score. The ultra-early ICH (uICH) score was developed by adding the independent non-contrast CT markers to the oICH score. Receiver operating characteristic curve analysis was used to compare performance among the oICH score, dICH score, and uICH score.

Results: There were 310 patients in this study which included 72 patients (23.2%) with hematoma expansion and 58 patients (18.7%) with IVH growth. Of 31 patients with two or more non-contrast computed tomography markers, 61.3% died, and 96.8% had poor outcomes at 90 days. After adjustment for potential confounding variables, we found that age, baseline Glasgow Coma Scale score, presence of IVH on initial CT, baseline ICH volume, infratentorial hemorrhage, hematoma expansion, IVH growth, blend sign, black hole sign, and island sign could independently predict poor outcomes in multivariate analysis. In comparison with the oICH score, the dICH score and uICH score exhibited better performance in the prediction of poor functional outcomes.

Conclusions: The dICH score and uICH score were useful clinical assessment tools that could be used for risk stratification concerning functional outcomes and provide guidance in clinical decision-making in acute ICH.

Minimally Invasive Intracerebral Hemorrhage Evacuation Techniques: A Review

Intracerebral hemorrhage (ICH) continues to have high morbidity and mortality. Improving ICH outcomes likely requires rapid removal of blood from the parenchyma and restraining edema formation while also limiting further neuronal damage due to the surgical intervention. Minimally invasive surgery (MIS) approaches promise to provide these benefits and have become alluring options for management of ICH. This review describes six MIS techniques for ICH evacuation including craniopuncture, stereotactic aspiration with thrombolysis, endoport-mediated evacuation, endoscope-assisted evacuation, adjunctive aspiration devices, and the surgiscope. The efficacy of each modality is discussed based on current literature. The largest clinical trials have yet to demonstrate definitive effects of MIS intervention on mortality and functional outcomes for ICH. Thus, there is a significant need for further innovation for ICH treatment. Multiple ongoing trials promise to better clarify the potential of the newer, non-thrombolytic MIS techniques.

Computed Tomography Imaging Predictors of Intracerebral Hemorrhage Expansion

PURPOSE OF REVIEW

Hematoma expansion (HE) is strongly associated with poor clinical outcome and is a compelling target for improving outcome after intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) is widely used in clinical practice due to its faster acquisition at the presence of acute stroke. Recently, imaging markers on NCCT are increasingly used for predicting HE. We comprehensively review the current evidence on HE prediction using NCCT and provide a summary for assessment of these markers in future research studies.

RECENT FINDINGS

Predictors of HE on NCCT have been described in reports of several studies. The proposed markers, including swirl sign, blend sign, black hole sign, island sign, satellite sign, and subarachnoid extension, were all significantly associated with HE and poor outcome in their small sample studies after ICH. In summary, the optimal management of ICH remains a therapeutic dilemma. Therefore, using NCCT markers to select patients at high risk of HE is urgently needed. These markers may allow rapid identification and provide potential targets for anti-HE treatments in patients with acute ICH.

Is the detectability of the spot sign on CT angiography depending on slice thickness and reconstruction type?

OBJECTIVE

The spot sign is a validated imaging marker widely used in CT angiography (CTA) to detect active bleeding and a higher risk of hematoma expansion in patients with intracerebral hemorrhage (ICH). The aim of this study was to investigate the detectability of spot signs on thin multiplanar projection reconstruction (MPR) images compared to thicker maximum intensity projection (MIP) images.

METHODS

In this retrospective analysis, we assessed imaging data of 146 patients with primary hypertensive/microangiopathic ICH who received emergency non-contrast computed tomography (NCCT) and CTA. Two experienced radiologists, blinded to each other, evaluated images of thin (1 mm) MPR images and thick (3 mm) MIP images on the presence of spot signs and performed a consensus reading. Kappa tests were used for data comparison.

RESULTS

In total, spot signs were observed in 27 cases (=18.5 %) in both thin MPR and thick MIP slices. Detectability of the spot sign did not differ in 1 mm MPR images and 3 mm MIP images (Cohen's kappa, 1.0; p = 0.00). Also, when the readings of the two radiologists were analyzed separately, results for MPR and MIP slices were similar (MPR: Cohen's kappa, 0.81, p = 0.00; MIP: Cohen's kappa, 0.74; p = 0.00).

CONCLUSION

No significant difference in the detectability of the spot sign could be demonstrated when comparing 1 mm MPR images with 3 mm MIP images.

Noncontrast Computed Tomography Markers as Predictors of Revised Hematoma Expansion in Acute Intracerebral Hemorrhage

Background Noncontrast computed tomography (NCCT) markers are the emerging predictors of hematoma expansion in intracerebral hemorrhage. However, the relationship between NCCT markers and the dynamic change of hematoma in parenchymal tissues and the ventricular system remains unclear. Methods and Results We included 314 consecutive patients with intracerebral hemorrhage admitted to our hospital from July 2011 to May 2017. The intracerebral hemorrhage volumes and intraventricular hemorrhage (IVH) volumes were measured using a semiautomated, computer-assisted technique. Revised hematoma expansion (RHE) was defined by incorporating the original definition of hematoma expansion into IVH growth. Receiver operating characteristic curve analysis was used to compare the performance of the NCCT markers in predicting the IVH growth and RHE. Of 314 patients in our study, 61 (19.4%) had IVH growth and 93 (23.9%) had RHE. After adjustment for potential confounding variables, blend sign, black hole sign, island sign, and expansion-prone hematoma could independently predict IVH growth and RHE in the multivariate logistic regression analysis. Expansion-prone hematoma had a higher predictive performance of RHE than any single marker. The diagnostic accuracy of RHE in predicting poor prognosis was significantly higher than that of hematoma expansion. Conclusions The NCCT markers are independently associated with IVH growth and RHE. Furthermore, the expansion-prone hematoma has a higher predictive accuracy for prediction of RHE and poor outcome than any single NCCT marker. These findings may assist in risk stratification of NCCT signs for predicting active bleeding.

An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage

Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. This article reviews the use of deep learning convolutional neural networks for managing hemorrhagic stroke. Such a capability may be used to alert appropriate care teams, make decisions about patient transport from a primary care center to a comprehensive stroke center, and assist in treatment selection. This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.

Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

Intracerebral hemorrhage (ICH) is the most lethal type of stroke, but there is no specific treatment. After years of effort, neurologists have found that hematoma expansion (HE) is a vital predictor of poor prognosis in ICH patients, with a not uncommon incidence ranging widely from 13 to 38%. Herein, the progress of studies on HE after ICH in recent years is updated, and the topics of definition, prevalence, risk factors, prediction score models, mechanisms, treatment, and prospects of HE are covered in this review. The risk factors and prediction score models, including clinical, imaging, and laboratory characteristics, are elaborated in detail, but limited by sensitivity, specificity, and inconvenience to clinical practice. The management of HE is also discussed from bench work to bed practice. However, the upmost problem at present is that there is no treatment for HE proven to definitely improve clinical outcomes. Further studies are needed to identify more accurate predictors and effective treatment to reduce HE.

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

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

Expansion-Prone Hematoma: Defining a Population at High Risk of Hematoma Growth and Poor Outcome

BACKGROUND

Noncontrast computed tomography (CT) markers are increasingly used for predicting hematoma expansion. The aim of our study was to investigate the predictive value of expansion-prone hematoma in predicting hematoma expansion and outcome in patients with intracerebral hemorrhage (ICH).

METHODS

Between July 2011 and January 2017, ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. Expansion-prone hematoma was defined as the presence of one or more of the following imaging markers: blend sign, black hole sign, or island sign. The diagnostic performance of blend sign, black hole sign, island sign, and expansion-prone hematoma in predicting hematoma expansion was assessed. Predictors of hematoma growth and poor outcome were analyzed using multivariable logistical regression analysis.

RESULTS

A total of 282 patients were included in our final analysis. Of 88 patients with early hematoma growth, 69 (78.4%) had expansion-prone hematoma. Expansion-prone hematoma had a higher sensitivity and accuracy for predicting hematoma expansion and poor outcome when compared with any single imaging marker. After adjustment for potential confounders, expansion-prone hematoma independently predicted hematoma expansion (OR 28.33; 95% CI 12.95-61.98) and poor outcome (OR 5.67; 95% CI 2.82-11.40) in multivariable logistic model.

CONCLUSION

Expansion-prone hematoma seems to be a better predictor than any single noncontrast CT marker for predicting hematoma expansion and poor outcome. Considering the high risk of hematoma expansion in these patients, expansion-prone hematoma may be a potential therapeutic target for anti-expansion treatment in future clinical studies.

Standards for Detecting, Interpreting, and Reporting Noncontrast Computed Tomographic Markers of Intracerebral Hemorrhage Expansion

Significant hematoma expansion (HE) affects one-fifth of people within 24 hours after acute intracerebral hemorrhage (ICH), and its prevention is an appealing treatment target. Although the computed tomography (CT)-angiography spot sign predicts HE, only a minority of ICH patients receive contrast injection. Conversely, noncontrast CT (NCCT) is used to diagnose nearly all ICH, so NCCT markers represent a widely available alternative for prediction of HE. However, different NCCT signs describe similar features, with lack of consensus on the optimal image acquisition protocol, assessment, terminology, and diagnostic criteria. In this review, we propose practical guidelines for detecting, interpreting, and reporting NCCT predictors of HE. ANN NEUROL 2019;86:480-492.

Meta-Analysis of Accuracy of the Spot Sign for Predicting Hematoma Growth and Clinical Outcomes

Background and Purpose- The computed tomography angiographic spot sign refers to contrast leakage within intracerebral hemorrhage (ICH). It has been proposed as a surrogate radiological marker for ICH growth. We conducted a meta-analysis to study the accuracy of the spot sign for predicting ICH growth and mortality. Methods- PubMed, Medline, conference proceedings, and article references in English up to June 2017 were searched for studies reporting "computed tomography angiography" and "spot sign" or "intracerebral hemorrhage" and "spot sign." Each study was ranked on 27 criteria resulting in a quality rating score. Bivariate random effect meta-analysis was used to calculate positive and negative likelihood ratios and area under summary receiver operating characteristics curve for ICH growth and mortality. Hematoma growth was defined using the change in ≥6 mL or ≥33% increase in volume. Results- There were 26 studies describing 5085 patients, including 15 studies not used in previous meta-analyses. Positive likelihood ratio and negative likelihood ratio for ICH growth were 4.85 (95% CI, 3.85-6.02; I²=76.1%) and 0.49 (95% CI, 0.40-0.58) and mortality were 4.65 (95% CI, 3.67-5.90) and 0.55 (95% CI, 0.40-0.69), respectively. For ICH growth, the pooled sensitivity was 0.57 (95% CI, 0.49-0.64) and pooled false positive rate was 0.12 (95% CI, 0.09-0.14). The post-test probability of ICH growth was 0.57. The area under the curve for ICH growth and mortality was 0.86 and 0.87 (CIs are not provided in bivariate method). Meta-regression showed sensitivity of the test to decline significantly with subsequent year of publication (β=-0.148; 95% CI, -0.295 to -0.001; P=0.05). Higher quality assessment is associated with lower false positive rate (β=-0.074; 95% CI, -0.126 to -0.022; P=0.006). Conclusions- The high area under the curve potentially suggests that the spot sign can predict hematoma growth and mortality. Caution is recommended in its application given the heterogeneity across studies, which is appropriate given the data.

BAT Score Versus Spot Sign in Predicting Intracerebral Hemorrhage Expansion

OBJECTIVE

The BAT score is a novel prediction score of hematoma expansion based on noncontrast computed tomography (CT) and consists of the blend sign, hypodensities, and time interval from onset to CT. This study aimed to compare the BAT score and the spot sign on CT angiography in a cohort of patients with spontaneous intracerebral hemorrhage.

METHODS

Eligible patients with spontaneous intracerebral hemorrhage were analyzed retrospectively. The BAT score and the spot sign were assessed according to the criteria described in previous studies. Receiver operating curve analysis was used to assess the performance of the BAT score and the spot sign in hematoma expansion prediction.

RESULTS

In 225 included patients, 34 (15.1%) had a BAT score ≥3. The spot sign was shown in 68 (30.2%) patients. Hematoma expansion was identified in 56 (24.9%) patients. In multivariate analysis, both BAT score ≥3 and presence of spot sign were independently correlated with hematoma expansion. BAT score ≥3 had 0.41 sensitivity and 0.93 specificity, and spot sign had 0.64 sensitivity and 0.81 specificity. The area under the curve of BAT score ≥3 and area under the curve of spot sign were 0.673 and 0.727, respectively (P = 0.386).

CONCLUSIONS

The BAT score based on noncontrast CT is a good predictor of hematoma expansion. When CT angiography spot sign is unobtainable, the BAT score can be used to predict hematoma expansion.

Association between serum glucose level and spot sign in intracerebral hemorrhage

Hyperglycemia was proved to cause neuron death in both animal experiments and poor outcome of hemorrhage patients, but the predictive ability of admission blood glucose level for early hematoma growth in patients with intracranial hemorrhage (ICH) is still controversial. Spot sign is a well-established imaging predictor for early hematoma growth, implying active microvascular bleeding. Here, we aim to assess associations between admission serum glucose and early hematoma expansion in ICH patients, as well as spot sign.We retrospectively reviewed all the patients with ICH from January 2017 to March 2018 in West China Hospital, Sichuan University. Admission blood glucose, clinical variables, radiological characteristics, and laboratorial parameters were obtained from medical record. According to computed tomography (CT) and computed tomography angiography (CTA) scan results, hematoma expansion and spot sign were identified by 2 experienced neuroradiologists. Multivariate logistic regression analyses were employed to adjust the associations of hematoma expansion and spot sign with other clinical parameters.Around 42 patients exhibited early hematoma expansions and 26 exhibited spot signs over 138 enrolled patients. The average level of admission blood glucose was 7.55 mmol/L. Multivariate logistic regression analyses revealed that Glasgow Coma Scale (GCS) score on admission, hematoma volume, spot sign, and hyperglycemia were associated with hematoma expansion, whereas admission serum glucose and hematoma size were only associated with spot sign, respectively.Admission blood glucose level is correlated with hematoma growth and incidence of spot sign. These results indicated that hyperglycemia probably plays a critical role in the pathological process of the active bleeding. Further studies should be drawn urgently to understand the potential molecular mechanism of systemic hyperglycemia in affecting prognosis of patients with ICH.

The comparative study of island sign and the spot sign in predicting short-term prognosis of patients with intracerebral hemorrhage

OBJECTIVES

It is well known that early hematoma expansion is associated with short-term prognosis of patients with intracranial hemorrhage (ICH). And spot sign is recognized as a reliable computed tomography angiography (CTA) predictor for early hematoma expansion. Recently, island sign is also reported as a novel computed tomography (CT) predictor for early hematoma growth. Here, we compared the predictive abilities of these two signs for short-term outcomes of ICH patients.

PATIENTS AND METHODS

All the ICH patients were retrospectively identified. Clinical characteristics and radiological parameters were obtained from electronic medical records. Hematoma expansion, spot sign and island sign were assessed by two senior neurologists according to the initial and follow-up CT scans. 3-months prognoses were estimated according to Glasgow outcome scale (GOS). Multivariate logistic regression analyses were employed to explore the associations of short-term prognosis on island sign, spot sign and other clinical variables.

RESULTS

There were 283 ICH patients included. 113 of them presented with early hematoma expansions. 66 of them exhibited island sign, while spot sign occurred in 85 patients. Univariate analyses demonstrated that GCS score at admission (OR: 0.464, 95%CI: 0.395-0.547), hematoma volume (OR:1.062, 95%CI: 1.041-1.083), interventricular extension(OR:9.528, 95%CI: 3.915-23.187), island sign (OR: 4.595, 95%CI: 2.404-8.784) and spot sign (OR: 4.052, 95%CI: 2.297-7.147) were correlated with 3-months morbidity. Moreover, multivariate logistic regression analyses further revealed that both spot sign (OR: 3.413, 95%CI: 1.570-7.422) and island sign (OR: 7.564, 95%CI: 2.969-19.273) were strongly associated with 3-months poor outcome and have comparable predictive values (AUC: 0.636 vs. 0.622, P = .58). However, spot sign exhibited a superior predictive ability for 3-months mortality compared to island sign (OR: 2.713, 95%CI: 1.570-4.217 vs. OR: 2.362, 95%CI: 1.238-3.899, AUC: 0.700 vs. 0.603, P < .01).

CONCLUSIONS

Island sign is not just a convenient and reliable predictor for short-term prognosis of ICH patients, but also could be used as an indicator for accurate diagnosis and aggressive treatment.

Evaluating the Predictive Value of Island Sign and Spot Sign for Hematoma Expansion in Spontaneous Intracerebral Hemorrhage

OBJECTIVE

Hematoma expansion (HE) is closely related to poor outcome in spontaneous intracerebral hemorrhage (sICH). Island sign (IS) is a novel HE predictor based on noncontrast computed tomography (NCCT). This study is aimed to confirm the accuracy of IS for predicting HE and compare it to the spot sign (SS) on computed tomography angiography (CTA).

METHODS

Patients with sICH and initial CTA within 6 hours after onset and follow-up NCCT within 24 hours after initial CTA were included. IS and SS were screened by 2 independent readers. The sensitivity and specificity were evaluated for both signs. Receiver-operator analysis was conducted to assess the accuracy of both signs for predicting HE.

RESULTS

This study included 165 patients. IS was found in 33 (20.0%) patients, and SS was identified in 42 (25.5%) patients. In the 41 patients with HE, 19 (46.3%) had IS and 26 (63.4%) had SS. The sensitivity and specificity of IS were 46.3% and 88.7%, respectively. In contrast, the sensitivity and specificity of SS were 63.4% and 87.1%, respectively. The areas under the curve of IS and SS were 0.675 and 0.753, respectively (P = 0.275).

CONCLUSIONS

IS is independently associated with HE. Although the accuracy of IS for predicting HE is lower than SS, it can be an alternative predictor if CTA cannot be performed.

Meta-Analysis of Predictive Significance of the Black Hole Sign for Hematoma Expansion in Intracerebral Hemorrhage

OBJECTIVE

Hematoma expansion is related to unfavorable prognosis in intracerebral hemorrhage (ICH). The black hole sign is a novel marker on non-contrast computed tomography for predicting hematoma expansion. However, its predictive values are different in previous studies. Thus, this meta-analysis was conducted to evaluate the predictive significance of the black hole sign for hematoma expansion in ICH.

METHODS

A systematic literature search was performed. Original researches on the association between the black hole sign and hematoma expansion in ICH were included. Sensitivity and specificity were pooled to assess the predictive accuracy. Summary receiver operating characteristics curve (SROC) was developed. Deeks' funnel plot asymmetry test was used to assess the publication bias.

RESULTS

Five studies with a total of 1495 patients were included in this study. The pooled sensitivity and specificity of the black hole sign for predicting hematoma expansion were 0.30 and 0.91, respectively. The area under the curve was 0.78 in SROC curve. There was no significant publication bias.

CONCLUSIONS

This meta-analysis shows that the black hole sign is a helpful imaging marker for predicting hematoma expansion in ICH. Although the black hole sign has a relatively low sensitivity, its specificity is relatively high.

Accuracy of the Blend Sign on Computed Tomography as a Predictor of Hematoma Growth after Spontaneous Intracerebral Hemorrhage: A Systematic Review

BACKGROUND

Hematoma growth is a strong independent predictor of poor outcome after intracerebral hemorrhage. However, there is no gold standard to accurately predict hematoma growth. Several noncontrast computed tomographic markers associated with hematoma growth have been reported recently. Blend sign, which is a new marker, has been reported in several studies and seems a particularly promising marker but lacks a standardized evaluation so far.

METHODS

A systematic review of published literature on blend sign and hematoma growth and clinical outcomes was conducted. Systematic review of best practices was followed, and study quality was assessed.

RESULTS

The 6 studies involved 1573 participants in this review. The prevalence of blend sign ranged from 8.70% to 38.46%. The sensitivity of blend sign to predict hematoma growth varied from 13.0% to 42.86%; the specificity varied from 88.51% to 95.5%. Blend sign showed lower sensitivity but superior specificity for prediction of hematoma growth. Four studies indicated that the presence of blend sign was an independent predictor of hematoma growth. Four studies showed that the prevalence of blend sign was significantly higher in patients with hematoma growth compared with those without hematoma growth (odds ratio, 9.33; 95% confidence interval, 5.20-16.74).

CONCLUSION

There was an association between blend sign and hematoma growth, but this finding is tentative in light of the fact that the number of included studies was relatively small.

Comparison of Swirl Sign and Black Hole Sign in Predicting Early Hematoma Growth in Patients with Spontaneous Intracerebral Hemorrhage

Background

Early hematoma growth is associated with poor outcome in patients with spontaneous intracerebral hemorrhage (ICH). The swirl sign (SS) and the black hole sign (BHS) are imaging markers in ICH patients. The aim of this study was to compare the predictive value of these 2 signs for early hematoma growth.

Material/Methods

ICH patients were screened for the appearance of the 2 signs within 6 h after onset of symptoms. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the 2 signs in predicting early hematoma growth were assessed. The accuracy of the 2 signs in predicting early hematoma growth was analyzed by receiver-operator analysis.

Results

A total of 200 patients were enrolled in this study. BHS was found in 30 (15%) patients, and SS was found in 70 (35%) patients. Of the 71 patients with early hematoma growth, BHS was found on initial computed tomography scans in 24 (33.8%) and SS in 33 (46.5%). The sensitivity, specificity, PPV, and NPV of BHS for predicting early hematoma growth were 33.8%, 95.3%, 80.0%, and 72.0%, respectively. The sensitivity, specificity, PPV, and NPV of SS were 46.5%, 71.3%, 47.0%, and 71.0%, respectively. The area under the curve was 0.646 for BHS and 0.589 for SS (P=0.08). Multivariate logistic regression showed that presence of BHS is an independent predictor of early hematoma growth.

Conclusions

The Black hole sign seems to be good predictor for hematoma growth. The presence of swirl sign on admission CT does not independently predict hematoma growth in patients with ICH.

Heterogeneity Signs on Noncontrast Computed Tomography Predict Hematoma Expansion after Intracerebral Hemorrhage: A Meta-Analysis

Background and Purpose

Hematoma expansion (HE) is related to clinical deterioration after intracerebral hemorrhage (ICH) and noncontrast computed tomography (NCCT) signs are indicated as predictors for HE but with inconsistent conclusions. We aim to clarify the correlations of NCCT heterogeneity signs with HE by meta-analysis of related studies.

Methods

PubMed, Embase, and Cochrane library were searched for eligible studies exploring the relationships between NCCT heterogeneity signs (hypodensity, mixed density, swirl sign, blend sign, and black hole sign) and HE. Poor outcome and mortality were considered as secondary outcomes. Odds ratio (OR) and its 95% confidence intervals (CIs) were selected as the effect size and combined using random effects model.

Results

Fourteen studies were included, involving 3240 participants and 435 HEs. The summary results suggested statistically significant correlations of heterogeneity signs with HE (OR, 5.17; 95% CI, 3.72–7.19, P < 0.001), poor outcome (OR, 3.60; 95% CI, 1.98–6.54, P < 0.001), and mortality (OR, 4.64; 95%, 2.96–7.27, P < 0.001).

Conclusions

Our findings suggested that hematoma heterogeneity signs on NCCT were positively associated with the increased risk of HE, poor outcome, and mortality rate in ICH.

Black Hole Sign Predicts Poor Outcome in Patients with Intracerebral Hemorrhage

Background: In spontaneous intracerebral hemorrhage (ICH), black hole sign has been proposed as a promising imaging marker that predicts hematoma expansion in patients with ICH. The aim of our study was to investigate whether admission CT black hole sign predicts hematoma growth in patients with ICH. Methods: From July 2011 till February 2016, patients with spontaneous ICH who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. The presence of black hole sign on admission non-enhanced CT was independently assessed by 2 readers. The functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days. Univariate and multivariable logistic regression analyses were performed to assess the association between the presence of the black hole sign and functional outcome. Results: A total of 225 patients (67.6% male, mean age 60.3 years) were included in our study. Black hole sign was identified in 32 of 225 (14.2%) patients on admission CT scan. The multivariate logistic regression analysis demonstrated that age, intraventricular hemorrhage, baseline ICH volume, admission Glasgow Coma Scale score, and presence of black hole sign on baseline CT independently predict poor functional outcome at 90 days. There are significantly more patients with a poor functional outcome (defined as mRS ≥4) among patients with black hole sign than those without (84.4 vs. 32.1%, p < 0.001; OR 8.19, p = 0.001). Conclusions: The CT black hole sign independently predicts poor outcome in patients with ICH. Early identification of black hole sign is useful in prognostic stratification and may serve as a potential therapeutic target for anti-expansion clinical trials.

Significance of satellite sign and spot sign in predicting hematoma expansion in spontaneous intracerebral hemorrhage

OBJECTIVES

Hematoma expansion is related to poor outcome in spontaneous intracerebral hemorrhage (ICH). Recently, a non-enhanced computed tomography (CT) based finding, termed the 'satellite sign', was reported to be a novel predictor for poor outcome in spontaneous ICH. However, it is still unclear whether the presence of the satellite sign is related to hematoma expansion.

MATERIAL AND METHODS

Initial computed tomography angiography (CTA) was conducted within 6h after ictus. Satellite sign on non-enhanced CT and spot sign on CTA were detected by two independent reviewers. The sensitivity and specificity of both satellite sign and spot sign were calculated. Receiver-operator analysis was conducted to evaluate their predictive accuracy for hematoma expansion.

RESULTS

This study included 153 patients. Satellite sign was detected in 58 (37.91%) patients and spot sign was detected in 38 (24.84%) patients. Among 37 patients with hematoma expansion, 22 (59.46%) had satellite sign and 23 (62.16%) had spot sign. The sensitivity and specificity of satellite sign for prediction of hematoma expansion were 59.46% and 68.97%, respectively. The sensitivity and specificity of spot sign were 62.16% and 87.07%, respectively. The area under the curve (AUC) of satellite sign was 0.642 and the AUC of spot sign was 0.746. (P=0.157) CONCLUSION: Our results suggest that the satellite sign is an independent predictor for hematoma expansion in spontaneous ICH. Although spot sign has the higher predictive accuracy, satellite sign is still an acceptable predictor for hematoma expansion when CTA is unavailable.