Noncontrast Computed Tomography Markers of Intracerebral Hemorrhage Expansion

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia

BACKGROUND AND PURPOSE

Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imaging studies in this population. The study aims to develop prediction models for 7-day and short-term mortality risk based on the non-contrast computed tomography (NCCT) image features.

METHODS

The NCCT image features of ICH in 135 leukemia patients between 2007-2023 were retrospectively extracted using manual assessment and radiomics methods. After multiple imputation of missing laboratory data, univariate logistic regression and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Random forest models were built with comprehensive evaluation and ranking of feature importance.

RESULT

135 and 129 patients were included in the studies for 7-day and short-term prognostic models, respectively. The median age of all enrolled patients was 35 years, and there were 86 male patients (63.7 %). Clinical models (validation: AUC [area under the curve] = 0.78, AUPRC [area under the precision-recall curve] = 0.73; AUC = 0.84, AUPRC = 0.86), radiomics models (validation: AUC = 0.82, AUPRC = 0.78; AUC = 0.75, AUPRC = 0.77), and the combined models (validation: AUC = 0.84, AUPRC = 0.83; AUC = 0.87, AUPRC = 0.89) predicted 7-day and short-term mortality with good predictive efficacy. Clinical decision curve analysis showed that the combined models predicted 7-day and 30-day risk of death would be more beneficial than other models. Shape features contributed significantly more than semantic features in both radiomics models and combined models (93.3 %, 52.1 %, as well as 85.2 %,37.4 %, respectively) for 7-day and 30-day mortality.

CONCLUSIONS

Combined models constructed based on NCCT perform well in predicting the risk of 7-day and short-term mortality in ICH patients with leukemia. Shape features extracted by radiomics are important markers for modeling the prognosis.

Diagnostic yield of computed tomography angiography in patients presenting with spontaneous intracerebral hemorrhage

BACKGROUND

Hypertension and cerebral amyloid angiopathy are the most common causes of spontaneous intracerebral hemorrhage (ICH); however, these conditions do not imply macrovascular pathology. Still, computed tomography (CT) angiography (CTA) is often performed in the acute phase in patients with ICH.

PURPOSE

To assess the diagnostic yield of CTA in the detection of secondary etiology in consecutive patients with spontaneous ICH.

MATERIAL AND METHODS

We performed a retrospective analysis of data from a prospective single-center cohort study of 203 patients presenting with spontaneous ICH admitted to a comprehensive stroke center over a two-year period (15 October 2016 to 15 October 2018). The underlying vascular pathology was assessed using CTA.

RESULTS

CTA was performed in addition to non-contrast CT and/or magnetic resonance imaging (MRI). Vascular pathology was found in 11 of 203 (5.4%) patients and included arteriovenous malformations (n=4), aneurysms (n=4), vasospasms (n=1), cerebral venous thrombosis (n=1), and other vascular malformations (n=1). In eight cases, the finding was deemed symptomatic. Patients with vascular pathology on CTA more often had lobar located hemorrhages (63.6% vs. 36.4%, P = 0.049). Numerically, patients with vascular pathology were younger, had smaller hematoma volumes, and lower mortality.

CONCLUSION

Underlying macrovascular pathology was detected on CTA in only approximately 1 of 20 consecutive patients with ICH. The patients with vascular pathology more often had a hemorrhage with a lobar location and young age and the present study is supportive of a risk-based stratification approach in performing CTA.

Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach

In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model's feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.

Predictors and Prognostic Impact of Hematoma Expansion in Infratentorial Cerebral Hemorrhage

BACKGROUND

Hematoma expansion (HE) is common and predicts poor outcome in patients with supratentorial intracerebral hemorrhage (ICH). We investigated the predictors and prognostic impact of HE in infratentorial ICH.

METHODS

We conducted a retrospective analysis of patients with brainstem and cerebellar ICH admitted at seven sites. Noncontrast computed tomography images were analyzed for the presence of hypodensities according to validated criteria, defined as any hypodense region strictly encapsulated within the hemorrhage with any shape, size, and density. Occurrence of HE (defined as > 33% and/or > 6-mL growth) and mortality at 90 days were the outcomes of interest. Their predictors were investigated using logistic regression with backward elimination at p < 0.1. Logistic regression models for HE were adjusted for baseline ICH volume, antiplatelet and anticoagulant treatment, onset to computed tomography time, and presence of hypodensities. The logistic regression model for mortality accounted for the ICH score and HE.

RESULTS

A total of 175 patients were included (median age 75 years, 40.0% male), of whom 38 (21.7%) had HE and 43 (24.6%) died within 90 days. Study participants with HE had a higher frequency of hypodensities (44.7 vs. 24.1%, p = 0.013), presentation within 3 h from onset (39.5 vs. 24.8%, p = 0.029), and 90-day mortality (44.7 vs. 19.0%, p = 0.001). Hypodensities remained independently associated with HE after adjustment for confounders (odds ratio 2.44, 95% confidence interval 1.13-5.25, p = 0.023). The association between HE and mortality remained significant in logistic regression (odds ratio 3.68, 95% confidence interval 1.65-8.23, p = 0.001).

CONCLUSION

Early presentation and presence of noncontrast computed tomography hypodensities were independent predictors of HE in infratentorial ICH, and the occurrence of HE had an independent prognostic impact in this population.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage

OBJECTIVE

Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it.

METHODS

This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram.

RESULTS

Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically.

CONCLUSIONS

Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.

Machine-learning-based performance comparison of two-dimensional (2D) and three-dimensional (3D) CT radiomics features for intracerebral haemorrhage expansion

AIM

To investigate the value of non-contrast CT (NCCT)-based two-dimensional (2D) radiomics features in predicting haematoma expansion (HE) after spontaneous intracerebral haemorrhage (ICH) and compare its predictive ability with the three-dimensional (3D) signature.

MATERIALS AND METHODS

Three hundred and seven ICH patients who received baseline NCCT within 6 h of ictus from two stroke centres were analysed retrospectively. 2D and 3D radiomics features were extracted in the manner of one-to-one correspondence. The 2D and 3D models were generated by four different machine-learning algorithms (regularised L1 logistic regression, decision tree, support vector machine and AdaBoost), and the receiver operating characteristic (ROC) curve was used to compare their predictive performance. A robustness analysis was performed according to baseline haematoma volume.

RESULTS

Each feature type of 2D and 3D modalities used for subsequent analyses had excellent consistency (mean ICC >0.9). Among the different machine-learning algorithms, pairwise comparison showed no significant difference in both the training (mean area under the ROC curve [AUC] 0.858 versus 0.802, all p>0.05) and validation datasets (mean AUC 0.725 versus 0.678, all p>0.05), and the 10-fold cross-validation evaluation yielded similar results. The AUCs of the 2D and 3D models were comparable either in the binary or tertile volume analysis (all p>0.5).

CONCLUSION

NCCT-derived 2D radiomics features exhibited acceptable and similar performance to the 3D features in predicting HE, and this comparability seemed unaffected by initial haematoma volume. The 2D signature may be preferred in future HE-related radiomic works given its compatibility with emergency condition of ICH.

Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma

PURPOSE

To evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma.

METHODS

Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.

RESULTS

105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778-1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.

CONCLUSION

The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

Optimal cut-off values of haematoma volume for predicting haematoma expansion at different intracerebral haemorrhage locations

BACKGROUND

Haematoma expansion (HE) is a frequent manifestation of acute intracerebral haemorrhage (ICH) and is associated with early disease progression and poor functional status. Approximately 30 % of patients with ICH experience substantial HE within the first few hours of onset.

OBJECTIVES

This study aimed to investigate the relationship between HE and initial volume at different locations in patients with ICH.

METHODS

We investigated consecutive patients with ICH admitted to the emergency room at Xiangyang No. 1 People's Hospital between January 2018 and June 2022. Haematoma volume was calculated using a three-dimensional slicer platform. Prediction models were assessed using a logistic regression model. The Youden index was used to assess the haematoma volume cut-off values for predicting HE.

RESULTS

This study included 306 patients: 161 had basal ganglia ICH, 41 lobar ICH, and 104 thalamic ICH. The area under the ROC curve (AUC) for the thalamic ICH score in predicting intraventricular haemorrhage (IVH) expansion ≥ 1 mL or delayed IVH expansion was 0.786, and the best cut-off value was 7.05 mL (specificity, 85.3 %; sensitivity, 62.8 %; and accuracy, 76.0 %). The AUC for the thalamic ICH and lobar ICH scores in predicting haematoma or IVH expansion were 0.756 and 0.653, respectively; the best cut-offs were 7.05 mL for the thalamus (specificity, 84.8 %; sensitivity, 60.0 %; and accuracy, 74.0 %) and 31.89 mL in the lobar area (specificity, 81.8 %; sensitivity, 52.3 %; and accuracy, 68.3 %).

CONCLUSIONS

Initial ICH volume predicted haematoma or IVH expansion at different locations. Moreover, it can assist clinicians in determining whether patients are suitable for future surgical interventions or other procedures.

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.

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers

Objective

To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH).

Methods

Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance.

Results

A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance.

Conclusion

Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.

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.

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.

Emergency management of intracerebral hemorrhage

Acute intracerebral hemorrhage is a medical emergency with high mortality and morbidity. Neuroimaging has a fundamental role in the etiological diagnosis, patients monitoring and in the risk stratification of hematoma expansion and poor outcome. The cornerstones of medical treatment in the acute phase are blood pressure lowering and coagulopathy reversal. Prevention of hematoma expansion is the main goal of these therapies and their efficacy is strongly time-dependent with a narrow time window. This review provides an update on the etiological diagnostic workup, acute treatment and prognosis of intracerebral hemorrhage.

Intracerebral haemorrhage

Intracerebral haemorrhage (ICH) is a dramatic condition caused by the rupture of a cerebral vessel and the entry of blood into the brain parenchyma. ICH is a major contributor to stroke-related mortality and dependency: only half of patients survive for 1 year after ICH, and patients who survive have sequelae that affect their quality of life. The incidence of ICH has increased in the past few decades with shifts in the underlying vessel disease over time as vascular prevention has improved and use of antithrombotic agents has increased. The pathophysiology of ICH is complex and encompasses mechanical mass effect, haematoma expansion and secondary injury. Identifying the causes of ICH and predicting the vital and functional outcome of patients and their long-term vascular risk have improved in the past decade; however, no specific treatment is available for ICH. ICH remains a medical emergency, with prevention of haematoma expansion as the key therapeutic target. After discharge, secondary prevention and management of vascular risk factors in patients remains challenging and is based on an individual benefit-risk balance evaluation.

Using Noncontrast Computed Tomography to Improve Prediction of Intracerebral Hemorrhage Expansion

BACKGROUND

Noncontrast computed tomography hypodensities are a validated predictor of hematoma expansion (HE) in intracerebral hemorrhage and a possible alternative to the computed tomography angiography (CTA) spot sign but their added value to available prediction models remains unclear. We investigated whether the inclusion of hypodensities improves prediction of HE and compared their added value over the spot sign.

METHODS

Retrospective analysis of patients admitted for primary spontaneous intracerebral hemorrhage at the following 8 university hospitals in Boston, US (1994-2015, prospective), Hamilton, Canada (2010-2016, retrospective), Berlin, Germany (2014-2019, retrospective), Chongqing, China (2011-2015, retrospective), Pavia, Italy (2017-2019, prospective), Ferrara, Italy (2010-2019, retrospective), Brescia, Italy (2020-2021, retrospective), and Bologna, Italy (2015-2019, retrospective). Predictors of HE (hematoma growth >6 mL and/or >33% from baseline to follow-up imaging) were explored with logistic regression. We compared the discrimination of a simple prediction model for HE based on 4 predictors (antitplatelet and anticoagulant treatment, baseline intracerebral hemorrhage volume, and onset-to-imaging time) before and after the inclusion of noncontrast computed tomography hypodensities, using receiver operating characteristic curve and De Long test for area under the curve comparison.

RESULTS

A total of 2465 subjects were included, of whom 664 (26.9%) had HE and 1085 (44.0%) had hypodensities. Hypodensities were independently associated with HE after adjustment for confounders in logistic regression (odds ratio, 3.11 [95% CI, 2.55-3.80]; P<0.001). The inclusion of noncontrast computed tomography hypodensities improved the discrimination of the 4 predictors model (area under the curve, 0.67 [95% CI, 0.64-0.69] versus 0.71 [95% CI, 0.69-0.74]; P=0.025). In the subgroup of patients with a CTA available (n=895, 36.3%), the added value of hypodensities remained statistically significant (area under the curve, 0.68 [95% CI, 0.64-0.73] versus 0.74 [95% CI, 0.70-0.78]; P=0.041) whereas the addition of the CTA spot sign did not provide significant discrimination improvement (area under the curve, 0.74 [95% CI, 0.70-0.78]).

CONCLUSIONS

Noncontrast computed tomography hypodensities provided a significant added value in the prediction of HE and appear a valuable alternative to the CTA spot sign. Our findings might inform future studies and suggest the possibility to stratify the risk of HE with good discrimination without CTA.

Intracerebral haemorrhage expansion: definitions, predictors, and prevention

Haematoma expansion affects a fifth of patients within 24 h of the onset of acute intracerebral haemorrhage and is associated with death and disability, which makes it an appealing therapeutic target. The time in which active intervention can be done is short as expansion occurs mostly within the first 3 h after onset. Baseline haemorrhage volume, antithrombotic treatment, and CT angiography spot signs are each associated with increased risk of haematoma expansion. Non-contrast CT features are promising predictors of haematoma expansion, but their potential contribution to current models is under investigation. Blood pressure lowering and haemostatic treatment minimise haematoma expansion but have not led to improved functional outcomes in randomised clinical trials. Ultra-early enrolment and selection of participants on the basis of non-contrast CT imaging markers could focus future clinical trials to show clinical benefit in people at high risk of expansion or investigate heterogeneity of treatment effects in clinical trials with broad inclusion criteria.

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].

Added value of non-contrast CT and CT perfusion markers for prediction of intracerebral hemorrhage expansion and outcome

OBJECTIVES

To test the hypothesis that the combined analysis of non-contrast CT (NCCT) and CT perfusion (CTP) imaging markers improves prediction of hematoma expansion (HE) and outcome in intracerebral hemorrhage (ICH).

METHODS

Retrospective, single-center analysis of patients with primary ICH undergoing NCCT and CTP within 6 h from onset. NCCT images were assessed for the presence of intrahematomal hypodensity and shape irregularity. Perihematomal cerebral blood volume and spot sign were assessed on CTP. The main outcomes of the analysis were HE (growth > 6 mL and/or > 33%) and poor functional prognosis (90 days modified Rankin Scale 3-6). Predictors of HE and outcome were explored with logistic regression.

RESULTS

A total of 150 subjects were included (median age 68, 47.1% males) of whom 54 (36%) had HE and 52 (34.7%) had poor outcome. The number of imaging markers on baseline imaging was independently associated with HE (odds ratio 2.66, 95% confidence interval 1.70-4.17, p < 0.001) and outcome (odds ratio 1.64, 95% CI 1.06-2.56, p = 0.027). Patients with the simultaneous presence of all the four markers had the highest risk of HE and unfavorable prognosis (mean predicted probability of 91% and 79% respectively). The combined-markers analysis outperformed the sensitivity of the single markers analyzed separately. In particular, the presence of at least one marker identified patients with HE and poor outcome with 91% and 87% sensitivity respectively.

CONCLUSION

NCCT and CTP markers provide additional yield in the prediction of HE and ICH outcome.

KEY POINTS

• Perihematomal hypoperfusion is associated with hematoma expansion and poor outcome in acute intracerebral hemorrhage. • Non-contrast CT and CT perfusion markers improve prediction of hematoma expansion and unfavorable prognosis. • A multimodal CT protocol including CT perfusion will help the identification of patients at high risk of clinical deterioration and poor outcome.

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.

A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage

OBJECTIVES

Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a machine learning approach.

METHODS

We enrolled 550 patients with spontaneous ICH to study early PHE expansion, and 389 patients to study delayed expansion. Two imaging researchers rated the shape and density of hematoma in non-contrast computed tomography (NCCT). We trained a radiological machine learning (ML) model, a radiomics ML model, and a combined ML model, using data from radiomics, traditional imaging, and clinical indicators. We then validated these models on an independent dataset by using a nested 4-fold cross-validation approach. We compared models with respect to their predictive performance, which was assessed using the receiver operating characteristic curve.

RESULTS

For both early and delayed PHE expansion, the combined ML model was most predictive (early/delayed AUC values were 0.840/0.705), followed by the radiomics ML model (0.799/0.663), the radiological ML model (0.779/0.631), and the imaging readers (reader 1: 0.668/0.565, reader 2: 0.700/0.617).

CONCLUSION

We validated a machine learning approach with high interpretability for the prediction of early and delayed PHE expansion. This new technique may assist clinical practice for the management of neurocritical patients with ICH.

KEY POINTS

• This is the first study to use artificial intelligence technology for the prediction of perihematomal edema expansion. • A combined machine learning model, trained on data from radiomics, clinical indicators, and imaging features associated with hematoma expansion, outperformed all other methods.

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.

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.

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.

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.

Irregular shape as an independent predictor of prognosis in patients with primary intracerebral hemorrhage

The utility of noncontrast computed tomography markers in the prognosis of spontaneous intracerebral hemorrhage has been studied. This study aimed to investigate the predictive value of the computed tomography (CT) irregularity shape for poor functional outcomes in patients with spontaneous intracerebral hemorrhage. We retrospectively reviewed all 782 patients with intracranial hemorrhage in our stroke emergency center from January 2018 to September 2019. Laboratory examination and CT examination were performed within 24 h of admission. After three months, the patient's functional outcome was assessed using the modified Rankin Scale. Multinomial logistic regression analyses were applied to identify independent predictors of functional outcome in patients with intracerebral hemorrhage. Out of the 627 patients included in this study, those with irregular shapes on CT imaging had a higher proportion of poor outcomes and mortality 90 days after discharge (P < 0.001). Irregular shapes were found to be significant independent predictors of poor outcome and mortality on multiple logistic regression analysis. In addition, the increase in plasma D-dimer was associated with the occurrence of irregular shapes (P = 0.0387). Patients with irregular shapes showed worse functional outcomes after intracerebral hemorrhage. The elevated expression level of plasma D-dimers may be directly related to the formation of irregular shapes.

A predictive nomogram for intracerebral hematoma expansion based on non-contrast computed tomography and clinical features

PURPOSE

To develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

HE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors.

RESULTS

A total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880-0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760-0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction.

CONCLUSION

The nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.

Noncontrast Computed Tomography Markers of Cerebral Hemorrhage Expansion: Diagnostic Accuracy Meta-Analysis

BACKGROUND AND PURPOSE

Assess the diagnostic accuracy of noncontrast computed tomography (NCCT) markers of hematoma expansion in patients with primary intracerebral hemorrhage.

METHODS

We performed a meta-analysis of observational studies and randomized controlled trials with available data for calculation of sensitivity and specificity of NCCT markers for hematoma expansion (absolute growth >6 or 12.5 mL and/or relative growth >33%). The following NCCT markers were analyzed: irregular shape, island sign (shape-related features); hypodensity, heterogeneous density, blend sign, black hole sign, and swirl sign (density-related features). Pooled accuracy values for each marker were derived from hierarchical logistic regression models.

RESULTS

A total of 10,363 subjects from 23 eligible studies were included. Significant risk of bias of included studies was noted. Hematoma expansion frequency ranged from 7% to 40%, mean intracerebral hemorrhage volume from 9 to 27.8 ml, presence of NCCT markers from 9% (island sign) to 82% (irregular shape). Among shape features, sensitivity ranged from 0.32 (95%CI = 0.20-0.47) for island sign to 0.68 (95%CI = 0.57-0.77) for irregular shape, specificity ranged from 0.47 (95%CI = 0.36-0.59) for irregular shape to 0.92 (95%CI = 0.85-0.96) for island sign; among density features sensitivity ranged from 0.28 (95%CI = 0.21-0.35) for black hole sign to 0.63 (95%CI = 0.44-0.78) for hypodensity, specificity ranged from 0.65 (95%CI = 0.56-0.73) for heterogeneous density to 0.89 (95%CI = 0.85-0.92) for blend sign.

CONCLUSION

Diagnostic accuracy of NCCT markers remains suboptimal for implementation in clinical trials although density features performed better than shape-related features. This analysis may help in better tailoring patients' selection for hematoma expansion targeted trials.

Prognostic determinants in patients with non traumatic intracerebral hemorrhage: a real life report

Background and aim: Nontraumatic intracerebral hemorrhage (ICH) remains a devastating disease for high in-hospital and long-term mortality and residual neurological disability. The aim of our study was to analyze the prognostic factors in patients managed for ICH in the real-life clinical practice.Materials and Methods: We retrospectively analyzed clinical and neuro-radiological data of consecutive patients admitted to our Hospital for ICH along 1 year. In-hospital mortality and 90-day modified Rankin scale (mRS) ≥4 were the study outcomes. Moreover, we compared patients admitted in Intensive Care Unit (ICU) with patients admitted in Stroke Unit (SU).Results: Ninety-eight patients with mean age ± SD 78 ± 12 years were enrolled. In-hospital and 90-day mortality were 36.7% and 41.8%, respectively. Patients who died had a significantly higher percentage of ICH volume >30 mL, irregular shape, lobar location, intraventricular hemorrhage (IVH), midline shift, hydrocephalus, hematoma enlargement, Glasgow Coma Scale (GCS) ≤9 at hospital admission, early neurological worsening (ENW), higher Hemphill ICH score, and underwent oro-tracheal intubation more frequently compared with patients who survived. Patients admitted to ICU were younger and significantly more critical compared with those who were admitted to SU. In-hospital mortality in patients admitted to ICU was 52.6% compared with 25% in patients admitted to SU (p < 0.01). Median mRS score at hospital discharge was 4 (IQR 3-5) and at 90 days was 4 (IQR 3-4). ENW, hematoma enlargement, Hemphill ICH score ≥3 and midline shift >10 mm were found independent risk factors for in-hospital mortality, while age was found as independent risk factor for 90-day mRS ≥4).Conclusion: In real life, prognosis of ICH is associated with clinical and radiological determinants. In our study ENW, hematoma enlargement, Hemphill ICH score ≥3 and midline shift >10 mm were associated with short-term mortality risk, while age with 90-day mRS ≥4.

Association Between Perihematomal Perfusion and Intracerebral Hemorrhage Outcome

BACKGROUND

The prognostic impact of perihematomal hypoperfusion in patients with acute intracerebral hemorrhage (ICH) remains unclear. We tested the hypothesis that perihematomal hypoperfusion predicts poor ICH outcome and explored whether hematoma growth (HG) is the pathophysiological mechanism behind this association.

METHODS

A prospectively collected single-center cohort of consecutive ICH patients undergoing computed tomography perfusion on admission was analyzed. Cerebral blood flow (pCBF) was measured in the manually outlined perihematomal low-density area. pCBF was categorized into normal (40-55 mL/100 g/min), low (< 40 mL/100 g/min), and high (> 55 mL/100 g/min). HG was calculated as total volume increase from baseline to follow-up CT. A modified Rankin scale > 2 at three months was the outcome of interest. The association between cerebral perfusion and outcome was investigated with logistic regression, and potential mediators of this relationship were explored with mediation analysis.

RESULTS

A total of 155 subjects were included, of whom 55 (35.5%) had poor outcome. The rates of normal pCBF, low pCBF, and high pCBF were 17.4%, 68.4%, and 14.2%, respectively. After adjustment for confounders and keeping subjects with normal pCBF as reference, the risk of poor outcome was increased in patients with pCBF < 40 mL/100 g/min (odds ratio 6.11, 95% confidence interval 1.09-34.35, p = 0.040). HG was inversely correlated with pCBF (R = -0.292, p < 0.001) and mediated part of the association between pCBF and outcome (proportion mediated: 82%, p = 0.014).

CONCLUSION

Reduced pCBF is associated with poor ICH outcome in patients with mild-moderate severity. HG appears a plausible biological mediator but does not fully account for this association, and other mechanisms might be involved.

Association between perihematomal perfusion and intracerebral hemorrhage shape

PURPOSE

The pathophysiological determinants of irregular intracerebral hemorrhage (ICH) shape are unclear. We aimed at characterizing the relationship between perihematomal perfusion and ICH shape.

METHODS

A single-center cohort of patients with primary ICH was analyzed. Patients underwent computed tomography perfusion within 6 h from onset. Cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) were calculated in the manually outlined perihematomal low-density region. ICH shape was rated on baseline non-contrast CT following international consensus criteria, and predictors of irregular shape were explored with logistic regression.

RESULTS

A total of 150 patients were included, of whom 66 (44%) had irregular shape. Perihematomal CBF was lower in irregular ICH (median 23 vs 35 mL/100 g/min, p<0.001). CBF<20 mL/100 g/min was independently associated with irregular shape (odds ratio 9.67, 95% CI 2.42-38.69, p=0.001).

CONCLUSION

Our findings suggest that perihematomal hypoperfusion may contribute to the CT appearance of acute ICH.

A prediction of hematoma expansion in hemorrhagic patients using a novel dual-modal machine learning strategy

Objective.Hematoma expansion is closely associated with adverse functional outcomes in patients with intracerebral hemorrhage (ICH). Prediction of hematoma expansion would therefore be of great clinical significance. We therefore attempted to predict hematoma expansion using a dual-modal machine learning (ML) strategy which combines information from non-contrast computed tomography (NCCT) images and multiple clinical variables.Approach.We retrospectively identified 140 ICH patients (57 with hematoma expansion) with 5616 NCCT images of hematoma (2635 with hematoma expansion) and 10 clinical variables. The dual-modal ML strategy consists of two steps. The first step is to derive a mono-modal predictor from a deep convolutional neural network using solely NCCT images. The second step is to achieve a dual-modal predictor by combining the mono-modal predictor with 10 clinical variables to predict hematoma growth using a multi-layer perception network.Main results. For the mono-modal predictor, the best performance was merely 69.5% in accuracy with solely the NCCT images, whereas the dual-modal predictor could boost the accuracy greatly to be 86.5% by combining clinical variables.Significance.To our knowledge, this is the best performance from using ML to predict hematoma expansion. It could be potentially useful as a screening tool for high-risk patients with ICH, though further clinical tests would be necessary to show its performance on a larger cohort of patients.

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

Background

Non-contrast CT (NCCT) brain imaging biomarkers of hematoma expansion in intracerebral hemorrhage (ICH) has gained relevance in recent times. Though intra-hematoma hypodensities (IHH) can predict hematoma expansion and outcome, it is postulated to be time-dependent.

Aim

To assess the differential prevalence of IHH in spontaneous ICH over time and assess its predictive valve in early hematoma expansion and functional outcome at 3 months.

Material and Methods

Patients with ICH within 48 h of stroke onset were included. Baseline clinical and demographic data were collected. Baseline NCCT brain was analyzed for hematoma volume, characterization of IHH, with 24-hours follow-up NCCT hematoma volume calculated for identification of hematoma expansion. Poor functional outcome was defined as mRS ≥3.

Results

Around 92 subjects were included in the study. IHH was found in 40%. Prevalence of IHH was higher in those with baseline NCCT performed within 3 h of symptom onset compared to those beyond 3 h (71% vs 29%, P = 0.002). The hematoma expansion was more common in patients with IHH compared to those without (54% vs 29%; P = 0.02). Multivariate analysis revealed the presence of IHH (rather than pattern or number) to be strongly associated with poor functional outcome at 3 months (OR 3.86; 95% CI: 1.11-13.42, P = 0.03).

Conclusion

There is a decreasing prevalence of IHH as the time from symptom onset to NCCT increases. Nevertheless, its presence is significantly associated with hematoma expansion and predicted poor short-term functional outcomes in spontaneous ICH.

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

PURPOSE

The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

METHOD

A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. An HE was defined as a volume increase of more than 33% or an increase greater than 12.5 mL from the volume of the baseline NCCT. Texture parameters of the baseline NCCT images were selected by the least absolute shrinkage and selection operator (LASSO) regression. We used support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k‑nearest neighbors (KNN), back-propagation neural network (BPNet) and Bayes to build models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was performed and compared among models.

RESULTS

Every model had a relatively high AUC (all > 0.75), SVM and KNN had the highest AUC of 0.91. There were significant differences between SVM and CIT (Z > 2.266, p = 0.02345), KNN and CIT (Z = 2.4834, p = 0.01301), RF and CIT (Z = 2.6956, p = 0.007027), KNN and BPNet (Z = 2.0122, p = 0.0442), RF and BPNet (Z = 1.9793, p = 0.04778). There was no significant difference among SVM, DT, RF, KNN and Bayes (p > 0.05). The SVM obtained the largest net benefit when the threshold probability was less than 0.33, while KNN obtained the largest net benefit when the threshold probability was greater than 0.33. Combined with ROC and DCA, SVM and KNN performed better in all the models for predicting HE.

CONCLUSION

Radiomic models based on different machine learning methods can be used to predict HE and the models generated by SVM and KNN performed best.

The new Hematoma Maturity Score is highly associated with poor clinical outcome in spontaneous intracerebral hemorrhage

OBJECTIVES

The purpose of this study was to analyze the new combined indicators on noncontrast computed tomography (NCCT) to predict functional outcome at discharge, compared to previously individual radiological NCCT signs.

METHODS

Patients with spontaneous intracerebral hemorrhage (ICH) who underwent baseline CT scan were retrospectively analyzed. Black hole (BH) sign, blend sign (BS), island sign (IS), swirl sign (SwS), Barras classification, any hypodensity, any irregularity, and two combined novel indicators-Combined Barras Total Score (CBTS) and Hematoma Maturity Score-were assessed independently by two radiologists blinded to clinical information. Patients were trichotomized depending on the disability or dependency at discharge according to the Modified Rankin Scale (mRS): no symptoms or no significant/mild disability (mRS 0-2); moderate or severe disability (mRS 3-5); and mortality (mRS 6).

RESULTS

One hundred fourteen patients with spontaneous ICH confirmed by NCCT were included in the analysis. Multivariable statistical analysis was adjusted for anticoagulation, hematoma volume, ventricular expansion, hypertension, blood glucose level at admission, age, and history of atrial fibrillation and demonstrated that any hypodensity (OR 4.768, p 0.006), any irregularity (OR 4.768, p 0.006), CBTS ≥ 4 (OR 3.205, p 0.025), and the new Hematoma Maturity Score (Immature) (OR 5.872, p 0.006) are independent predictors of functional outcome at discharge.

CONCLUSIONS

The new concept of the Hematoma Maturity Score was the radiological sign on NCCT with the highest impact on clinical outcome in comparison with the rest of the evaluated radiological signs.

KEY POINTS

• This is the first manuscript where density and shape characteristics of the ICH had been evaluated together and integrated in a new Hematoma Maturity Score. • The new Hematoma Maturity Score is the radiological sign on NCCT with the highest impact on clinical outcome at discharge.

Validation and Comparison of Noncontrast CT Scores to Predict Intracerebral Hemorrhage Expansion

BACKGROUND AND PURPOSE

The BAT, BRAIN, and HEP scores have been proposed to predict hematoma expansion (HE) with noncontrast computed tomography (NCCT). We sought to validate these tools and compare their diagnostic performance.

METHODS

We retrospectively analyzed two cohorts of patients with primary intracerebral hemorrhage. HE expansion was defined as volume growth > 33% or > 6 mL. Two raters analyzed NCCT scans and calculated the scores, blinded to clinical and imaging data. The inter-rater reliability was assessed with the interclass correlation statistic. Discrimination and calibration were calculated with area under the curve (AUC) and Hosmer-Lemeshow χ² statistic, respectively. AUC comparison between different scores was explored with DeLong test. We also calculated the sensitivity, specificity, positive, and negative predictive values of the dichotomized scores with cutoffs identified with the Youden's index.

RESULTS

A total of 230 subjects were included, of whom 86 (37.4%) experienced HE. The observed AUC for HE were 0.696 for BAT, 0.700 for BRAIN, and 0.648 for HEP. None of the scores had a significantly superior AUC compared with the others (all p > 0.4). All the scores had good calibration (all p > 0.3) and good-to-excellent inter-rater reliability (interclass correlation > 0.8). BAT ≥ 3 showed the highest specificity (0.81), whereas BRAIN ≥ 6 had the highest sensitivity (0.76).

CONCLUSIONS

The BAT, BRAIN, and HEP scores can predict HE with acceptable discrimination and require just a baseline NCCT scan. These tools may be used to stratify the risk of HE in clinical practice or randomized controlled trials.

Can perihaematomal radiomics features predict haematoma expansion?

AIM

To evaluate the association between perihaematomal radiomics features and haematoma expansion (HE).

MATERIALS AND METHODS

Clinical and radiological data were collected retrospectively. The 1:1 propensity score matching (PSM) method was used to balance the difference of baseline characteristics between patients with and without HE. Radiomics features were extracted from the intra- and perihaematomal regions. Top HE-associated features were selected using the minimum redundancy, maximum relevancy algorithm. Support vector machine models were used to predict HE. Predictive performance of radiomics features from different regions was evaluated by receiver operating characteristic curve and confusion matrix-derived metrics.

RESULTS

A total of 1,062 patients were enrolled. After PSM analysis, the propensity score-matched cohort (PSM cohort) included 314 patients (HE: n=157; non-HE: n=157). The PSM cohort was distributed into the training (n=218) and the validation cohorts (n=96). The predictive performance of intra- and perihaematomal features were comparable in the training (area under the receiver operating characteristic curve [AUC], 0.751 versus 0.757; p=0.867) and the validation cohorts (AUC, 0.724 versus 0.671; p=0.454). By incorporating intra- and perihaematomal features, the combined model outperformed the single intrahaematomal model in the training cohort (AUC, 0.872 versus 0.751; p<0.001). Decision curve analysis (DCA) further confirmed the clinical usefulness of the combined model.

CONCLUSION

Perihaematomal radiomics features can predict HE. The integration of intra- and perihaematomal signatures may provide additional benefit to the prediction of HE.

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.

Radiomics for intracerebral hemorrhage: are all small hematomas benign?

OBJECTIVES

We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas.

METHODS

We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated the developed radiomic score and clinical factors was then constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models.

RESULTS

The addition of radiomics to clinical factors significantly improved the prediction performance of HE compared with the clinical model alone in both the training {AUC, 0.762 [95% CI (0.665-0.859)] versus AUC, 0.651 [95% CI (0.556-0.745)], p = 0.007} and test {AUC, 0.776 [95% CI (0.655-0.897) versus AUC, 0.631 [95% CI (0.451-0.810)], p = 0.001} cohorts. Moreover, the radiomic-based model achieved good discrimination ability of poor outcomes in the 3-10 ml group (AUCs 0.720 and 0.701).

CONCLUSION

Compared with clinical information alone, combined model had greater potential for discriminating between benign and malignant course in patients with small ICH, particularly 3-10 ml hematomas.

ADVANCES IN KNOWLEDGE

Radiomics can be used as a supplement to conventional medical imaging, improving clinical decision-making and facilitating personalized treatment in small ICH.

Clinical-radiomics Nomogram for Risk Estimation of Early Hematoma Expansion after Acute Intracerebral Hemorrhage

RATIONALE AND OBJECTIVES

Noncontrast CT-based radiomics signature has shown ability for detecting hematoma expansion (HE) in spontaneous intracerebral hemorrhage (ICH). We sought to compare its predictive performance with clinical risk factors and develop a clinical-radiomics nomogram to assess the risk of early HE.

MATERIALS AND METHODS

In total, 1153 patients with ICH who underwent baseline cranial CT within 6 hours and follow-up scans within 72 hours of stroke onset were enrolled, of whom 864 (75%) were assigned to the derivation cohort and 289 (25%) to the validation cohort. Based on LASSO algorithm or stepwise logistic regression analysis, three models (clinical model, radiomics model, and hybrid model) were constructed to predict HE. The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of fit of the three models, and the AUC was used to evaluate their discrimination ability for HE.

RESULTS

The hybrid model (AIC = 681.426; χ²= 128.779) was the optimal model with the lowest AIC and highest chi-square values compared to the radiomics model (AIC = 767.979; χ² = 110.234) or the clinical model (AIC = 753.757; χ² = 56.448). The radiomics model was superior in the prediction of HE to the clinical model in both derivation (p = 0.009) and validation (p = 0.022) cohorts. In both datasets, the clinical-radiomics nomogram showed satisfactory discrimination and calibration for detecting HE (AUC = 0.771, Sensitivity = 87.0%; AUC = 0.820, Sensitivity = 88.1%; respectively).

CONCLUSION

Among patients with acute ICH, noncontrast CT-based radiomics model outperformed the clinical-only model in the prediction of HE, and the established clinical-radiomics nomogram with favorable performance can offer a noninvasive tool for the risk stratification of HE.

A nomogram to predict early hematoma expansion of hypertensive cerebral hemorrhage

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

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.

Revised intracerebral hemorrhage expansion definitions: Relationship with care limitations

BACKGROUND

Hematoma expansion is an important therapeutic target in intracerebral hemorrhage. Recently proposed hematoma expansion definitions have not been validated, and no previous definition has accounted for withdrawal of care.

AIMS

To externally validate revised definitions of hematoma expansion that incorporate intraventricular hemorrhage, and to test their validity in the context of withdrawal of care.

METHODS

We analyzed data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage II trial, comparing revised definitions of hematoma expansion incorporating intraventricular hemorrhage expansion to the conventional definition of "≥6 mL or ≥33%." Primary outcome was modified Rankin Scale of 4-6 at 90 days. We calculated the incidence, sensitivity, specificity, positive and negative predictive values, and c-statistic for all definitions of hematoma expansion. Definitions were compared using nonparametric methods. Secondary analyses were performed after removing patients with withdrawal of care.

RESULTS

Primary analysis included 948 patients. Using the conventional definition, the sensitivity was 37.1% and specificity was 83.2% for the primary outcome. Sensitivity improved with all three revised definitions (53.3%, 48.7%, and 45.3%, respectively), with minimal change to specificity (78.4%, 80.5%, and 81.0%, respectively). The greatest improvement was seen with the definition "≥6 mL or ≥33% or any intraventricular hemorrhage," with increased c-statistic from 60.2% to 65.9% (p < 0.001). Secondary analysis excluded 46 participants who experienced withdrawal of care. The revised definitions similarly outperformed the conventional definition in this population, with the greatest improvement in c-statistic using "≥6 mL or ≥33% or any intraventricular hemorrhage" (58.1% vs. 64.1%, p < 0.001).

CONCLUSIONS

Revised hematoma expansion definitions incorporating intraventricular hemorrhage expansion outperformed conventional definitions for predicting poor outcome, even after accounting for care limitations.

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.

Noncontrast CT markers of intracerebral hemorrhage expansion and poor outcome: A meta-analysis

OBJECTIVE

To provide precise estimates of the association between noncontrast CT (NCCT) markers, hematoma expansion (HE), and functional outcome in patients presenting with intracerebral hemorrhage (ICH) through a systematic review and meta-analysis.

METHODS

We searched PubMed for English-written observational studies or randomized controlled trials reporting data on NCCT markers of HE and outcome in spontaneous ICH including at least 50 subjects. The outcomes of interest were HE (hematoma growth >33%, >33% and/or >6 mL, >33% and/or >12.5 mL), poor functional outcome (modified Rankin Scale 3-6 or 4-6) at discharge or at 90 days, and mortality. We pooled data in random-effects models and extracted cumulative odds ratio (OR) for each NCCT marker.

RESULTS

We included 25 eligible studies (n = 10,650). The following markers were associated with increased risk of HE and poor outcome, respectively: black hole sign (OR = 3.70, 95% confidence interval [CI] = 1.42-9.64 and OR = 5.26, 95% CI = 1.75-15.76), swirl sign (OR = 3.33, 95% CI = 2.42-4.60 and OR = 3.70; 95% CI = 2.47-5.55), heterogeneous density (OR = 2.74; 95% CI = 1.71-4.39 and OR = 2.80; 95% CI = 1.78-4.39), blend sign (OR = 3.49; 95% CI = 2.20-5.55 and OR = 2.21; 95% CI 1.16-4.18), hypodensities (OR = 3.47; 95% CI = 2.18-5.50 and OR = 2.94; 95% CI = 2.28-3.78), irregular shape (OR = 2.01, 95% CI = 1.27-3.19 and OR = 3.43; 95% CI = 2.33-5.03), and island sign (OR = 7.87, 95% CI = 2.17-28.47 and OR = 6.05, 95% CI = 4.44-8.24).

CONCLUSION

Our results suggest that multiple NCCT ICH shape and density features, with different effect size, are important markers for HE and clinical outcome and may provide useful information for future randomized controlled trials.