Hematoma Expansion In Intracerebral Hemorrhage Research Articles

Reliability of ABC/2 volumetric estimation in spontaneous intracerebral hemorrhage for hematoma expansion prediction scores.

Prediction scores for hematoma expansion in spontaneous intracerebral hemorrhage (ICH), such as the 9-point and BRAIN scores, were developed predominantly using planimetry to measure hematoma volume. In this study, we aim to investigate whether the ABC/2 formula, which is known to overestimate hematoma volume, can be reliably used as a substitute for planimetry in these prediction scores. A total of 429 patients from four hospitals were retrospectively enrolled. CT scan and clinical data at admission and follow-up CT scan were collected. The 9-point and BRAIN scores were calculated using hematoma volume from ABC/2 and planimetry. Hematoma expansion was assessed using hematoma volume from planimetry. The median hematoma volume measured by ABC/2 was 11.97 ml (interquartile range [IQR], 4.8-30.0), whereas the volume measured by planimetry was 11.70 ml (IQR, 4.9-26.6). The median measurement error between ABC/2 and planimetry was 0.30 ml (IQR, -0.72-2.87). ABC/2 overestimated hematoma volume in 244 patients (56.9%) compared to planimetry. In the 9-point score, the area under the curves (AUCs) for predicting hematoma expansion were 0.735 (95% confidence interval [CI], 0.675-0.796) with ABC/2 and 0.732 (95% CI, 0.672-0.793) with planimetry. In the BRAIN score, the AUCs were 0.753 (95% CI, 0.693-0.813) with ABC/2 and 0.745 (95% CI, 0.688-0.803) with planimetry. The 9-point and BRAIN scores using hematoma volume measured by ABC/2 and planimetry showed good performance in predicting hematoma expansion in ICH. ABC/2 volumetric estimation proved to be reliable for these scores.

Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study

BackgroundEarly prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. However, complex image processing procedures, especially hematoma segmentation, are time-consuming and dependent on assessor experience. We provide a fully automated hematoma segmentation method, and construct a hybrid predictive model for risk stratification of hematoma expansion. PurposeTo propose an automatic approach for predicting early hemorrhage expansion after spontaneous intracerebral hemorrhage using deep-learning and radiomics methods. MethodsA total of 258 patients with sICH were retrospectively enrolled for model construction and internal validation, while another two cohorts (n=87, 149) were employed for independent validation. For hemorrhage segmentation, an iterative segmentation procedure was performed to delineate the area using an nnU-Net framework. Radiomics models of intra-hemorrhage and multiscale peri-hemorrhage were established and evaluated, and the best discriminative-scale peri-hemorrhage radiomics model was selected for further analysis. Combining clinical factors and intra- and peri-hemorrhage radiomics signatures, a hybrid nomogram was constructed for the early HE prediction using multivariate logistic regression. For model validation, the receiver operating characteristic (ROC) curve analyses and DeLong test were used to evaluate the performances of the constructed models, and the calibration curve and decision curve analysis were performed for clinical application. ResultsOur iterative auto-segmentation model showed satisfactory results for hematoma segmentation in all four cohorts. The Dice similarity coefficient of this hematoma segmentation model reached 0.90, showing an expert-level accuracy in hematoma segmentation. The consumed time of the efficient delineation was significantly decreased, from 18 min to less than 2 min, with the assistance of the auto-segmentation model. The radiomics model of 2-mm peri-hemorrhage had a preferable area under ROC curve (AUC) of 0.840 (95 % confidence interval [CI]: 0.768, 0.912) compared with the original (0-mm dilatation) model with an AUC of 0.796 (95 % CI: 0.717, 0.875). The clinical–radiomics hybrid model showed better performances for HE prediction, with AUC of 0.853, 0.852, 0.772, and 0.818 in the training, internal validation, and independent validation cohorts 1 and 2, respectively. ConclusionsThe fully automatic clinical–radiomics model based on deep learning and radiomics exhibits a good ability for hematoma segmentation and a favorable performance in stratifying HE risks.

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

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

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network

Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion. We retrospectively enrolled ICH patients at four hospitals between 2017 and 2021, assigning patients from three hospitals to the training and validation dataset and patients from one hospital to the test dataset. Admission CT images and clinical variables were collected. CT findings were evaluated by experts. Three types of models were developed and trained: (1) a CNN model analyzing CT images, (2) a multimodal CNN model analyzing CT images and clinical variables, and (3) a non-CNN model analyzing CT findings and clinical variables with machine learning. The models were evaluated on the test dataset, focusing first on sensitivity and second on area under the receiver operating curve (AUC). Two hundred seventy-three patients (median age, 71 years [59–79]; 159 men) in the training and validation dataset and 106 patients (median age, 70 years [62–82]; 63 men) in the test dataset were included. Sensitivity and AUC of a CNN model were 1.000 (95% confidence interval [CI] 0.768–1.000) and 0.755 (95% CI 0.704–0.807); those of a multimodal CNN model were 1.000 (95% CI 0.768–1.000) and 0.799 (95% CI 0.749–0.849); and those of a non-CNN model were 0.857 (95% CI 0.572–0.982) and 0.733 (95% CI 0.625–0.840). We developed a multimodal neural network model incorporating CNN analysis of CT images and neural network analysis of clinical variables to predict hematoma expansion in ICH. The model was externally validated and showed the best performance of all the models.

Continuous arterial blood pressure indices and early hematoma expansion in patients with spontaneous intracerebral hemorrhage

BackgroundBlood pressure variability (BPV) and its potential association with early hematoma expansion (HE) in intracerebral hemorrhage (ICH) remains to be fully elucidated. Our study explores the potential link between BPV within the first 24 h after admission and HE in ICH. MethodsIn a prospective cohort single-center study, we analyzed consecutive patients with spontaneous ICH. Continuous BP data via an arterial line extracted from the Intellispace Critical Care and Anesthesia information system (Philips Healthcare) were analyzed over 0–2, 0–8, 0–12, and 0–24 h intervals post-admission. BPV was assessed through successive variability (SV), standard deviation (SD), and coefficient of variation (CV) using all available BP measurements. Early HE was defined as an absolute [≥ 6 ml] or relative [≥ 33 %] increase in ICH volume on 24-hours follow-up brain imaging. Secondary endpoints were the influence of BP on admission and other potential risk factors for HE. ResultsAmong 305 ICH-patients (mean age ± SD 70.1 ± 14.9 years, 47.9 % female, median NIHSS 6 (3, 13), median ICH score 1 (1, 2)), 41 (13.4 %) experienced HE. HE-patients had higher NIHSS (p = 0.015), ICH-score (p = 0.005), ICH volume (p < 0.001) and higher pre-anticoagulation treatment (p = 0.004) on admission. There was no difference in BPV comparing ICH-patients with HE to those without. However, patients with HE had significantly lower diastolic BP (76.6 ± 14.8 vs. 86.3 ± 19.7 mmHg, p = 0.005) and MAP (103.2 ± 22.4 vs. 112.2 ± 22.6, p = 0.027) on admission. This pattern of lower diastolic BP persisted across the first 24 h. Logistic regression revealed larger ICH volume and pre-existing anticoagulation as significant predictors of HE, with higher initial diastolic BP reducing HE risk. Hemorrhages ≥ 30 cm3 showed significantly lower initial diastolic BP, MAP, and BPV across all time frames compared to ICH < 30 cm3. ConclusionsBPV within the first 24 h was not associated with HE. Lower diastolic BP on admission, anticoagulation use, and larger ICH volume are potential predictors for HE. However, larger hemorrhage size (>30 cm3) experienced lower absolute BP and BPV indices and worse clinical outcomes. These findings suggest a nuanced relationship between BP dynamics and ICH severity, underscoring the need for individualized BP management in acute ICH care. Further research is necessary to explore these relationships and optimize treatment strategies.

Red cell distribution width to lymphocyte ratio could serve as a new inflammatory biomarker for predicting hematoma expansion in patients with intracerebral hemorrhage

BackgroundHematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients.MethodsWe conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion.ResultsThe study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54–4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25–2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13–2.85; P = 0.022) and hematoma expansion in these patients.ConclusionsOur findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.

Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach

Spontaneous intracerebral hemorrhage (ICH) is the most severe form of stroke. The timely assessment of early hematoma enlargement and its proper treatment are of great significance in curbing the deterioration and improving the prognosis of patients with ICH. This study aimed to develop an automated hybrid approach to predict hematoma expansion in ICH. The transfer learning method was applied to build a hybrid model based on a convolutional neural network (CNN) to predict the expansion of hematoma. The model integrated (1) a CNN for automated hematoma segmentation and (2) a CNN-based classifier for hematoma expansion prediction that incorporated both 2-dimensional images and the radiomics features of the 3-dimensional hematoma shape. The radiomics feature module had the highest area under the receiver operating characteristic curve (AUC) of 0.58, a precision of 0, a recall of 0, and an average precision (AP) of 0.26. The ResNet50 and Inception_v3 modules had AUCs of 0.79 and 0.93, a precision of 0.56 and 0.86, a recall of 0.42 and 0.75, and an AP of 0.51 and 0.85, respectively. Radiomic with Inception_v3 and Radiomic with ResNet50 had AUCs of 0.95 and 0.81, a precision of 0.90 and 0.57, a recall of 0.79 and 0.17, and an AP of 0.87 and 0.69, respectively. A model using deep learning and radiomics was successfully developed. This model can reliably predict the hematoma expansion of ICH with a fully automated process based on non-contrast computed tomography imaging. Furthermore, the radiomics fusion with the Inception_v3 model had the highest accuracy.

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

BackgroundSeveral studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. MethodsThe cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. ResultsWe finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. ConclusionsNCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Comparison of stress hyperglycemia ratio and glycemic gap on acute ICH in-hospital outcomes.

To compare the effect of different indicators on stress-induced hyperglycemia for predicting in-hospital outcomes of acute intracerebral hemorrhage. Using data from the Chinese Stroke Center Alliance database, which is a national, multicenter, prospective, and consecutive program. Stress-induced hyperglycemia was described as glycemic gap (GG, defined as fasting blood glucose [FBG] minus estimated average blood glucose) and stress hyperglycemia ratio (SHR, defined as FBG-to-estimated average blood glucose ratio [SHR 1] or FBG-to-HbA1c ratio [SHR 2]). The primary outcome was in-hospital mortality, and the second outcome was hematoma expansion. A total of 71,333 patients with acute intracerebral hemorrhage were included. In multivariate analyses, the highest levels of GG (OR 1.68, 95% CI 1.12-2.51), SHR 1 (OR 1.73, 95% CI 1.15-2.60), and SHR 2 (OR 2.07, 95% CI 1.33-3.23) were associated with in-hospital death (all the p trends <0.01). Only the highest level of SHR 2 (OR 1.24 [1.02-1.51], p trend >0.05) was related to hematoma expansion. No association between GG or SHR 1 and hematoma expansion was observed. The areas under the ROC curve of GG, SHR 1, and SHR 2 for in-hospital mortality were 0.8808 (95% CI 0.8603-0.9014), 0.8796 (95% CI 0.8589-0.9002), and 0.8806 (95% CI 0.8600-0.9012). The areas under the ROC curve of SHR 2 for hematoma expansion were 0.7133 (95% CI 0.6964-0.7302). SHR (FBG-to-HbA1c ratio) was associated with both in-hospital death and hematoma expansion in intracerebral hemorrhage, and might serve as an accessory indicator for the in-hospital prognosis of intracerebral hemorrhage.

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

ObjectiveThe significance of non-contrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hence, our objective was to examine the predictive value of clinical factors and CT image markers for HE within the initial 24 hours using machine learning algorithms. MethodsFour machine learning algorithms, including extreme gradient boosting (XGBoost), support vector machine, random forest, and logistic regression, were employed to assess the predictive efficacy of HE within every 6-hour interval during the first 24 hours post-ICH. The area under the receiver operating characteristic curves was utilized to appraise predictive performance across various time periods within the initial 24 hours. ResultsA total of 604 patients were included, with 326 being male, and 112 experiencing HE. The findings from machine learning algorithms revealed that CT image markers, baseline hematoma volume, and other factors could accurately predict HE. Among these algorithms, XGBoost demonstrated the most robust predictive model results. XGBoost's accuracy at different time intervals was 0.89, 0.82, 0.87, and 0.94, accompanied by F1-scores of 0.89, 0.80, 0.87, and 0.93, respectively. The corresponding area under the curve was 0.96, affirming the precision of the predictive capability. ConclusionsCT imaging markers and clinical factors could effectively predict HE within the initial 24 hours across various time periods by machine learning algorithms. In the expansive landscape of big data and multimodal cerebral hemorrhage, machine learning held significant potential within the realm of neuroscience.

A reassessment of hemoglobin and hematoma expansion in intracerebral hemorrhage

BackgroundIn patients with spontaneous intracerebral hemorrhage (ICH), prior studies identified an increased risk of hematoma expansion (HE) in those with lower admission hemoglobin (Hgb) levels. We aimed to reproduce these findings in an independent cohort. MethodsWe conducted a cohort study of patients admitted to a Comprehensive Stroke Center for acute ICH within 24 hours of onset. Admission laboratory and CT imaging data on ICH characteristics including HE (defined as >33% or >6 mL), and 3-month outcomes were collected. We compared laboratory data between patients with and without HE and used multivariable logistic regression to determine associations between Hgb, HE, and unfavorable 3-month outcomes (modified Rankin Scale 4-6) while adjusting for confounders including anticoagulant use, and laboratory markers of coagulopathy. ResultsAmong 345 patients in our cohort (mean [SD] age 72.9 [13.7], 49% male), 71 (21%) had HE. Patients with HE had similar Hgb versus those without HE (mean [SD] 13.1 [1.8] g/dl vs. 13.1 [1.9] g/dl, p=0.92). In fully adjusted multivariable models, Hgb was not associated with HE (OR per 1g/dl 1.01, 95% CI 0.86 -1.17, p = 0.94), however higher admission Hgb levels were associated with lower odds of unfavorable 3-month outcome (OR 0.83 per 1 g/dl Hgb, 95% CI 0.72–0.96, p=0.01). ConclusionWe did not confirm a previously reported association between admission Hgb and HE in patients with ICH, although Hgb and HE were both associated with poor outcome. These findings suggest that the association between Hgb and poor outcome is mediated by other factors.

Associations of Cerebral Small Vessel Disease on the Features of Hematoma and Hematoma Expansion in Intracerebral Hemorrhage.

Several early noncontrast CT (NCCT) signs of spontaneous intracerebral hemorrhage (ICH) can predict hematoma expansion (HE). However, the associations of underlying cerebral small vessel disease (SVD) on early NCCT signs and HE have been less explored. We conducted an analysis of all patients with spontaneous supratentorial ICH and received follow-up imaging between 2016 and 2020 at a stroke center. The early NCCT signs were categorized as shape or density signs. HE was defined as an increase in hematoma volume ≥6 mL or 33% from baseline. The severity of SVD was assessed by both a 3-point CT-based and a 4-point magnetic resonance imaging (MRI)-based SVD score. Regression models were used to examine the associations between SVD score and hematoma volume, NCCT signs, and HE. A total of 328 patients (median age: 64 years; 38% female) were included. The median baseline ICH volume was 8.6 mL, with 38% of the patients had shape signs and 52% had density signs on the initial NCCT. Higher MRI-SVD scores were associated with smaller ICH volumes (p = 0.0006), fewer shape (p = 0.001), or density signs (p = 0.0003). Overall, 16% of patients experienced HE. A higher MRI-SVD score was inversely associated with HE (adjusted odds ratio 0.71, 95% CI: 0.53-0.96). Subgroup analysis revealed that this association was primarily observed in patients who were younger (&lt;65 years), male, had deep hemorrhage, or did not meet the criteria for cerebral amyloid angiopathy diagnosis. In patients with spontaneous ICH, a more severe SVD was associated with smaller hematoma volume, fewer NCCT signs, and a lower risk of HE. Further research is required to investigate why a higher burden of severely diseased cerebral small blood vessels is associated with less bleeding.

Non-contrast computed tomography features predict intraventricular hemorrhage growth

ObjectivesNon-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth.MethodsPatients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models.ResultsA total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16–2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48–2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91–3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion.ConclusionsNCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies.Clinical relevance statementNon-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies.Key Points• NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences.• The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion.• Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.

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

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. 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. 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.

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

Rapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction. Original data was retrospectively obtained from three major prospective clinical trials ["Spot Sign" Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set. 317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE. NCCT-based radiological and radiomics features can improve HE prediction when added to clinical features.

A high neutrophil-to-platelet ratio is associated with hematoma expansion in patients with spontaneous intracerebral hemorrhage: a retrospective study

BackgroundEarly hematoma expansion (HE) occurs in 20 to 40% of spontaneous intracerebral hemorrhage (ICH) patients and is a primary determinant of early deterioration and poor prognosis. Previous studies have shown that inflammation is a major pathological feature of ICH, and the neutrophil-to-platelet ratio (NPR) is a marker of systemic inflammation. Therefore, we aimed to assess the association between the NPR and HE in ICH patients.MethodsWe retrospectively collected and analyzed data from ICH patients who received treatment at our institution from January 2018 to November 2019. The NPR was calculated from the admission blood test. Brain computed tomography (CT) scans were performed at admission and repeated within 24 h. Hematoma growth was defined as relative growth > 33% or absolute growth > 6 ml.ResultsA total of 317 patients were enrolled in our study. Multivariate logistic regression analysis indicated that the NPR was an independent predictor of HE [odds ratio (OR) = 1.742; 95% CI: 1.508–2.012, p < 0.001]. Receiver operating characteristic (ROC) curve analysis revealed that the NPR could predict HE, with an area under the curve of 0.838 (95% CI, 0.788–0.888, p < 0.001). The best predictive cut-off of the NPR for HE was 5.47 (sensitivity, 75.3%; specificity, 77.6%).ConclusionsA high NPR was associated with an increased risk of HE in patients with ICH.

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

BackgroundIrregular hematoma is considered as a risk sign of hematoma expansion. The aim of this study was to quantify hematoma irregularity with computed tomography based on 3D Slicer.MethodsPatients with spontaneous intracerebral hemorrhage who underwent an initial and subsequent non-contrast computed tomography (CT) at a single medical center between January 2019 to January 2020 were retrospectively identified. The Digital Imaging and Communication in Medicine (DICOM) standard images were loaded into the 3D Slicer, and the surface area (S) and volume (V) of hematoma were calculated. The hematoma irregularity index (HII) was defined as frac{sqrt{S/pi}}2/sqrt[3]{3V/4pi}times100. Logistic regression analyses and receiver operating characteristic (ROC) curve analysis were performed to assess predictive performance of HII.ResultsThe enrolled patients were divided into those with hematoma enlargement (n = 36) and those without the enlargement (n = 57). HII in hematoma expansion group was 130.4 (125.1–140.0), and the index in non-enlarged hematoma group was 118.6 (113.5-122.3). There was significant difference in HII between the two groups (P < 0.01). Multivariate logistic regression analysis revealed that the HII was significantly associated with hematoma expansion before (odds ratio = 1.203, 95% confidence interval [CI], 1.115–1.298; P < 0.001) and after adjustment for age, hematoma volume, Glasgow Coma Scale score (odds ratio = 1.196, 95% CI, 1.102–1.298, P < 0.001). The area under the ROC curve was 0.86 (CI, 0.78–0.93, P < 0.01), and the best cutoff of HII for predicting hematoma growth was 123.8.ConclusionAs a quantitative indicator of irregular hematoma, HII can be calculated using the 3D Slicer. And the HII was independently correlated with hematoma expansion.

Inhibition of the LRRC8A channel promotes microglia/macrophage phagocytosis and improves outcomes after intracerebral hemorrhagic stroke.

Promoting microglial/macrophage (M/Mφ) phagocytosis accelerates hematoma clearance and improves the prognosis of intracerebral hemorrhagic stroke (ICH). Cation channels such as Piezo1 modulate bacterial clearance by regulating M/Mφ. Whether LRRC8A, an anion channel, affects M/Mφ erythrophagocytosis and functional recovery after ICH was investigated here. We found that LRRC8A is highly expressed on M/Mφ in the perihematomal region of ICH mice. Conditional knockout of Lrrc8a in M/Mφ or treatment with an LRRC8A channel blocker accelerated hematoma clearance, reduced neuronal death, and improved functional recovery after ICH. Mechanistically, the LRRC8A channel inhibition promoted M/Mφ phagocytosis by activating AMP-activated protein kinase (AMPK), thereby inducing nuclear translocation of nuclear factor-erythroid 2 related factor 2 (Nrf2) and increasing Cd36 transcription. Our findings illuminate the regulation of M/Mφ phagocytosis by the LRRC8A channel via the AMPK-Nrf2-CD36 pathway after ICH, suggesting that LRRC8A is a potential target for hematoma clearance in ICH treatment.

Deep Learning-Based Prediction of Hematoma Expansion Using a Single Brain Computed Tomographic Slice in Patients With Spontaneous Intracerebral Hemorrhages

We aimed to predict hematoma expansion in intracerebral hemorrhage (ICH) patients by using the deep learning technique. We retrospectively collected data from ICH patients treated between May 2015 and May 2019. Head computed tomography (CT) scans were performed at admission, and 6 hours, 24 hours, and 72 hours after admission. CT scans were mandatory when neurologic deficits occurred. Univariate and multivariate analyses were conducted to illustrate the association between clinical variables and hematoma expansion. Convolutional neural network (CNN) was adopted to predict hematoma expansion based on brain CT slices. In addition, 5 machine learning methods, including support vector machine, multi-layer perceptron, naive Bayes, decision tree, and random forest, were also performed to predict hematoma expansion based on clinical variables for comparisons. A total of 223 patients were included. It was revealed that patients' older age (odds ratio [95% confidence interval]: 1.783 [1.417-1.924]), cerebral hemorrhage and breaking into the ventricle (2.524 [1.291-1.778]), coagulopathy (2.341 [1.677-3.454]), and baseline National Institutes of Health Stroke Scale (1.545 [1.132-3.203]) and Glasgow Coma Scale scores (0.782 [0.432-0.918]) independently associated with hematoma expanding. After 4-5 epochs, the CNN framework was well trained. The average sensitivity, specificity, and accuracy of CNN prediction are 0.9197, 0.8837, and 0.9058, respectively. Compared with 5 machine learning methods based on clinical variables, CNN can also achieve better performance. More than 90% of hematomas with or without expansion can be precisely classified by deep learning technology within this study, which is better than other methods based on clinical variables only. Deep learning technology could favorably predict hematoma expansion from non-contrast CT scan images.

Predicting hematoma expansion in intracerebral hemorrhage from brain CT scans via K-nearest neighbors matting and deep residual network

Hematoma Expansion (HE) in spontaneous IntraCerebral Hemorrhage (ICH) is one of the highest mortality rates in neurosurgery. It is also influenced by various factors such as hematoma volume, location, shape, and so on. Although it’s crucial to present a method to predict HE, there is no work on the prediction model using brain imaging for this task to our knowledge. Inspired by the above, this work proposes a method including preprocessing and classification to predict HE in ICH from brain computed tomography (CT) scans. A k-nearest neighbors matting method is adopted in the preprocessing of brain CT scans to remove the outer part of the skull and retain brain tissue features. A deep residual network is then presented to give classification results, which helps to learn from features of hematoma and other parts in brain tissue. Experimental results on 223 patients including 137 HE patients show that the proposed framework can achieve 0.890±0.020 accuracy, 0.880±0.033 specificity, 0.925±0.041 sensitivity, and 0.867±0.025 F1-Score. Compared with other state-of-the-art works that need a large number of clinic data, the proposed method can obtain better prediction performance using brain CT scans only.