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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage

PURPOSE

To develop and validate an end-to-end model for automatically predicting hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH) using a novel deep learning framework.

METHODS

This multicenter retrospective study collected cranial noncontrast computed tomography (NCCT) images of 490 patients with sICH at admission for model training (n = 236), internal testing (n = 60), and external testing (n = 194). A HE-Mind model was designed to predict HE, which consists of a densely connected U-net for segmentation process, a multi-instance learning strategy for resolving label ambiguity and a Siamese network for classification process. Two radiomics models based on support vector machine or logistic regression and two deep learning models based on residual network or Swin transformer were developed for performance comparison. Reader experiments including physician diagnosis mode and artificial intelligence mode were conducted for efficiency comparison.

RESULTS

The HE-Mind model showed better performance compared to the comparative models in predicting HE, with areas under the curve of 0.849 and 0.809 in the internal and external test sets respectively. With the assistance of the HE-Mind model, the predictive accuracy and work efficiency of the emergency physician, junior radiologist, and senior radiologist were significantly improved, with accuracies of 0.768, 0.789, and 0.809 respectively, and reporting times of 7.26 s, 5.08 s, and 3.99 s respectively.

CONCLUSIONS

The HE-Mind model could rapidly and automatically process the NCCT data and predict HE after sICH within three seconds, indicating its potential to assist physicians in the clinical diagnosis workflow of HE.

Swirl sign score system: a novel and practical tool for predicting hematoma expansion risk after spontaneous intracerebral haemorrhage

OBJECTIVE

To methodically analyse the swirl sign and construct a scoring system to predict the risk of hematoma expansion (HE) after spontaneous intracerebral haemorrhage (sICH).

METHODS

We analysed 231 of 683 sICH patients with swirl signs on baseline noncontrast CT (NCCT) images. The characteristics of the swirl sign were analysed, including the number, maximum diameter, shape, boundary, minimum CT value of the swirl sign, and the minimum distance from the swirl sign to the edge of the hematoma. In the development cohort, univariate and multivariate analyses were used to identify independent predictors of HE, and logistic regression analysis was used to construct the swirl sign score system. The swirl sign score system was verified in the validation cohort.

RESULTS

The number and the minimum CT value of the swirl sign were independent predictors of HE. The swirl sign score system was constructed (2 points for the number of swirl signs >1 and 1 point for the minimum CT value ≤41 Hounsfield units). The area under the curve of the swirl sign score system in predicting HE was 0.773 and 0.770 in the development and validation groups, respectively.

CONCLUSIONS

The swirl sign score system is an easy-to-use radiological grading scale that requires only baseline NCCT images to effectively identify subjects at high risk of HE.

ADVANCES IN KNOWLEDGE

Our newly developed semiquantitative swirl sign score system greatly improves the ability of swirl sign to predict HE.

Black hole sign migration in short-term brain CT scans: A possible link with clot evolution and histology

The black hole sign (BHS) is a rare radiological sign seen in the hyperacute phase of bleeding. It manifests within a hemorrhage in early hours, with limited studies exploring clot formation and evolution over a short duration. Despite various hypothesized mechanisms, the precise lifetime and dynamics of black hole sign development remain unclear. We describe the rare finding of a black hole sign within a deep brain hemorrhage, initially observed in the lateral portion of the clot during the first CT scan. Remarkably, in a subsequent CT scan, just 1 hour later, the BHS migrated towards the inner edge. Notably, while the hemorrhage size remained largely unchanged within this short timeframe, hyperacute bleeding led to increased perihematomal edema and sulci flattening. Histopathological features of the "evolving clot" are initially characterized by heightened cellularity. This increased cell density renders the hematoma less resistant to compressive forces, such as heightened endocranial pressure, offering a plausible explanation for the crushing and displacement of the BHS. Our study sheds light on the unique radiological progression of BHS within a deep brain ICH, emphasizing its association with dynamic clot formation and the consequential impact on surrounding structures.

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

DISCUSSION AND CONCLUSION

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

CLINICAL RELEVANCE STATEMENT

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

KEY POINTS

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

Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival

The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan-Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the "first-order energy" radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p < 0.0001), alongside age, National Institutes of Health Stroke Scale (NIHSS), and baseline International Normalized Ratio (INR). Using a Receiver Operating Characteristic (ROC) analysis, "the first-order energy" was a predictor of mortality at 1-week, 1-month, and 3-month post-ICH (all p < 0.0001), with Area Under the Curves (AUC) of >0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher "first-order energy" or very bright hematomas are associated with worse survival outcomes.

The relationship between perihematomal edema and hematoma expansion in acute spontaneous intracerebral hemorrhage: an exploratory radiomics analysis study

Background

The relationship between early perihematomal edema (PHE) and hematoma expansion (HE) is unclear. We investigated this relationship in patients with acute spontaneous intracerebral hemorrhage (ICH), using radiomics.

Methods

In this multicenter retrospective study, we analyzed 490 patients with spontaneous ICH who underwent non-contrast computed tomography within 6 h of symptom onset, with follow-up imaging at 24 h. We performed HE and PHE image segmentation, and feature extraction and selection to identify HE-associated optimal radiomics features. We calculated radiomics scores of hematoma (Radscores_HEA) and PHE (Radscores_PHE) and constructed a combined model (Radscore_HEA_PHE). Relationships of the PHE radiomics features or Radscores_PHE with clinical variables, hematoma imaging signs, Radscores_HEA, and HE were assessed by univariate, correlation, and multivariate analyses. We compared predictive performances in the training (n = 296) and validation (n = 194) cohorts.

Results

Shape_VoxelVolume and Shape_MinorAxisLength of PHE were identified as optimal radiomics features associated with HE. Radscore_PHE (odds ratio = 1.039, p = 0.032) was an independent HE risk factor after adjusting for the ICH onset time, Glasgow Coma Scale score, baseline hematoma volume, hematoma shape, hematoma density, midline shift, and Radscore_HEA. The areas under the receiver operating characteristic curve of Radscore_PHE in the training and validation cohorts were 0.808 and 0.739, respectively. After incorporating Radscore_PHE, the integrated discrimination improvements of Radscore_HEA_PHE in the training and validation cohorts were 0.009 (p = 0.086) and -0.011 (p < 0.001), respectively.

Conclusion

Radscore_PHE, based on Shape_VoxelVolume and Shape_MinorAxisLength of PHE, independently predicts HE, while Radscore_PHE did not add significant incremental value to Radscore_HEA.

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.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT

PURPOSE

To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning.

METHODS

Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated.

RESULTS

For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models.

CONCLUSION

The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network

PURPOSE

To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance.

METHODS

All 254 patients with primary cerebral hemorrhage from January 2017 to December 2022 in the General Hospital of the Western Theater Command were included. According to the criteria of hematoma enlargement exceeding 33% or the volume exceeding 6 ml, the patients were divided into the HE group and the hematoma non-enlargement (NHE) group. Multiple models and the 10-fold cross-validation method were used to screen the most valuable features and model the probability of predicting HE. The area under the curve (AUC) was used to analyze the prediction efficiency of each model for HE.

RESULTS

They were randomly divided into a training set of 204 cases in an 8:2 ratio and 50 cases of the test set. The clinical imaging deep feature joint model (22 features) predicted the area under the curve of HE as follows: clinical Navie Bayes model AUC 0.779, traditional radiology logistic regression (LR) model AUC 0.818, deep learning LR model AUC 0.873, and clinical NCCT deep learning multilayer perceptron model AUC 0.921.

CONCLUSION

The combined clinical imaging deep learning model has a high predictive effect for early HE in sICH patients, which is helpful for clinical individualized assessment of the risk of early HE in sICH patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features

Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.

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

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

Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage

OBJECTIVE

This study aimed to determine the predictive performance of non-contrast CT (NCCT) signs for hemorrhagic growth after intracerebral hemorrhage (ICH) when stratified by onset-to-imaging time (OIT).

MATERIALS AND METHODS

1488 supratentorial ICH within 6 h of onset were consecutively recruited from six centers between January 2018 and August 2022. NCCT signs were classified according to density (hypodensities, swirl sign, black hole sign, blend sign, fluid level, and heterogeneous density) and shape (island sign, satellite sign, and irregular shape) features. Multivariable logistic regression was used to evaluate the association between NCCT signs and three types of hemorrhagic growth: hematoma expansion (HE), intraventricular hemorrhage growth (IVHG), and revised HE (RHE). The performance of the NCCT signs was evaluated using the positive predictive value (PPV) stratified by OIT.

RESULTS

Multivariable analysis showed that hypodensities were an independent predictor of HE (adjusted odds ratio [95% confidence interval] of 7.99 [4.87-13.40]), IVHG (3.64 [2.15-6.24]), and RHE (7.90 [4.93-12.90]). Similarly, OIT (for a 1-h increase) was an independent inverse predictor of HE (0.59 [0.52-0.66]), IVHG (0.72 [0.64-0.81]), and RHE (0.61 [0.54-0.67]). Blend and island signs were independently associated with HE and RHE (10.60 [7.36-15.30] and 10.10 [7.10-14.60], respectively, for the blend sign and 2.75 [1.64-4.67] and 2.62 [1.60-4.30], respectively, for the island sign). Hypodensities demonstrated low PPVs of 0.41 (110/269) or lower for IVHG when stratified by OIT. When OIT was ≤ 2 h, the PPVs of hypodensities, blend sign, and island sign for RHE were 0.80 (215/269), 0.90 (142/157), and 0.83 (103/124), respectively.

CONCLUSION

Hypodensities, blend sign, and island sign were the best NCCT predictors of RHE when OIT was ≤ 2 h. NCCT signs may assist in earlier recognition of the risk of hemorrhagic growth and guide early intervention to prevent neurological deterioration resulting from hemorrhagic growth.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Objective

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

Methods

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

Results

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

Conclusion

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

[CT-predictors of unfavorable outcome in hemorrhagic stroke]

OBJECTIVE

To analyze the relationship between the changes on non-contrast CT of the head in patients with acute hemorrhagic stroke and its unfavorable outcome within 90 days.

MATERIAL AND METHODS

The retrospective analysis of the clinical, demographic parameters and results of CT of the head of all patients admitted to the stroke unit of the district hospital between January 2015 and December 2021 was performed. The data of 131 patients were included in the work (52% males), average age was 65.75±14.1.

RESULTS

Fatal outcomes were recorded in 13.7% of cases. The age of the patient, severity of neurological deficit and CT-signs predicting hematoma expansion were independent predictors of unfavorable outcomes of hemorrhagic stroke within 90 days.

CONCLUSION

Detection of the sings predicting hematoma enlargement on CT scans improves prognostication of the outcomes of hemorrhagic stroke.

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.

Hematoma Enlargement After Intracerebral Hemorrhage: A Bibliometric Analysis

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage

OBJECTIVES

To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.

METHODS

A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS).

RESULTS

A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively).

CONCLUSIONS

Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage.

CLINICAL RELEVANCE STATEMENT

The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage.

KEY POINTS

• Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Non-contrast computed tomography features predict intraventricular hemorrhage growth

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL RELEVANCE STATEMENT

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

KEY POINTS

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

Intensive Blood Pressure Reduction is Associated with Reduced Hematoma Growth in Fast Bleeding Intracerebral Hemorrhage

OBJECTIVE

Patients with spontaneous intracerebral hemorrhage (ICH) at the highest risk of hematoma growth are those with the most potential to benefit from anti-expansion treatment. Large clinical trials have not definitively shown a clear benefit of blood pressure (BP) reduction. We aim to determine whether intensive blood pressure reduction could benefit patients with fast bleeding ICH.

METHODS

An exploratory analysis of data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 (ATACH-2) randomized controlled trial was performed. In order to capture not just early bleeding (even if a small amount), but the rate of bleeding (ml/hour), we restricted the study to "Fast bleeding ICH," defined as an ICH volume/onset to computed tomography (CT) time >5 ml/hr. Hematoma growth, as defined as an increase of hematoma volume > 33% between baseline and 24 hours.

RESULTS

A total of 940 patients were included (mean age = 62.1 years, 61.5% men), of whom 214 (22.8%) experienced hematoma expansion. Of these, 567 (60.3%) met the definition of "fast bleeding" with baseline ICH volume/time to presentation of at least 5 ml/hr. Intensive BP reduction was associated with a significantly lower rate of hematoma growth in fast bleeding patients (20.6% vs 31.0%, p = 0.005). In a subgroup of 266 (46.9%) fast-bleeding patients who received treatment within 2 hours after symptom onset, intensive BP lowering was associated with improved functional independence (odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.06-3.69, p = 0.031).

INTERPRETATION

Our results suggest that early use of intensive BP reduction may reduce hematoma growth and improve outcome in fast bleeding patients. ANN NEUROL 2023.

Development of Cloud-Based Telemedicine Platform for Acute Intracerebral Hemorrhage in Gangwon-do : Concept and Protocol

We aimed to develop a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local hospitals in rural and underserved areas in Gangwon-do using artificial intelligence and non-face-to-face collaboration treatment technology. This is a prospective and multi-center development project in which neurosurgeons from four university hospitals in Gangwondo will participate. Information technology experts will verify and improve the performance of the cloud-based telemedicine collaboration platform while treating ICH patients in the actual medical field. Problems identified will be resolved, and the function, performance, security, and safety of the telemedicine platform will be checked through an accredited certification authority. The project will be carried out over 4 years and consists of two phases. The first phase will be from April 2022 to December 2023, and the second phase will be from April 2024 to December 2025. The platform will be developed by dividing the work of the neurosurgeons and information technology experts by setting the order of items through mutual feedback. This article provides information on a project to develop a cloud-based telemedicine platform for acute ICH patients in Gangwon-do.

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.

Early Detection of Underlying Cavernomas in Patients with Spontaneous Acute Intracerebral Hematomas

BACKGROUND AND PURPOSE

Early identification of the etiology of spontaneous acute intracerebral hemorrhage is essential for appropriate management. This study aimed to develop an imaging model to identify cavernoma-related hematomas.

MATERIALS AND METHODS

Patients 1-55 years of age with acute (≤7 days) spontaneous intracerebral hemorrhage were included. Two neuroradiologists reviewed CT and MR imaging data and assessed the characteristics of hematomas, including their shape (spherical/ovoid or not), their regular or irregular margins, and associated abnormalities including extralesional hemorrhage and peripheral rim enhancement. Imaging findings were correlated with etiology. The study population was randomly split to provide a training sample (50%) and a validation sample (50%). From the training sample, univariate and multivariate logistic regression was performed to identify factors predictive of cavernomas, and a decision tree was built. Its performance was assessed using the validation sample.

RESULTS

Four hundred seventy-eight patients were included, of whom 85 had hemorrhagic cavernomas. In multivariate analysis, cavernoma-related hematomas were associated with spherical/ovoid shape (P < .001), regular margins (P = .009), absence of extralesional hemorrhage (P = .01), and absence of peripheral rim enhancement (P = .002). These criteria were included in the decision tree model. The validation sample (n = 239) had the following performance: diagnostic accuracy of 96.1% (95% CI, 92.2%-98.4%), sensitivity of 97.95% (95% CI, 95.8%-98.9%), specificity of 89.5% (95% CI, 75.2%-97.0%), positive predictive value of 97.7% (95% CI, 94.3%-99.1%), and negative predictive value of 94.4% (95% CI, 81.0%-98.5%).

CONCLUSIONS

An imaging model including ovoid/spherical shape, regular margins, absence of extralesional hemorrhage, and absence of peripheral rim enhancement accurately identifies cavernoma-related acute spontaneous cerebral hematomas in young patients.

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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 (<65 years), male, had deep hemorrhage, or did not meet the criteria for cerebral amyloid angiopathy diagnosis.

CONCLUSIONS

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.

NCCT Markers of Intracerebral Hemorrhage Expansion Using Revised Criteria: An External Validation of Their Predictive Accuracy

BACKGROUND AND PURPOSE

Several NCCT expansion markers have been proposed to improve the prediction of hematoma expansion. We retrospectively evaluated the predictive accuracy of 9 expansion markers.

MATERIALS AND METHODS

Patients admitted for intracerebral hemorrhage within 24 hours of last seen well were retrospectively included from April 2016 to April 2020. The primary outcome was revised hematoma expansion, defined as any of a ≥6-mL or ≥33% increase in intracerebral hemorrhage volume, a ≥ 1-mL increase in intraventricular hemorrhage volume, or de novo intraventricular hemorrhage. We assessed the predictive accuracy of expansion markers and determined their association with revised hematoma expansion.

RESULTS

We included 124 patients, of whom 51 (41%) developed revised hematoma expansion. The sensitivity of each marker for the prediction of revised hematoma expansion ranged from 4% to 78%; the specificity, 37%-97%; the positive likelihood ratio, 0.41-7.16; and the negative likelihood ratio, 0.49-1.06. By means of univariable logistic regressions, 5 markers were significantly associated with revised hematoma expansion: black hole (OR = 8.66; 95% CI, 2.15-58.14; P = .007), hypodensity (OR = 3.18; 95% CI, 1.49-6.93; P = .003), blend (OR = 2.90; 95% CI, 1.08-8.38; P = .04), satellite (OR = 2.84; 95% CI, 1.29-6.61; P = .01), and Barras shape (OR = 2.41, 95% CI; 1.17-5.10; P = .02). In multivariable models, only the black hole marker remained independently associated with revised hematoma expansion (adjusted OR = 5.62; 95% CI, 1.23-40.23; P = .03).

CONCLUSIONS

No single NCCT expansion marker had both high sensitivity and specificity for the prediction of revised hematoma expansion. Improved image-based analysis is needed to tackle limitations associated with current NCCT-based expansion markers.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Current Evaluation of Intracerebral Hemorrhage

Advanced imaging is currently critical in diagnosing, predicting, and managing intracerebral hemorrhage. MD CT angiography has occupied the first line of evaluating patients with a clinical diagnosis of a stroke, given its rapid acquisition time, high resolution of vascular structures, and sensitivity for secondary causes of 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.

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks

OBJECTIVES

This study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks.

METHODS

A total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes.

RESULTS

The ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001).

CONCLUSIONS

The dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema

PURPOSE

The aim is to explore the potential value of CT-based radiomics in predicting perihematomal edema (PHE) volumes after acute intracerebral hemorrhage (ICH) from admission to 24 h.

METHODS

A total of 231 patients newly diagnosed with acute ICH at two institutes were analyzed retrospectively. The patients were randomly divided into training (N = 117) and internal validation cohort (N = 45) from institute 1 with a ratio of 7:3. According to radiomics features extracted from baseline CT, the radiomics signatures were constructed. Multiple logistic regression analysis was used for clinical radiological factors and then the nomogram model was generated to predict the extent of PHE according to the optimal radiomics signature and the clinical radiological factors. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination performance. The calibration curve and Hosmer-Lemeshow test were used to evaluate the consistency between the predicted and actual probability. The support vector regression (SVR) model was constructed to predict the overall value of follow-up PHE. The performance of the models was evaluated on the internal and independent validation cohorts.

RESULTS

The perihematoma 5 mm radiomics signature (AUC: 0.875) showed good ability to discriminate the small relative PHE(rPHE) from large rPHE volumes, comparing to intrahematoma radiomics signature (AUC: 0.711) or perihematoma 10 mm radiomics signature (AUC: 0.692) on the training cohort. The AUC of the combined nomogram model was 0.922 for the training cohort, 0.945 and 0.902 for the internal and independent validation cohorts, respectively. The calibration curves and Hosmer-Lemeshow test of the nomogram model suggested that the predictive performance and actual outcome were in favorable agreement. The SVR model also predicted the overall value of follow-up rPHE (root mean squared error, 0.60 and 0.45; Pearson correlation coefficient, 0.73 and 0.68; P < 0.001).

CONCLUSION

Among patients with acute ICH, the established nomogram and SVR model with favorable performance can offer a noninvasive tool for the prediction of PHE after ICH.

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.

Imaging of Central Nervous System Hemorrhage

OBJECTIVE

This article aims to familiarize the reader with the various types of nontraumatic central nervous system (CNS) hemorrhage and the various neuroimaging modalities used to help diagnose and manage them.

LATEST DEVELOPMENTS

According to the 2019 Global Burden of Diseases, Injuries, and Risk Factors Study, intraparenchymal hemorrhage accounts for 28% of the global stroke burden. In the United States, hemorrhagic stroke makes up 13% of all strokes. The incidence of intraparenchymal hemorrhage increases substantially with age; thus, despite improvements in blood pressure control through various public health measures, the incidence is not decreasing as the population ages. In fact, in the most recent longitudinal study of aging, autopsy findings showed intraparenchymal hemorrhage and cerebral amyloid angiopathy in 30% to 35% of patients.

ESSENTIAL POINTS

Rapid identification of CNS hemorrhage, which includes intraparenchymal hemorrhage, intraventricular hemorrhage, and subarachnoid hemorrhage, requires either head CT or brain MRI. Once hemorrhage is identified on the screening neuroimaging study, the pattern of blood in conjunction with the history and physical examination can guide subsequent neuroimaging, laboratory, and ancillary tests as part of the etiologic assessment. After determination of the cause, the chief aims of the treatment regimen are reducing hemorrhage expansion and preventing subsequent complications such as cytotoxic cerebral edema, brain compression, and obstructive hydrocephalus. In addition, nontraumatic spinal cord hemorrhage will also be briefly discussed.

Multilesion Segmentations in Patients with Intracerebral Hemorrhage: Reliability of ICH, IVH and PHE Masks

Background and Purpose: Fully automated methods for segmentation and volume quantification of intraparenchymal hemorrhage (ICH), intraventricular hemorrhage extension (IVH), and perihematomal edema (PHE) are gaining increasing interest. Yet, reliabilities demonstrate considerable variances amongst each other. Our aim was therefore to evaluate both the intra- and interrater reliability of ICH, IVH and PHE on ground-truth segmentation masks. Methods: Patients with primary spontaneous ICH were retrospectively included from a German tertiary stroke center (Charité Berlin; January 2016−June 2020). Baseline and follow-up non-contrast Computed Tomography (NCCT) scans were analyzed for ICH, IVH, and PHE volume quantification by two radiology residents. Raters were blinded to all demographic and outcome data. Inter- and intrarater agreements were determined by calculating the Intraclass Correlation Coefficient (ICC) for a randomly selected set of patients with ICH, IVH, and PHE. Results: 100 out of 670 patients were included in the analysis. Interrater agreements ranged from an ICC of 0.998 for ICH (95% CI [0.993; 0.997]), to an ICC of 0.979 for IVH (95% CI [0.984; 0.993]), and an ICC of 0.886 for PHE (95% CI [0.760; 0.938]), all p-values < 0.001. Intrarater agreements ranged from an ICC of 0.997 for ICH (95% CI [0.996; 0.998]), to an ICC of 0.995 for IVH (95% CI [0.992; 0.996]), and an ICC of 0.980 for PHE (95% CI [0.971; 0.987]), all p-values < 0.001. Conclusion Manual segmentations of ICH, IVH, and PHE demonstrate good-to-excellent inter- and intrarater reliabilities, with the highest agreement for ICH and IVH and lowest for PHE. Therefore, the degree of variances reported in fully automated quantification methods might be related amongst others to variances in ground-truth masks.

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].

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

A 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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Background

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

Methods

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

Results

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

Conclusion

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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