Recognizing and Preventing Intracerebral Hematoma Expansion

Caplan, Louis R.

doi:10.1001/jamaneurol.2016.1899

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…The computed tomography angiography (CTA) spot sign, as an independent predictor, has been well established and prospectively validated, which turned out to be of limited sensitivity (Demchuk et al, 2012;Dowlatshahi et al, 2016). Besides that, CTA has not yet been a routine measurement for emergency radiology in some institutions, following nephrotoxicity and allergy problems (Caplan, 2016). Rather, several novel markers based on noncontrast CT (NCCT), including blend sign, black hole sign, swirl sign, island sign, and satellite sign, recently gained attention, mainly focused on the heterogeneity and the irregularity of hematoma (Boulouis et al, 2017;Shimoda et al, 2017;Morotti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

Ding

Shan

et al. 2020

Front. Neurosci.

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Background: We aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion.Methods: A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal-Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed via a calibration curve. Decision curve analysis (DCA) and Kaplan-Meier (KM) survival analysis were performed to evaluate the clinical value of the model.Results: Four optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson's r: 0.451). The nomogram model showed the best performance with high area under the curves in both training cohort (0.881, sensitivity: 0.973; specificity: 0.787) and external validation cohort (0.857, sensitivity: 0.950; specificity: 0.766). The calibration curve, DCA, and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction. Conclusion:A nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.

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Section: Introductionmentioning

confidence: 99%

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

Ding

Shan

et al. 2020

Front. Neurosci.

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“…The computed tomography angiography (CTA) spot sign is regarded as a useful predictor for HE and poor functional outcomes (13)(14)(15)(16)(17). However, CTA is not routinely used in many institutions (18). Recently, several NCCT markers have shown the ability to predict HE (19), such as the blend sign (BS) (20), black hole sign (BHS) (21), satellite sign (22), and island sign (23).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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

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“…Previous studies have proposed several imaging predictors to identify hematoma expansion. CTA spot sign, leakage sign, and spot-and-tail sign are some of the proven independent predictors of hematoma enlargement (Goldstein et al, 2007 ; Wada et al, 2007 ; Sorimachi et al, 2013 ; Caplan, 2016 ; Orito et al, 2016 ). However, all of the above imaging markers have a common shortcoming of requiring initial CTA examination.…”

Section: Discussionmentioning

confidence: 99%

“…Noncontrast computed tomography (NCCT) plays an extremely important role in the diagnosis and treatment of hematoma enlargement of the intracerebral hemorrhage. Past studies indicate that computed tomographic angiography (CTA) spot sign is an independent risk factor for hematoma enlargement (Goldstein et al, 2007 ; Wada et al, 2007 ; Demchuk et al, 2012 ; Brouwers et al, 2015 ; Caplan, 2016 ). However, due to the high requirements of emergency CTA for patients and the burden on hospitals, there are significant limitations in the application of CTA for early detection.…”

Section: Introductionmentioning

confidence: 99%

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

Teng

Ren

Zhang³

et al. 2021

Front. Aging Neurosci.

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

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Recognizing and Preventing Intracerebral Hematoma Expansion

Cited by 10 publications

References 16 publications

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

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

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

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