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"Computed Tomography Perihematomal Rims": A Perihematomal Low-Density Area Is a Part of an Acute Brain Hemorrhage.

ABSTRACT: Objective Computed tomography (CT) can be used for visualizing acute intracerebral hemorrhages (ICHs) as distinct hyperdense areas and cerebral edema as perihematomal low-density areas (LDAs). We observed a perihematomal LDA on CT, which appeared to be part of a hemorrhage on magnetic resonance imaging (MRI) in acute ICH. We named this "CT perihematomal rim" and evaluated its characteristics and clinical significance. Methods We stratified patients with acute ICH according to the presence or absence of a CT perihematomal rim and then compared their radiologic findings. Logistic regression analyses were performed to assess whether the CT findings can predict the presence of a CT perihematomal rim. Patients Patients within 24 hours of ICH onset who were admitted between September 1, 2014, and October 31, 2018, were registered. Results Overall, 139 patients (91 men; mean age, 66 years) were investigated. CT perihematomal rims were observed in 40 patients (29%). ICH volumes on CT were 30% smaller than those on MRI in patients with CT perihematomal rims. On a multivariate analysis, the presence of a CT perihematomal rim was independently associated with the maximum diameter of the perihematomal LDA. According to a receiver operating characteristic analysis, the maximum LDA diameter threshold was 7.5 mm (sensitivity, 85%; specificity, 83%). Conclusion CT perihematomal rims were observed in 29% of the patients with acute ICH. A perihematomal LDA (>7.5 mm) in acute ICH cases should be considered a CT perihematomal rim. Clinicians should be aware that the ICH volume on CT may be underestimated by 30%.

SUBMITTER: Sato T

PROVIDER: S-EPMC8381191 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionRadiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.MethodsNon-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases.ResultsThe hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70.DiscussionAn open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.

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

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"Computed Tomography Perihematomal Rims": A Perihematomal Low-Density Area Is a Part of an Acute Brain Hemorrhage.

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