A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features

Standard b-value DWI-derived stiffness index analysis may provide a way to evaluate the development of intracerebral hematoma

Background and purpose

The development of intracerebral hemorrhage (ICH) is closely related to mechanical forces. However, noninvasively evaluating mechanical forces for ICH patients in the current clinical setting is challenging. In this study, we aimed to build an easily accessible stiffness index (STI) and evaluate the stiffness of the perihematomal edema (PHE) region in ICH patients.

Materials and methods

In this retrospective study, two cohorts of 57 patients were included. One cohort (the exploratory cohort) comprised patients with both standard b-value diffusion-weighted imaging (sDWI) (b-values of 0 and 1,000 s/mm2, b0 and b1000) and higher b-value diffusion-weighted imaging (hDWI) (b-values of 200 and 1,500 s/mm2). Another cohort (the hemorrhage cohort) consisted of patients who were diagnosed with ICH and who underwent sDWI within 48 h from onset. The hDWI-based virtual shear modulus (μdiff) was calculated and correlated with the sDWI data in the exploratory cohort. In the hemorrhage cohort, STI maps that were used to estimate μdiff were generated. The mean STI (mSTI) and coefficient of variation (COV) of the STI were computed on the basis of the STI maps in the whole and largest-slice PHE regions.

Results

The STI could be calculated with the Equation 0.047697*S1000-0.022944*S0 + 5.359883, where S1000 and S0 represent the signal intensities of the b1000 and b0 images, respectively. In the whole and largest-slice PHE regions, both the mSTI and COV were correlated with the hematoma volume (p < 0.01), but neither were correlated with the time from onset.

Conclusion

The standard b-value DWI-derived stiffness index analysis may provide a noninvasive and easily accessible way to evaluate the development of ICH.

Morphological characteristics of CT blend sign predict hematoma expansion and outcomes in intracerebral hemorrhage in elderly patients

Background and purpose

The underlying basis of the blend sign on brain computed tomography (CT) in patients with intracerebral hemorrhage (ICH) is unclear. Few studies have examined the morphological alterations in the CT blend sign in ICH. Therefore, we assessed changes in the CT blend sign and their association with hematoma expansion (HE) and adverse outcomes in ICH patients.

Methods

We recorded the clinical and radiographic parameters of patients with ICH and blend sign on brain CT. The patients were categorized into two groups, with changes in the relatively hypoattenuating region of the blend sign (CHB group) and with no changes in the relatively hypoattenuating region of the blend sign (NHB groups). We performed univariate and multivariate logistic regression analyses to examine the correlations between CHB and HE and poor outcomes. Furthermore, receiver operating characteristic curve analysis was used to confirm the predictive power of CHB.

Results

In total, 183 patients were included in the study, of whom 74 (40.4%) demonstrated changes in the hypoattenuating region of the blend sign, whereas 109 (59.6%) did not. Compared with the NHB group, patients in the CHB group exhibited significantly higher levels of HE and adverse outcomes. After adjustment for confounding factors, CHB was independently associated with HE (odds ratio, 19.401 [95% CI, 7.217-52.163]; p < 0.001) and poor 3-month outcomes (odds ratio, 2.638 [95% CI, 1.391-5.003]; p = 0.003) in the multivariate analysis. The sensitivity, specificity, positive predictive value, and negative predictive value of CHB for predicting HE were 0.877, 0.768, 0.791, and 0.862, respectively, whereas these values for predicting poor outcomes were 0.789, 0.641, 0.688, and 0.752, respectively.

Conclusion

Changes of a hypoattenuating region within the blend sign have good predictive accuracy for HE and short-term adverse outcomes in elderly patients with ICH.

Clinical trial registration

ClinicalTrials.gov, NCT05548530.

Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview

Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.

A convenient scoring system to distinguish intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis based on magnetic resonance imaging features

OBJECTIVES

To develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM).

METHODS

A total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM.

RESULTS

Six independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score.

CONCLUSIONS

The established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features.

CLINICAL RELEVANCE STATEMENT

A reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features.

KEY POINTS

• Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). • A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

Prior knowledge-based precise diagnosis of blend sign from head computed tomography

Introduction

Automated diagnosis of intracranial hemorrhage on head computed tomography (CT) plays a decisive role in clinical management. This paper presents a prior knowledge-based precise diagnosis of blend sign network from head CT scans.

Method

We employ the object detection task as an auxiliary task in addition to the classification task, which could incorporate the hemorrhage location as prior knowledge into the detection framework. The auxiliary task could help the model pay more attention to the regions with hemorrhage, which is beneficial for distinguishing the blend sign. Furthermore, we propose a self-knowledge distillation strategy to deal with inaccuracy annotations.

Results

In the experiment, we retrospectively collected 1749 anonymous non-contrast head CT scans from the First Affiliated Hospital of China Medical University. The dataset contains three categories: no intracranial hemorrhage (non-ICH), normal intracranial hemorrhage (normal ICH), and blend sign. The experimental results demonstrate that our method performs better than other methods.

Discussion

Our method has the potential to assist less-experienced head CT interpreters, reduce radiologists' workload, and improve efficiency in natural clinical settings.