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.

A comprehensive prediction model predicts perihematomal edema growth in the acute stage after intracerebral hemorrhage.

BACKGROUND: Perihematomal edema (PHE) is regarded as a potential intervention indicator of secondary injury following intracerebral hemorrhage (ICH). But it still lacks a comprehensive prediction model for early PHE formation. METHODS: The included ICH patients have received an initial Computed Tomography scan within 6 hours of symptom onset. Hematoma volume and PHE volume were computed using semiautomated computer-assisted software. The volume of the hematoma, edema around the hematoma, and surface area of the hematoma were calculated. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte cell count. All analyses were 2-tailed, and the significance level was determined by P <0.05. RESULTS: A total of 226 patients were included in the final analysis. The optimal cut-off values for PHE volume increase to predict poor outcomes were determined as 5.5 mL. For clinical applicability, we identified a value of 5.5 mL as the optimal threshold for early PHE growth. In the multivariate logistic regression analyses, we finally found that baseline hematoma surface area (p < 0.001), expansion-prone hematoma (p < 0.001), and PLR (p = 0.033) could independently predict PHE growth. The comprehensive prediction model demonstrated good performance in predicting PHE growth, with an area under the curve of 0.841, sensitivity of 0.807, and specificity of 0.732. CONCLUSION: In this study, we found that baseline hematoma surface area, expansion-prone hematoma, and PLR were independently associated with PHE growth. Additionally, a risk nomogram model was established to predict the PHE growth in patients with ICH.

Effects of a Soluble Epoxide Hydrolase Inhibitor on Lipopolysaccharide-Induced Acute Lung Injury in Mice.

OBJECTIVES: Inflammation plays a key role in the pathogenesis of acute lung injury (ALI). Soluble epoxide hydrolase (sEH) is suggested as a vital pharmacologic target for inflammation. In this study, we determined whether a sEH inhibitor, AUDA, exerts lung protection in lipopolysaccharide (LPS)-induced ALI in mice. METHODS: Male BALB/c mice were randomized to receive AUDA or vehicle intraperitoneal injection 4 h after LPS or phosphate buffered saline (PBS) intratracheal instillation. Samples were harvested 24 h post LPS or PBS administration. RESULTS: AUDA administration decreased the pulmonary levels of monocyte chemoattractant protein (MCP)-1 and tumor necrosis factor (TNF)-α. Improvement of oxygenation and lung edema were observed in AUDA treated group. AUDA significantly inhibited sEH activity, and elevated epoxyeicosatrienoic acids (EETs) levels in lung tissues. Moreover, LPS induced the activation of nuclear factor (NF)-κB was markedly dampened in AUDA treated group. CONCLUSION: Administration of AUDA after the onset of LPS-induced ALI increased pulmonary levels of EETs, and ameliorated lung injury. sEH is a potential pharmacologic target for ALI.