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Updated: Oct 1, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Computed tomography and clinical parameters predict intracerebral hemorrhage expansion
1Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China.
Abstract:
This study aimed to evaluate the association of imaging signs, and to establish a predictive model through selecting highly relevant imaging signs in combination with clinical parameters for hematoma expansion.Intracerebral Hemorrhage (ICH) patients who received 2 consecutive noncontrast computed tomography scans were examined and recruited through January 2014 to December 2020. Demographic information and clinical characteristics were collected. Two experienced radiologists reviewed baseline noncontrast computed tomography images to assess the imaging characteristics. Correlation analysis was analyzed with Pearson and Spearman correlation tests. The association between clinical and imaging predictors with hematoma expansion was evaluated in multivariate models. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate predictive performance.A total of 232 ICH patients, with mean age of 59.73 years, and 31% of female were included, among which, 32 patients occurred with hematoma expansion. For sex, ICH density, low density in hematoma, the midline shift, and Glasgow Coma Scale score, liquid level, H-tra, edema Cor, H Volume, time from onset to examination, there were significant differences between the 2 groups. As for imaging signs, only blend sign showed a significant difference, that patients with blend sign had a higher incidence of ICH expansion. The logistic analysis found that radiation attenuation, liquid level, the midline shift, Glasgow Coma Scale score, history of ischemic stroke, and smoking could predict the occurrence of ICH expansion.In summary, the model combined radiological characteristics with clinical indicators showed considerable predictive performance. Further validation is needed to verify the findings and help transfer to clinical practice.
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