Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival

Abstract

The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan–Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the “first-order energy” radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p p 0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher “first-order energy” or very bright hematomas are associated with worse survival outcomes.

Keywords

• stroke
• large vessel occlusion
• radiomics
• machine learning
• collateral status