Quantitative Volumetric Computed Tomography Density Predicts Basal Ganglia Hemorrhage Expansion and Enhances Spot Sign Diagnostic Accuracy
Ahmed Kashkoush, Robert Winkelman, Rebecca Achey, Mark A. Davison, Varun R. Kshettry, Nina Moore, Catherine E. Hassett, Joao Gomes, Mark BainBACKGROUND AND OBJECTIVES:
Identifying patients with basal ganglia intracranial hemorrhage (ICH) at risk of hematoma expansion (HE) may better define selection criteria for early surgical evacuation. The aim of this study was to use automated radiographic feature extraction to improve risk stratification for basal ganglia ICH expansion.
METHODS:
A single-center retrospective review was performed to identify patients with basal ganglia ICH between 2013 and 2024. ICH volumes were automatically segmented from the initial noncontrast computed tomography (CT) of the head using a custom-trained convolutional neural network. Features were quantified from the segmented ICH including stereotactic location, normalized volumetric CT density (nv-CTD, measured as mean ICH CT density divided by the background parenchymal CT density), volume, orientation, and border irregularity. HE was defined as an increase in hemorrhage volume of 10 mL or at a rate of 1.7 mL/h.
RESULTS:
A total of 108 patients (median age 55 years, 62% male) were included. HE occurred in 24 patients (22%) and was associated with shorter duration between symptom onset and initial CT (median 1 vs 3 hours,
CONCLUSION:
nv-CTD is a measure of bgICH acuity and can augment spot-sign bgICH expansion risk stratification.