Enhancing prediction of basal ganglia hemorrhage expansion: a radiomic approach with texture analysis in computed tomography

Objectives Early hematoma expansion is a major determinant of poor outcome in hypertensive basal ganglia hemorrhage. This study evaluated whether CT-based radiomic texture analysis could improve early prediction of hematoma expansion. Methods A retrospective cohort of 104 patients with hypertensive basal ganglia hemorrhage who underwent baselinef CT within 6 h of symptom onset and follow-up CT within 48 h was analyzed. Hematoma regions of interest were manually segmented, and 256 texture features were extracted using MaZda. Fisher’s score, probability of error and average correlation coefficient, and mutual information were used for dimensionality reduction. Classification performance was assessed using raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis, followed by ROC analysis. Results Hematoma expansion occurred in 40 of 104 patients (38.4 %). Nonlinear discriminant analysis showed the lowest misclassification rate overall, including 0 under the POE-ACC feature set. ROC analysis demonstrated good diagnostic performance for several texture features, with S(3, −3)Difvarnc (AUC 0.944), S(4, −4)Difvarnc (AUC 0.942), and GrVariance (AUC 0.917) showing the strongest predictive value. Conclusions CT-based texture analysis provides quantitative imaging biomarkers that may support early risk stratification of hematoma expansion in basal ganglia hemorrhage.