CT-based radiomic markers to predict late-onset seizures after traumatic brain injury

Abstract

Post-traumatic epilepsy (PTE) is a major long-term complication of traumatic brain injury (TBI), but early risk prediction remains imprecise. Radiomics enables quantitative analysis of subtle abnormalities on non-contrast head CT (NCCT) that are not readily visible on routine imaging and may improve early risk stratification. This pilot study assessed the performance of radiomic features from acute NCCT, alone or combined with clinical variables, to predict late post-traumatic seizures (PTS) within six months of injury, an early marker of PTE. Eighty-two patients with TBI were included, and two machine-learning approaches were employed: a radiomics-only model and a clinically augmented model incorporating demographics, admission Glasgow Coma Scale (GCS), and prophylactic antiseizure medication use. Radiomics-only models showed moderate discrimination in nested cross-validation (logistic regression AUC = 0.719). Frequently selected features reflected frontal and temporal lobe asymmetry and regional heterogeneity. Adding clinical variables significantly improved performance across all models. The best model, a clinically augmented logistic regression, achieved an AUC of 0.842 with improved accuracy, precision, recall, and F1 score. Admission GCS and antiseizure prophylaxis were the most influential clinical predictors. The findings of this pilot study support NCCT-based radiomics combined with clinical data as a promising framework to be further validated for early PTE risk stratification.