Posttraumatic epilepsy (PTE), or recurrent seizures after traumatic brain injury (TBI), is a debilitating complication of TBI. We present a multimodal approach to classify seizure outcomes using computed tomography (CT) and magnetic resonance imaging (MRI) features that characterize lesion phenotypes. Five logistic regression models to predict seizure outcome are presented, using patient demographics and clinical information in conjunction with CT and MRI variables that describe lesion characteristics including contusion type and location as well as whole-brain lesion volumetrics. The optimal model utilized all four categories of features, yielding 91.4% sensitivity, 75% specificity, and 0.886 area under the curve performance.
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