Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement

We developed radiomics-based predictive models to assess the likelihood of progressively refractory intracranial hypertension leading to secondary DC in a selected cohort of TBI patients who had undergone emergent craniotomy for hematoma evacuation with bone flap replacement. The model relying solely on radiomic features extracted from the lesion demonstrated satisfactory performance. When these features were integrated with demographic and clinical data to create a multiomic model, predictive performance further improved. These findings highlight the model's potential to identify high-risk patients, enabling early intervention to prevent further deterioration.