Background: Acute subdural hematoma is a critical condition, leading to significant morbidity and mortality. Despite advancements in surgical techniques, a portion of patients only show limited clinical improvement post-evacuation. Surgical intervention decisions are critically important, as they can either improve or worsen a patient’s condition. Radiomics offers significant potential by extracting complex patterns from digital medical images and transforming them into high-dimensional data that reflect the underlying pathophysiology. By integrating Radiomics with individual patient characteristics, we can develop decision support models. This study aims to analyze radiomic parameters of aSDH to determine whether they support the decision to proceed with urgent surgery or opt for a conservative approach. We hypothesized that surface area could be a significant predictor of neurological outcome such as maintaining independent mobility (mRS ≥ 3) and survival rates. Methods: This retrospective study involved radiomic analysis according to neurological outcome and survival. Radiomic parameters were measured using 3D Slicer software. Statistical analyses explored correlations, employing AUC-analysis and Kaplan-Meier survival. Results: Our findings revealed significant correlations between hematoma and surface area with poorer neurological prognosis. Further subgroup analysis showed surface area as a significant predictor for poorer outcomes in patients undergoing craniotomy (p = 0.006 in univariant- and p = 0.020 in multivariant analysis). In the total cohort, among conservatively managed and craniotomy subgroups, survival analysis highlighted an advantageous survival for patients exhibiting smaller surface areas (< 339.50 cm²). Conclusions: Especially in craniotomy patients, surface area emerged as a possible predictor for neurological outcome and survival.

Background: Acute subdural hematoma (aSDH) is associated with a high risk of epilepsy, a complication linked to poor outcomes. Craniotomy is a known risk factor, with an epilepsy incidence of approximately 25%. This study evaluated radiomic features from preoperative CT scans to predict epilepsy risk in aSDH patients undergoing craniotomy. Methods: A retrospective analysis of 178 adult aSDH patients treated between 2016 and 2022 identified 64 patients meeting inclusion criteria. Radiomic features (e.g., Feret diameter, elongation, flatness, surface area, and volume) from preoperative CT scans within 24 h of surgery were analyzed alongside clinical factors, including cardiac comorbidities, pupillary response, SOFA score, age, and anticoagulation status. Results: Of the 64 patients, 18 (28%) developed generalized seizures. Univariate analysis showed significant associations with Feret diameter (p = 0.045), elongation (p = 0.005), cardiac comorbidities (p = 0.017), and SOFA score (p = 0.036). ROC analysis showed excellent discriminatory ability for elongation (AUC = 0.82). Multivariate analysis identified elongation as an independent predictor (p = 0.003); elongation ≥ 1.45 increased seizure risk 7.78-fold (OR = 7.778; 95% CI = 1.969–30.723). Conclusions: Radiomic features, particularly elongation, may help predict epilepsy risk in aSDH patients undergoing craniotomy. Prospective validation is needed.