Background: Cerebral vasospasm is a frequent and severe complication after aneurysmal subarachnoid hemorrhage (aSAH), often causing delayed cerebral ischemia (DCI) and poor outcomes. Despite progress in neurocritical care, early vasospasm prediction after aSAH remains challenging due to its multifactorial nature but is essential for timely intervention. Methods: We retrospectively analyzed 503 consecutive patients with spontaneous subarachnoid hemorrhage (SAH) treated between 2013 and 2018. Of these, 345 with angiographically confirmed aSAH were included in the primary analysis, and 158 SAH cases in a sensitivity analysis. We extracted demographic, clinical, and imaging parameters including age, sex, Hunt and Hess grade, Fisher scale, aneurysm and treatment features, external ventricular drainage (EVD), and central nervous system (CNS) infection. Seven supervised machine learning (ML) models, including logistic regression and gradient-boosted trees, were trained using nested cross-validation and evaluated by AUC-ROC, AUC-PR, accuracy, precision, sensitivity, specificity, and F1 score. Results: Over half of aSAH patients developed moderate to severe vasospasm. Independent predictors included younger age, higher Hunt and Hess and Fisher grades, and EVD placement (all p < 0.001). Logistic regression achieved the best discrimination (AUC-ROC 0.723), while tree-based models reached higher sensitivity (0.867) at the expense of specificity. Aneurysmal etiology further increased vasospasm risk (OR 4.72). Conclusions: Routinely available clinical and imaging parameters enable reliable ML-based vasospasm prediction after aSAH. Logistic regression provided the best balance between accuracy and interpretability, while tree-based models optimized sensitivity. This web-based, interpretable ML tool—one of the first using routine clinical data—may support the bedside prediction of vasospasm and requires prospective validation.

Background: Superior cerebellar artery (SCA) infarction is a rare but clinically significant subtype of posterior circulation stroke. Extensive swelling in the SCA territory may cause downward brainstem compression and appear as brainstem hypodensity on computed tomography, potentially leading to premature treatment withdrawal. Methods: We report the case of a 50-year-old woman with acute SCA-territory infarction (NIHSS = 7) present-ing with vertigo, dysphagia, dysarthria, and diplopia. Initial computed tomography sug-gested extensive brainstem infarction, prompting withdrawal of treatment. Diffu-sion-weighted MRI revealed reversible edema with brainstem sparing. The patient un-derwent suboccipital decompressive craniectomy and ventricular drainage with favorable neurological recovery. In addition, a systematic literature search was conducted according to PRISMA 2020 guidelines in PubMed, Web of Science, and Scopus (studies published since 1 January 2015). Fifteen studies met predefined eligibility criteria. Results: Magnetic resonance imaging findings were decisive in avoiding a falsely dismal prognosis and in-appropriate withdrawal of care. Across literature, infarct volume (> 30–35 mL), brainstem involvement and bilateral cerebellar infarction emerged as key predictors of malignant course. Early decompressive surgery was consistently associated with improved survival, though functional outcomes varied. Fast magnetic resonance imaging techniques and volumetric imaging improved risk stratification and surgical decision-making. Conclu-sions: SCA infarction can mimic brainstem infarction on computed tomography due to secondary compression rather than true ischemia. Magnetic resonance imaging is essen-tial to guide treatment and prevent avoidable mortality. Multimodal imaging combined with interdisciplinary management allows for accurate prognostication and optimized surgical timing in malignant SCA infarction.

Background/Objectives: Superior cerebellar artery (SCA) infarction is a rare but clinically significant subtype of posterior circulation stroke. Extensive swelling in the SCA territory may cause downward brainstem compression and appear as brainstem hypodensity on computed tomography, potentially leading to premature treatment withdrawal. Methods: We report the case of a 50-year-old woman with acute SCA-territory infarction (NIHSS = 7) presenting with vertigo, dysphagia, dysarthria, and diplopia. Initial computed tomography suggested extensive brainstem infarction, prompting withdrawal of treatment. Diffusion-weighted MRI revealed reversible edema with brainstem sparing. The patient underwent suboccipital decompressive craniectomy and ventricular drainage with favorable neurological recovery. In addition, a systematic literature search was conducted according to PRISMA 2020 guidelines in PubMed, Web of Science, and Scopus (studies published since 1 January 2015). Fifteen studies met predefined eligibility criteria. Results: Magnetic resonance imaging findings were decisive in avoiding a falsely dismal prognosis and inappropriate withdrawal of care. Across the literature, infarct volume (>30–35 mL), brainstem involvement and bilateral cerebellar infarction emerged as key predictors of malignant course. Early decompressive surgery was consistently associated with improved survival, though functional outcomes varied. Fast magnetic resonance imaging techniques and volumetric imaging improved risk stratification and surgical decision-making. Conclusions: SCA infarction can mimic brainstem infarction on computed tomography due to secondary compression rather than true ischemia. Magnetic resonance imaging is essential to guide treatment and prevent avoidable mortality. Multimodal imaging combined with interdisciplinary management allows for accurate prognostication and optimized surgical timing in malignant SCA infarction.