Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance

Abstract

Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is critical for initiating timely interventions such as decompressive hemicraniectomy. Artificial intelligence (AI) and radiomics have emerged as promising tools for predicting MCE, offering the potential to transform reactive stroke management into proactive care. However, variability in methodologies and inconsistent reporting limits the widespread adoption of these technologies. A comprehensive search of PubMed, Embase, Web of Science, and Scopus identified studies reporting on the sensitivity, specificity, and area under the curve (AUC) of AI models in MCE prediction. Data were synthesized using random-effects meta-analyses. Subgroup analyses explored the impact of study design, machine learning input type, and other key factors on diagnostic accuracy. Publication bias was assessed using Egger's test and funnel plot analyses. Data from ten studies encompassing 1,594 unique stroke patients were included in the analysis. The pooled sensitivity and specificity of AI models for predicting MCE were 81.1% (95% CI: 73.0-87.2%) and 92.6% (95% CI: 91.2-93.9%), respectively, with an AUC of 0.939. The diagnostic odds ratio was 43.73 (95% CI: 24.78-77.15), demonstrating excellent discriminative ability. Subgroup analyses revealed higher sensitivity and specificity in prospective studies (92.0% and 93.3%) compared to retrospective studies (76.1% and 91.4%). Radiomics-based models showed slightly higher sensitivity (84.2%) compared to non-radiomics models (80.4%), though both input types achieved comparable specificity. Interestingly, patients undergoing revascularization had a higher prevalence of MCE, likely due to their more severe initial presentations. Minimal heterogeneity was observed in specificity across studies, while publication bias was noted for sensitivity estimates. AI models show excellent diagnostic performance for predicting malignant cerebral edema (MCE), offering high sensitivity and specificity. Prospective studies, radiomics integration, and multi-center collaborations enhance their accuracy. However, external validation and standardized methodologies are needed to ensure broader clinical adoption and improve outcomes for stroke patients at risk of MCE. Clinical trial number Not applicable.

Keywords: Artificial intelligence; Machine learning; Malignant cerebral edema; Radiomics; Stroke.