Abstract 385: Beyond Detection: A Systematic Review of Machine Learning and Radiomics for Predicting Intracranial Aneurysm Rupture Risk

Abstract

Introduction While the majority of intracranial aneurysms (IAs) remain asymptomatic, rupture results in subarachnoid hemorrhage, a devastating event with high morbidity and mortality. Traditional rupture risk assessment relies on morphology, size, and location, yet these parameters lack individualized precision. Recent advances in machine learning (ML) and radiomics provide an opportunity to integrate high‐dimensional imaging features and clinical data to predict rupture risk with greater accuracy. This systematic review and meta‐analysis evaluates the performance of ML‐ and radiomics‐based approaches for IA rupture prediction. Methods We reviewed studies applying ML or radiomics for IA rupture risk prediction, including systematic reviews, retrospective datasets, and prospective validation studies. Eligible reports used angiographic imaging (CTA, MRA, or DSA) with reference standards of rupture status or clinical outcomes. Extracted data included sample size, sensitivity, specificity, and area under the curve (AUC). Random‐effects models were used to synthesize predictive performance. Subgroup analyses evaluated conventional ML algorithms, radiomics‐based models, and deep learning approaches. Results Four representative studies highlight the evolving field. A meta‐analysis by Akhtar et al. (2024) demonstrated pooled sensitivity and specificity of 83% for ML rupture prediction, with AUC ∼0.85. Stumpo et al. (2022) reported that ML models consistently outperformed conventional risk scores, achieving sensitivity and specificity near 80%. Hu et al. (2023), using digital subtraction angiography in 263 patients, achieved exceptional performance with an AUC of 0.982, sensitivity of 94.4%, and specificity of 97.5% in distinguishing ruptured from unruptured aneurysms. In contrast, Heo et al. (2020) leveraged a national claims dataset to develop population‐level ML models, reaching AUROC 0.765 for rupture prediction, demonstrating the feasibility of integrating epidemiological and imaging data. Pooled analysis across studies yielded an average AUC of 0.85 (95% CI: 0.80‐0.89), with sensitivity ∼0.85 and specificity ∼0.86. Notably, radiomics models incorporating texture, intensity, and morphological signatures demonstrated superior discrimination compared to size‐based parameters alone. However, performance was heterogeneous across datasets, and generalizability was limited by small sample sizes and retrospective designs. Conclusion Machine learning and radiomics approaches extend aneurysm assessment “beyond detection,” offering promising accuracy in rupture risk prediction. With pooled AUCs near 0.85 and select studies reporting >0.95 performance, these models surpass traditional size‐based criteria. However, variability across cohorts and lack of standardized imaging features limit clinical translation. Future directions include prospective multicenter validation, integration of radiomics with clinical and hemodynamic markers, and development of explainable AI frameworks to ensure reproducibility. Ultimately, ML‐guided rupture risk stratification holds potential to personalize management and reduce subarachnoid hemorrhage incidence.

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