Artificial Intelligence CT Texture Radiomics for Outcome Prediction After EVAR: A Narrative Review

Background: Endovascular aneurysm repair (EVAR) requires lifelong imaging surveillance because endoleaks, aneurysm sac expansion, and severe adverse events occur in up to one-third of the patients. Conventional follow-up based on sac diameter and visual assessment may fail to detect early microstructural changes that precede clinical deterioration. Methods: This narrative review summarizes the current evidence on texture-based radiomics and artificial intelligence (AI) applied to computed tomography (CT) and CT angiography (CTA) for post-EVAR outcome prediction and surveillance. Original studies evaluating radiomic features and AI-based models for endoleak detection, aneurysm sac behavior, and EVAR-related adverse events were included and qualitatively synthesized. Results: Ten studies were included. Radiomic features describing texture heterogeneity, gray-level nonuniformity, entropy, and spatial complexity were extracted from the aneurysm sac, intraluminal thrombus, and perivascular adipose tissue. Machine learning and deep learning models achieved good to excellent performance, with reported AUC values ranging from 0.78 to 0.95 for predicting endoleaks, sac expansion, and severe adverse events. Texture-based radiomics consistently outperformed morphology-only assessments and showed complementary value to deep learning, including applications on non-contrast CT. Conclusions: CT texture radiomics combined with AI represents an emerging research approach with potential relevance for post-EVAR surveillance, although current evidence remains limited. By capturing tissue heterogeneity beyond conventional morphology, radiomics may enable the earlier detection of complications and support risk-adapted follow-up. However, the heterogeneity of methods limited external validation, and reproducibility issues remain major barriers to clinical translation.