{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:16:56Z","timestamp":1774451816685,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics16070964","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:57:34Z","timestamp":1774447054000},"page":"964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence CT Texture Radiomics for Outcome Prediction After EVAR: A Narrative Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7917-5027","authenticated-orcid":false,"given":"Chiara","family":"Zanon","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Padova, Via Giustiniani 2, 35128 Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3995-4066","authenticated-orcid":false,"given":"Giovanni Alfonso","family":"Chiariello","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, Agostino Gemelli Foundation Polyclinic IRCCS, 00136 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0004-6378","authenticated-orcid":false,"given":"Tommaso","family":"D\u2019Angelo","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology Unit, BIOMORF Department, University Hospital \u201cPoliclinico G. Martino\u201d, 98125 Messina, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-9365","authenticated-orcid":false,"given":"Emilio","family":"Quaia","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Padova, Via Giustiniani 2, 35128 Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"ref_1","first-page":"e73066","article-title":"Endovascular Aneurysm Repair Versus Open Surgical Repair in Treating Abdominal Aortic Aneurysm","volume":"16","author":"Vienneau","year":"2024","journal-title":"Cureus"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1097\/01.sla.0000234893.88045.dc","article-title":"Long-term Outcomes After Endovascular Abdominal Aortic Aneurysm Repair","volume":"244","author":"Brewster","year":"2006","journal-title":"Ann. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1177\/1526602819852085","article-title":"Effect of Imaging Surveillance After Endovascular Aneurysm Repair on Reinterventions and Mortality: A Systematic Review and Meta-analysis","volume":"26","author":"Geraedts","year":"2019","journal-title":"J. Endovasc. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13244-021-01112-4","article-title":"CT angiography for the assessment of EVAR complications: A pictorial review","volume":"13","author":"Gozzo","year":"2022","journal-title":"Insights Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.23736\/S0021-9509.18.10584-2","article-title":"Post-EVAR aneurysm sac shrinkage is prognostically favorable, but does not justify withholding follow-up","volume":"61","author":"Langenberg","year":"2020","journal-title":"J. Cardiovasc. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cicho\u0144, M., Wybraniec, M.T., Oko\u0144, O., Zielonka, M., Antoniuk, S., Szatan, T., and Mizia-Stec, K. (2023). Repeated Dose of Contrast Media and the Risk of Contrast-Induced Acute Kidney Injury in a Broad Population of Patients Hospitalized in Cardiology Department. J. Clin. Med., 12.","DOI":"10.3390\/jcm12062166"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s13139-017-0512-7","article-title":"Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies","volume":"52","author":"Park","year":"2018","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/23808993.2016.1164013","article-title":"Radiomics: A new application from established techniques","volume":"1","author":"Parekh","year":"2016","journal-title":"Expert Rev. Precis. Med. Drug Dev."},{"key":"ref_9","first-page":"e20240039","article-title":"A Pictorial Review of Current Approaches in Endoleak Imaging","volume":"10","author":"Nagatomi","year":"2025","journal-title":"Interv. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1177\/02841851211032443","article-title":"Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: A proof of concept","volume":"63","author":"Charalambous","year":"2022","journal-title":"Acta Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s12265-023-10404-7","article-title":"Radiomic-based Textural Analysis of Intraluminal Thrombus in Aortic Abdominal Aneurysms: A Demonstration of Automated Workflow","volume":"16","author":"Rezaeitaleshmahalleh","year":"2023","journal-title":"J. Cardiovasc. Transl. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1148\/rg.2017170056","article-title":"CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges","volume":"37","author":"Lubner","year":"2017","journal-title":"RadioGraphics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Varghese, B.A., Fields, B.K.K., Hwang, D.H., Duddalwar, V.A., Matcuk, G.R., and Cen, S.Y. (2023). Spatial assessments in texture analysis: What the radiologist needs to know. Front. Radiol., 3.","DOI":"10.3389\/fradi.2023.1240544"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting cancer outcomes with radiomics and artificial intelligence in radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133138","DOI":"10.1016\/j.ijcard.2025.133138","article-title":"A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair","volume":"429","author":"Huang","year":"2025","journal-title":"Int. J. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1186\/s13244-024-01804-7","article-title":"Assessing abdominal aortic aneurysm growth using radiomic features of perivascular adipose tissue after endovascular repair","volume":"15","author":"Lv","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1007\/s00270-024-03805-x","article-title":"Detection of Endoleak after Endovascular Aortic Repair through Deep Learning Based on Non-contrast CT","volume":"47","author":"Yang","year":"2024","journal-title":"Cardiovasc. Intervent. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1007\/s00330-023-10000-y","article-title":"Unenhanced computed tomography radiomics help detect endoleaks after endovascular repair of abdominal aortic aneurysm","volume":"34","author":"Hu","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12268","DOI":"10.1038\/s41598-020-69226-1","article-title":"CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression","volume":"10","author":"Ding","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10278-011-9417-7","article-title":"Evaluation of Texture for Classification of Abdominal Aortic Aneurysm After Endovascular Repair","volume":"25","author":"Maiora","year":"2012","journal-title":"J. Digit. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhou, M., Ding, Y., Li, X., Zhou, Z., Shi, Z., and Fu, W. (2022). Development and Comparison of Multimodal Models for Preoperative Prediction of Outcomes After Endovascular Aneurysm Repair. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.870132"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1177\/17085381221091061","article-title":"A radiomics model for predicting the outcome of endovascular abdominal aortic aneurysm repair based on machine learning","volume":"31","author":"Wang","year":"2023","journal-title":"Vascular"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1177\/17085381241262575","article-title":"Unsupervised machine learning cluster analysis to identification EVAR patients clinical phenotypes based on radiomics","volume":"33","author":"Wang","year":"2025","journal-title":"Vascular"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mukhtar, H., Ganjizadeh, A., Misra, A., Kane, J., Wang, P., Singh, P.G., Lemieux, A., Baranwal, G., Kilari, S., and Erickson, B.J. (2026). Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review. J. Imaging Inform. Med., online ahead of print.","DOI":"10.1007\/s10278-025-01803-0"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/7\/964\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:15:08Z","timestamp":1774448108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/7\/964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":24,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["diagnostics16070964"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16070964","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]}}}