Deep-learning Predictors of Abdominal Aortic Aneurysm Enlargement in Type II Endoleaks After Endovascular Aortic Repair: The RADAR Study

Background and rationale: Abdominal aortic aneurysm (AAA) is a weakening and enlargement of the main artery in the abdomen. Endovascular aneurysm repair (EVAR) is a minimally invasive treatment used to repair an AAA. After EVAR, some patients develop a complication, called a type II endoleak (T2EL), in which blood continues to flow into the aneurysm sac through small blood vessels. Many T2ELs disappear on their own, but in some patients they can cause the aneurysm sac to enlarge and may require another procedure. The RADAR study aims to develop a deep-learning computer model that can use the CT scan performed before EVAR to predict which patients are more likely to develop a T2EL associated with aneurysm enlargement. The model will be developed using information from several hospitals and tested on patients from hospitals that were not involved in its development. Duration: July 2026 - December 2028 The study will include patients who have undergone EVAR between 1 January 2015 and 31 December 2025. Their available follow-up information will be collected until 31 December 2026 or until an earlier event such as the last available CT scan, a procedure related to T2EL, another defined medical event, or death. The baseline postoperative CTA, acquired 1-3 months after EVAR, will serve as the reference examination for assessment of aneurysm-sac growth during follow-up. Patients who have not developed the study outcome generally need at least 24 months of imaging follow-up to be classified reliably. Objectives: The primary objective is to develop and test a deep-learning model that can predict, before EVAR, whether a patient will develop a T2EL and whether it will be associated with significant enlargement of the aneurysm sac. The model will be tested using data from hospitals that were not involved in its development. Secondary objectives are to assess whether the model works consistently across different hospitals and CT scanning methods; compare the new model with t