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

Summary

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 the original NornirNet model; determine whether adding clinical and anatomical information improves the predictions; assess differences in performance between hospitals; and evaluate how well the model's predicted risks correspond to the outcomes actually observed.

Study population:

The study is a multicenter observational study involving patients over the age of 18 who underwent elective EVAR for an intact fusiform abdominal aortic aneurysm at participating hospitals in Switzerland, Europe, and the United States between 2015 and 2025. Patients must have suitable CT scans before and after EVAR and sufficient medical and imaging information to determine whether a T2EL occurred and how the aneurysm changed over time. Patients treated for a ruptured aneurysm, patients with certain other types of aneurysms, patients who had previous aortic procedures, and patients whose CT scans are not suitable for analysis will be excluded. The planned study population is approximately 1,250 patients, depending on the number of eligible patients available at the participating hospitals.

Study procedures:

This is a retrospective study, meaning that it uses information and CT scans that were already collected as part of routine medical care. No additional examinations or procedures are performed for the study, and no biological samples are collected. Participating hospitals will provide coded clinical information and anonymized CT scans. The information collected may include age, sex, other medical conditions, body weight and height, laboratory results, heart-related information, details about the aneurysm and blood vessels, and information about the EVAR procedure. Follow-up CT scans will be reviewed by specialists to determine whether a T2EL occurred and whether the aneurysm sac became larger. Patients will be classified into three groups: those with no T2EL, those with a T2EL without significant aneurysm enlargement, and those with a T2EL associated with aneurysm enlargement of at least 5 mm or a related additional procedure. The deep-learning model will be developed using data from some participating hospitals and then tested on data from other hospitals that were not involved in developing the model. Its ability to make accurate and consistent predictions will then be evaluated.

Conditions

• Type II Endoleak
• Abdominal Aortic Aneurysm Enlargement

Interventions

OTHER

Artificial intelligence model analyzing preoperative CT angiography

This study involves the retrospective multicenter analysis of preoperative CT angiography (CTA) and clinical data from patients who underwent EVAR. A new and improved three-dimensional deep-learning model, building on the NornirNet framework, will be developed using data from designated development centers and subsequently evaluated, after model freezing, on independent held-out centers.

Sponsors & Collaborators

• Ente Ospedaliero Cantonale, Bellinzona

  lead OTHER

Principal Investigators

• Giorgio Prouse, MD · Ente Ospedaliero Cantonale, Bellinzona

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-10-01

Primary Completion

2027-12-31

Completion

2028-12-31