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

NCT07783126 | Not Yet Recruiting DMC

• 1 other identifier: 2026-06264
• Study Type: observational
• Target: 1,250
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

type II endoleak; abdominal aortic aneurysm; AI; cardiovascular surgery; aorta; radiology; RADAR; EVAR; CT; CTA; T2ELs; sac enlargement; endograft; quantitative features

Outcome Measures

Primary Outcomes (1)

• Performance of the deep-learning model in held-out test centers

  Performance of the frozen deep-learning model will be evaluated in independent held-out test centers for three-class prediction of T2EL outcome: Class 0 (no T2EL), Class 1 (T2EL without significant aneurysm sac growth), and Class 2 (T2EL with significant sac growth or T2EL-related reintervention). Discrimination and classification performance will be assessed using prespecified performance metrics, including AUC, sensitivity, and specificity.

  From the baseline postoperative CTA (1-3 months after EVAR) through the last available follow-up, T2EL-related reintervention, censoring event, death, or administrative censoring on 31 December 2026.

Secondary Outcomes (1)

• Secondary performance analyses of the deep-learning model

  From the baseline postoperative CTA (1-3 months after EVAR) through the last available follow-up, T2EL-related reintervention, censoring event, death, or administrative censoring on 31 December 2026.

Study Arms (1)

Retrospective multicenter EVAR cohort

This retrospective multicenter observational cohort includes adult patients who underwent elective standard EVAR for an intact fusiform infrarenal abdominal aortic aneurysm between 01.01.2015 and 31.12.2025 at participating centers. Eligible patients are identified according to predefined inclusion and exclusion criteria. Based on imaging follow-up, patients with sufficient information for outcome adjudication are assigned to one of three classes: Class 0, no type II endoleak (T2EL); Class 1, T2EL without significant aneurysm sac growth; or Class 2, T2EL associated with significant sac growth or T2EL-related reintervention. Eligible patients with insufficient follow-up for reliable outcome-class assignment are classified as indeterminate, retained in the study database, and excluded from the primary analysis.

Other: Artificial intelligence model analyzing preoperative CT angiography

Interventions

Artificial intelligence model analyzing preoperative CT angiography OTHER

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.

Retrospective multicenter EVAR cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study includes a retrospective multicenter cohort of approximately 1,250 adult patients (≥18 years) who underwent elective standard EVAR for an intact fusiform infrarenal abdominal aortic aneurysm between 01.01.2015 and 31.12.2025. Patients must have available preoperative and postoperative CT angiography (CTA) scans of sufficient quality and adequate follow-up data. Patients will be excluded if they underwent emergency repair for ruptured aneurysm, had non-fusiform aneurysms, previous aortic interventions, complex EVAR procedures, non-diagnostic imaging, or refused the use of their health data for research where applicable. Patients will be classified according to follow-up findings into three outcome classes: Class 0, no type II endoleak; Class 1, type II endoleak without significant aneurysm sac growth; or Class 2, type II endoleak with significant sac growth or T2EL-related reintervention.

You may not qualify if:

• Age ≥ 18 years at the time of the index procedure.
• Elective repair of an intact, fusiform, infrarenal abdominal aortic aneurysm (AAA), symptomatic or asymptomatic, with a maximum aneurysm diameter ≥50 mm in women and 55 mm in men, or repaired for symptoms or documented rapid growth (≥5 mm in 6 months or 10 mm in 12 months).
• Treatment by standard EVAR, defined as implantation of a commercially available bifurcated infrarenal stent-graft with proximal sealing in the infrarenal neck, below the lowermost renal artery, and without any of the following:
• fenestrated, branched or scallop-modified devices (F/BEVAR);
• parallel-graft techniques;
• adjunctive sac or side-branch embolization at or before the index procedure;
• endostapling endoanchor devices at the index procedure;
• endovascular aneurysm sealing systems (e.g. EVAS);
• proximal or distal extension cuffs placed during or after the index procedure;
• aorto-uni-iliac configurations and iliac branch devices;
• Index procedure performed between 1 January 2015 and 31 December 2025 at one of the participating centers, with follow-up data censored at the administrative cut-off date of 31 December 2026.
• Preoperative arterial-phase CTA available in digital (DICOM) format, acquired ≤ 6 months before the index procedure.
• Baseline postoperative CTA acquired between 1 and 3 months after the index procedure.
• All qualifying CTA examinations (preoperative, baseline and follow-up) acquired with a reconstructed slice thickness ≤ 2.5 mm, with anatomical coverage extending at minimum from the celiac trunk to the external iliac arteries, and including an arterial phase and a delayed (venous) phase.
• Repair for a ruptured AAA, or emergent repair for contained rupture.

Sponsors & Collaborators

• Ente Ospedaliero Cantonale, Bellinzona: lead

Related Publications (16)

• Andreoli F, Mattiussi F, Wasseh E, et al. NornirNet: a deep learning framework to distinguish benign from malignant type II endoleaks after endovascular aortic aneurysm repair using preoperative imaging. AI. 2026;7(2):57. doi:10.3390/ai7020057

  BACKGROUND

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MeSH Terms

Conditions

Aortic Aneurysm, Abdominal

Condition Hierarchy (Ancestors)

Aortic Aneurysm; Aneurysm; Vascular Diseases; Cardiovascular Diseases; Aortic Diseases

Study Officials

• Giorgio Prouse, MD

  Ente Ospedaliero Cantonale, Bellinzona

  STUDY CHAIR

Central Study Contacts

Giorgio Prouse, MD

CONTACT

+41 91 811 6328; giorgio.prouse@eoc.ch

Mariacarla Andreozzi, PhD

CONTACT

+41 91 811 7361; mariacarla.andreozzi@eoc.ch

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: August 20, 2026
• First Posted: August 24, 2026
• Study Start (Estimated): October 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2028
• Last Updated: August 24, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share