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Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients (CORO-CTAIOMICS)

NCT06029777 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2190

Last updated 2026-01-15

No results posted yet for this study

Summary

CAD is a leading cause of mortality in Europe. cCTA is recommended to rule out obstructive CAD, but, in most patients, it shows non-obstructive CAD. The management of these patients is unclear due to lack of reproducible quantitative measurement, beyond stenosis severity, capable to assess the risk of disease progression towards developing MACEs. To improve identification and phenotypization of patients at high risk of disease progression, the investigators propose the application of artificial intelligence algorithms to cCTA images to automatically extract periluminal radiomics features to characterize the atherosclerotic process. By leveraging machine-learning empowered radiomics the investigators aim to improve patients' risk stratification in a robust, quantitative and reproducible fashion. By developing a novel quantitative AI based cCTA measure, the investigators expect to provide a risk score capable to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome

Conditions

Sponsors & Collaborators

  • Ministry of Health, Italy

    collaborator OTHER_GOV

  • IRCCS San Raffaele

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-07-31
Primary Completion
2024-09-24
Completion
2024-09-24

Countries

  • Italy

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06029777 on ClinicalTrials.gov