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

NCT ID: NCT06029777

Last Updated: 2026-01-15

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 2190 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-07-31
• Study Completion Date: 2024-09-24

Brief 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

Detailed Description

Background and rationale

In the last years, cCTA has become a pivotal diagnostic tool in the setting of suspected CAD. The latest ESC guidelines recommend cCTA as the first line diagnostic test for patients with symptoms suspected to originate from CAD, especially in intermediate pre-test probability (15 to 85%). patients. The AHA and Italian guidelines essentially suggest the same approach. The recommendations are based on the extremely high accuracy of cCTA in ruling-out obstructive CAD. As result the investigators assist to a tremendous increase in the number of patients undergoing CCTA in the daily clinical routine. Most of these patients (80%) result to not have obstructive CAD, but this result does not mean that all this patients are at low risk. The prognostic stratification of these patients is still an urgent unmet need. Among patients with absence of obstructive CAD the investigators found patients with different degree of atherosclerotic burden and patients with different kind of plaques (high or low risk plaques) and different degrees of coronary wall and pericoronary inflammation. The prognostic stratification of these patients is still an urgent unmet need. The cCTA images include a lot of information with a great potential informative content about the atherosclerotic burden and the vulnerability of the non-obstructive CAD of each single patients, but all these information are not currently exploited in the clinical routine to change the patients management for a lack of robust and reproducible tools to extract this data in quantitative way and o integrate them in a prognostic risk score.

In the last years, it has been shown that coronary artery plaque characteristics (e.g. lesion length, volume, stenosis, attenuation, remodeling index, etc) (2) and pericoronary adipose tissue (3) attenuation carry a significant prognostic value. However, it is known that many of these biomarkers suffer from low reproducibility, mainly due to technical constrains in automatic or semiautomatic separation of plaques from surrounding adipose tissue. This may cause the incorrect inclusion of plaques into the segmentation of pericoronary adipose tissue and vice-versa, leading to unreliable results in the assessment of plaque burden and plaque attenuation (4). Furthermore, the qualitative or semiquantitative evaluation of the aforementioned plaque characteristics and pericoronary fat density might reflect only part of the information available. In this scenario, radiomics-based assessment may unveil information hidden to the human eye, leading to better risk stratification of cardiovascular risk (5, 6).

This study adds two major improvements in prognostic risk stratification based on cCTA plaque and adipose tissue analysis. First, the investigators propose a method to segment both the plaques and the pericoronary adipose tissue easily and independently by semiautomatic or automatic identification of the edge between plaque and fat (which is critical). In fact, this tool will perform a segmentation of all the tissue included in a circular range outside the coronary lumen with a diameter based on the lumen diameter itself. This technical solution will greatly increase reproducibility of segmentation. Secondly, the investigators propose the use radiomics to analyze this circular pericoronary milieu, potentially individuating novel biomarkers -invisible to human eye- of CAD instability capable of providing important prognostic value.

In detail, the novelty of this research lies in the fact that 1) pericoronary adipose tissue and plaques will be treated as a single milieu, thus significantly reducing the effort for accurate tissue segmentation and reducing reproducibility concerns; 2) radiomics will be applied to extract meaningful information invisible to human eye capable. Furthermore, this radiomics approach will be supported by machine learning (ML) models including regularized regression, genetic algorithms and deep learning, due to the capability of ML to directly manage and assess the huge amount of data extracted from the radiological images. Thus, the final outcome of this research will be an algorithm capable of predicting the risk of MACEs of the single patient by automatically analyzing peri-luminal coronary tissue radiomics data derived from cCTA performed in the routine clinical practice.

Conditions

Coronary Artery Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Retrospective cohort

The retrospective cohort will include 2190 patients who underwent a clinically indicated cCTA between 2017 and 2019 at the Radiology Unit of San Raffaele Hospital.

No other interventions will be performed. Patients will be solely contacted via a telephone call to assess their clinical status.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. Patients with CT performed for CAD assessment between 2017 and 2019.

2. Follow-up duration of at least 4 years.

Exclusion Criteria

1. Refusal to participate in the study

2. Age <18 years old

3. History of previous coronary revascularization

4. Presence of other cardiovascular comorbidities (e.g. inflammatory cardiomyopathy, valvular cardiomyopathy, idiopathic dilated cardiomyopathy, infiltrative cardiomyopathy)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Ministry of Health, Italy

OTHER_GOV

Sponsor Role: collaborator

IRCCS San Raffaele

OTHER

Sponsor Role: lead

Responsible Party

Antonio Esposito

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

IRCCS San Raffaele

Milan, , Italy

Countries

Italy

References

Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.

Reference Type: BACKGROUND

PMID: 30357870 (View on PubMed)

Nerlekar N, Ha FJ, Cheshire C, Rashid H, Cameron JD, Wong DT, Seneviratne S, Brown AJ. Computed Tomographic Coronary Angiography-Derived Plaque Characteristics Predict Major Adverse Cardiovascular Events: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2018 Jan;11(1):e006973. doi: 10.1161/CIRCIMAGING.117.006973.

Reference Type: BACKGROUND

PMID: 29305348 (View on PubMed)

Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.

Reference Type: BACKGROUND

PMID: 33867303 (View on PubMed)

Tzolos E, McElhinney P, Williams MC, Cadet S, Dweck MR, Berman DS, Slomka PJ, Newby DE, Dey D. Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography. J Cardiovasc Comput Tomogr. 2021 Jan-Feb;15(1):81-84. doi: 10.1016/j.jcct.2020.03.007. Epub 2020 Apr 14.

Reference Type: BACKGROUND

PMID: 32312662 (View on PubMed)

Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B, Maurovich-Horvat P. Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign. Circ Cardiovasc Imaging. 2017 Dec;10(12):e006843. doi: 10.1161/CIRCIMAGING.117.006843.

Reference Type: BACKGROUND

PMID: 29233836 (View on PubMed)

Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, Nerlekar N, Nicholls SJ, Slomka PJ, Maurovich-Horvat P, Wong DTL, Dey D. Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study. JACC Cardiovasc Imaging. 2020 Nov;13(11):2371-2383. doi: 10.1016/j.jcmg.2020.06.033. Epub 2020 Aug 26.

Reference Type: BACKGROUND

PMID: 32861654 (View on PubMed)

Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006 Feb 23;7:91. doi: 10.1186/1471-2105-7-91.

Reference Type: BACKGROUND

PMID: 16504092 (View on PubMed)

Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M; IBIS Network; Schlaggar BL, Pruett JR Jr, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med. 2017 Jun 7;9(393):eaag2882. doi: 10.1126/scitranslmed.aag2882.

Reference Type: BACKGROUND

PMID: 28592562 (View on PubMed)

Brown PJ, Zhong J, Frood R, Currie S, Gilbert A, Appelt AL, Sebag-Montefiore D, Scarsbrook A. Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT. Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2790-2799. doi: 10.1007/s00259-019-04495-1. Epub 2019 Sep 4.

Reference Type: BACKGROUND

PMID: 31482428 (View on PubMed)

Bardosi ZR, Dejaco D, Santer M, Kloppenburg M, Mangesius S, Widmann G, Ganswindt U, Rumpold G, Riechelmann H, Freysinger W. Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification. Cancers (Basel). 2022 Jan 18;14(3):477. doi: 10.3390/cancers14030477.

Reference Type: BACKGROUND

PMID: 35158745 (View on PubMed)

Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021 Nov 12;4(1):1286. doi: 10.1038/s42003-021-02814-7.

Reference Type: BACKGROUND

PMID: 34773070 (View on PubMed)

Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006 Nov-Dec;26(6):565-74. doi: 10.1177/0272989X06295361.

Reference Type: BACKGROUND

PMID: 17099194 (View on PubMed)

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015 Mar 4;10(3):e0118432. doi: 10.1371/journal.pone.0118432. eCollection 2015.

Reference Type: BACKGROUND

PMID: 25738806 (View on PubMed)

Other Identifiers

PNRR-MAD-2022-12376633

Identifier Type: -

Identifier Source: org_study_id