Prediction of the development of new coronary atherosclerotic plaques with radiomics.
Coronary artery atherosclerosis
Coronary artery disease
Coronary computed tomography angiography
Radiomics
Journal
Journal of cardiovascular computed tomography
ISSN: 1876-861X
Titre abrégé: J Cardiovasc Comput Tomogr
Pays: United States
ID NLM: 101308347
Informations de publication
Date de publication:
19 Feb 2024
19 Feb 2024
Historique:
received:
20
11
2023
revised:
01
02
2024
accepted:
12
02
2024
medline:
21
2
2024
pubmed:
21
2
2024
entrez:
20
2
2024
Statut:
aheadofprint
Résumé
Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV ≥ 1 mm In total, 9583 normal coronary segments were identified from 1162 patients (60.3 ± 9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p > 0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p < 00.0001 compared to Models 1 and 2). Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. ClinicalTrials.gov NCT0280341.
Sections du résumé
BACKGROUND
BACKGROUND
Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA).
METHODS
METHODS
From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV ≥ 1 mm
RESULTS
RESULTS
In total, 9583 normal coronary segments were identified from 1162 patients (60.3 ± 9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p > 0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p < 00.0001 compared to Models 1 and 2).
CONCLUSION
CONCLUSIONS
Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque.
CLINICAL TRIAL REGISTRATION
BACKGROUND
ClinicalTrials.gov NCT0280341.
Identifiants
pubmed: 38378314
pii: S1934-5925(24)00032-7
doi: 10.1016/j.jcct.2024.02.003
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest Dr. Chang receives funding from the Leading Foreign Research Institute Recruitment Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (MSIT) (Grant No. 2012027176). Dr. James K. Min receives funding from GE Healthcare and serves on the scientific advisory board of Arineta and GE Healthcare. Dr. Min also has an equity interest in and is an employee of Cleerly, Inc. The remaining authors have no relevant disclosures.