ReviewThe international journal of cardiovascular imaging2020
From CT to artificial intelligence for complex assessment of plaque-associated risk.
Review in The international journal of cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05488249 (CCTA Study of Atheromatous Lesions, Coronary Hemodynamics and Systemic Inflammation in Patients With Vulnerable Plaques), which is not on this map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
CCTA Study of Atheromatous Lesions, Coronary Hemodynamics and Systemic Inflammation in Patients With Vulnerable Plaques (COHESIVE STUDY)
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Functional Coronary Artery Assessment: a Systematic Literature Review.Wiener klinische Wochenschrift · 2022Pooled it
- Artificial Intelligence in Coronary Computed Tomography Angiography: A Bibliometric Analysis of Trends and Themes.Clinical cardiology · 2026Review
- Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.Annals of biomedical engineering · 2026Article
- AI-Driven multi-view learning from CCTA for myocardial infarction diagnosis.The international journal of cardiovascular imaging · 2026Article
- Non-enhanced CT-based radiomics signature of epicardial adipose tissue for screening coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance.The international journal of cardiovascular imaging · 2025Article
- Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms.European heart journal. Imaging methods and practice · 2024Article
- Radiomics Signature of Epicardial Adipose Tissue for Predicting Postoperative Atrial Fibrillation after Off-Pump Coronary Artery Bypass Surgery.Reviews in cardiovascular medicine · 2023Article
- Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.PloS one · 2023Article
- Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2022Article
- Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease.Current cardiology reports · 2022Review
- Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects.Frontiers in cardiovascular medicine · 2022Review
- Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review.European journal of hybrid imaging · 2021Review
- The Radiologist as a Gatekeeper in Chest Pain.International journal of environmental research and public health · 2021Review
- Imaging Cardiovascular Inflammation in the COVID-19 Era.Diagnostics (Basel, Switzerland) · 2021Review
- Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.Diagnostics (Basel, Switzerland) · 2021Review
- Introduction topical issue on CT plaque burden.The international journal of cardiovascular imaging · 2020Article
- Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.BioMed research international · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.