ArticleDiagnostics (Basel, Switzerland)2022
Ultrasound Radiomics Nomogram Integrating Three-Dimensional Features Based on Carotid Plaques to Evaluate Coronary Artery Disease.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Synergizing Systemic Inflammation and Multimodal Ultrasound: Methodological Insights Into Predicting Carotid Plaque Vulnerability.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Shear Wave Elastography for Carotid Artery Stiffness: Ready for Prime Time?Diagnostics (Basel, Switzerland) · 2025Review
- Quality assessment of radiomics models in carotid plaque: a systematic review.Quantitative imaging in medicine and surgery · 2024Review
- Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS.Cancers · 2022Article
- Development and Validation of a Nomogram for Predicting the Severity of Coronary Artery Disease Based on Cardiopulmonary Exercise Testing.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
This study aimed to explore the feasibility of ultrasound radiomics analysis before invasive coronary angiography (ICA) for evaluating the severity of coronary artery disease (CAD) quantified by the SYNTAX score (SS). This study included 105 carotid plaques from 105 patients (64 low-SS patients, 41 intermediate-high-SS patients). The clinical characteristics and three-dimensional ultrasound (3D-US) features before ICA were assessed. Ultrasound images of carotid plaques were used for radiomics analysis. Least absolute shrinkage and selection operator (LASSO) regression, which generated several nonzero coefficients, was used to select features that could predict intermediate-high SS. Based on those coefficients, the radiomics score (Rad-score) was calculated. The selected clinical characteristics, 3D-US features, and Rad-score were finally integrated into a radiomics nomogram. Among the clinical characteristics and 3D-US features, high-density lipoprotein (HDL), apolipoprotein B (Apo B), and plaque volume were identified as predictors for distinguishing between low SS and intermediate-high SS. During the radiomics process, 8 optimal radiomics features most capable of identifying intermediate-high SS were selected from 851 candidate radiomics features. The differences in Rad-score between the training and the validation set were significant (
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