ArticleCardiovascular engineering and technology2026
Coronary CT Angiography-Based Prediction Model for Hemodynamically Significant Coronary Stenosis Integrating Morphological and Plaque Characteristics.
Article in Cardiovascular engineering and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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18 authors.
Funding
Abstract
purposeA rising trend involves the application of machine learning to coronary computed tomography angiography (CCTA) for predicting physiological significance of coronary lesions. This study aimed to formulate a clinical model using CCTA-derived features and the least absolute shrinkage and selection operator (LASSO) regression method to predict hemodynamically significant coronary stenosis.
methodsThe study population comprised individuals prospectively recruited from two tertiary medical centres with suspected or known coronary artery disease. The model was developed using patients from Centre 1 and independently validated in patients from Centre 2. All participants underwent CCTA followed by invasive coronary angiography with fractional flow reserve (FFR) measurements. The LASSO model incorporated coronary morphological and plaque features derived semi-automatically from CCTA, along with clinical and demographic variables, as input predictors. Model performance was assessed against the reference standard of invasive FFR ≤ 0.80.
resultsThe analysis included 210 diseased vessels from 133 patients-141 vessels from 84 patients in Centre 1 (development cohort) and 69 vessels from 49 patients in Centre 2 (validation cohort). Using the LASSO algorithm, nine predictive variables were selected from an initial set of 33 candidate features: heart rate, minimal lumen diameter (MLD), area stenosis, diameter stenosis ≥ 50%, lesion length/MLD
conclusionsA prediction model integrating coronary and plaque parameters outperformed existing CCTA-based methods in identifying hemodynamically significant coronary stenosis, offering improved accuracy for ischemia detection.
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