Why the study?
Accurate CAD stenosis grading requires time-consuming manual assessment and suffers from interobserver variability, and no deep learning methods had explored combining radiomic and autoencoder-based features.
Does a combined radiomics-deep learning approach improve automated CAD-RADS classification accuracy in patients with suspected CAD compared to single-domain models?
Population
2,548 CCTA-derived MPR images from 220 patients
Comparison
Combined radiomic and AE-based features vs single feature sets
Design
Machine learning model development and validation study
Key result
A combined radiomics and autoencoder-based machine learning model achieved an overall balanced accuracy of 0.91 for automated CAD-RADS classification, outperforming single-domain models.
Authors
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May support automated CAD-RADS scoring in suspected CAD; hypothesis-generating and requires prospective validation before clinical adoption.
Observational (n=220)
No
Does a combined radiomics-deep learning approach improve automated CAD-RADS classification accuracy in patients with suspected CAD compared to single-domain models?
Combining radiomic and autoencoder-based deep learning features from CCTA images enables highly accurate, automated patient-level CAD-RADS classification, potentially reducing manual assessment time and interobserver variability.
Absolute Event Rate: 0.91% vs 0.82%
Iacono et al. (2025) conducted an observational in Coronary Artery Disease (n=220). Combined radiomics and autoencoder (AE)-based machine learning model vs. Single radiomic model and single AE-based model was evaluated on Overall balanced accuracy for 3-class CAD-RADS stratification on the test set. A combined radiomics and autoencoder-based machine learning model achieved an overall balanced accuracy of 0.91 for automated CAD-RADS classification, outperforming single-domain models.