Deep learning-derived coronary artery calcium scoring from non-gated chest CT achieved 77.5% accuracy and an AUC of 0.769 for CAC-DRS risk stratification compared to ECG-gated reference scans.
Observational (n=1,213)
No
Does deep learning-derived coronary artery calcium scoring from non-gated chest CT accurately agree with ECG-gated CACS, and how do scanning parameters affect this accuracy?
Deep learning-derived coronary artery calcium scoring from non-gated chest CT shows good agreement with standard ECG-gated scans, but its accuracy is significantly influenced by CT acquisition parameters like slice thickness and rotation time.
Estimación del efecto: AUC 0.769 (95% CI 0.754-0.785)
valor p: p=<0.001
To evaluate the accuracy of deep learning–derived coronary artery calcium scores (DL‑CACS) from non–electrocardiogram (ECG)-gated chest CT against ECG‑gated reference CACS and to determine the impact of CT acquisition parameters on measurement accuracy. From January 2020 to February 2021, 1213 patients at our institution underwent ECG‑gated cardiac CT and non‑gated chest CT within 3 months. An automated pipeline generated DL‑CACS from non‑gated scans. Agreement with ECG‑gated CACS was assessed using Spearman correlation and Bland–Altman analysis; kappa analysis evaluated categorical agreement for the Coronary Artery Calcium Data and Reporting System (CAC‑DRS). Diagnostic performance was evaluated by ROC/AUC. Univariable and multivariable logistic regression quantified associations between acquisition parameters and misclassification. Non‑gated DL‑CACS correlated strongly with gated CACS across LM, LAD, LCX, RCA and TOTAL scores (ρ ≤ 0.865; all p < 0.001). CAC‑DRS agreement was substantial (κ = 0.641), with accuracy 77.5% and AUC 0.769 (95% CI 0.754–0.785). Collimation width, rotation time, reconstruction slice thickness and reconstruction kernel were independent predictors of misclassification; tube voltage and pitch were not significant. Narrower collimation, slower rotation, thicker slices and smoother kernels were associated with higher odds of misclassification. DL‑based CACS from non–ECG‑gated chest CT shows good agreement with ECG‑gated CACS and useful accuracy, although performance remains sensitive to acquisition settings. In the future, protocol standardisation and algorithm robustness across vendors and parameters may further enhance clinical utility, supporting more consistent performance across sites and facilitating broader adoption in routine practice.
Chen et al. (Thu,) conducted a observational in Coronary atherosclerosis (n=1,213). Deep learning-derived coronary artery calcium scoring (DL-CACS) from non-gated chest CT vs. ECG-gated cardiac CT CACS was evaluated on Accuracy of CAC-DRS risk stratification (AUC 0.769, 95% CI 0.754-0.785, p=<0.001). Deep learning-derived coronary artery calcium scoring from non-gated chest CT achieved 77.5% accuracy and an AUC of 0.769 for CAC-DRS risk stratification compared to ECG-gated reference scans.