Optimized categorization algorithm of coronary artery calcification score on non-gated chest low-dose CT screening...

Area of Science:

• Radiology
• Cardiovascular Imaging
• Medical Diagnostics

Background:

• Coronary artery calcification score (CACS) is crucial for cardiovascular risk stratification.
• Non-gated low-dose CT (LDCT) is increasingly used for screening.
• Accurate CACS measurement on LDCT is essential for reliable risk assessment.

Purpose of the Study:

• To develop and validate an optimized categorization algorithm for CACS.
• To improve the accuracy of risk assessment using non-gated LDCT.
• To compare CACS categorization between non-gated LDCT and ECG-gated CT.

Main Methods:

• 102 patients with coronary artery calcification (CAC) were included.
• CACS was measured on both non-gated LDCT and ECG-gated CT using Agatston analysis.
• An optimized categorization algorithm was developed and validated using ROC curves.

Main Results:

• The original CACS categorization on non-gated LDCT showed good agreement with ECG-gated CT (kappa=0.602).
• An optimized algorithm with cutoff values of 87.44 and 255.26 demonstrated excellent agreement (kappa=0.781).
• Optimized categorization significantly improved CACS accuracy on non-gated LDCT.

Conclusions:

• Coronary artery calcification score (CACS) may be underestimated on non-gated LDCT.
• An optimized categorization algorithm enhances CACS accuracy for risk assessment.
• This optimized algorithm provides a more reliable method for evaluating cardiovascular risk using LDCT.