Optimized categorization algorithm of coronary artery calcification score on non-gated chest low-dose CT screening using iterative model reconstruction technique

Objectives: To investigate the optimized categorization algorithm of coronary artery calcification score (CACS) for more accurate risk assessment on non-gated chest low-dose CT (LDCT) screening using iterative model reconstruction (IMR) technique. Methods: We enrolled 102 patients who required coronary artery CTA examination and had coronary artery calcification (CAC) in this study. The CACS on non-gated LDCT and ECG-gated CT images were both measured by Agatston analysis software on Philips workstation. Results: According to the original algorithm (1-100, 100-400 and > 400), the CACS measured by non-gated LDCT scan showed a good agreement with ECG-gated CT scan (weighted kappa value of 0.602, P < 0.05). Two ROC curves were drawn to evaluate the accuracy of CACS categorization. The cutoff values were 87.44 and 255.26 respectively. Based on the best diagnostic cutoff value, the CACS measured by the non-gated LDCT scan showed an excellent agreement with ECG-gated CT scan (weighted kappa value of 0.781, P < 0.05). Conclusions: The CACS on non-gated LDCT may have been underestimated. We therefore developed an optimized categorization algorithm of non-gated CACS in this study.