Using AI-Quantitative CT to evaluate the relationship between coronary artery calcium and segment involvement scores in quantifying coronary plaque burden

Accurate assessment of coronary plaque burden is essential for risk stratification in coronary artery disease (CAD). The Coronary Artery Disease – Reporting and Data System (CAD-RADS) 2.0 classification incorporates P-scores derived from coronary artery calcium (CAC) and segment involvement scores (SIS) to semi-quantitatively characterize plaque burden. However, limited data exist on the concordance and clinical implications of these two plaque characteristics. We retrospectively analyzed 461 coronary CT angiography (CCTA) studies using a commercial AI-based quantitative CT (AI-QCT) plaque analysis platform (CLEERLY). CAC scores were extracted using prompt-based natural language processing, and SIS were determined by AI quantification. Unilateral P-scores for CAC (PCAC) and SIS (PSIS) were calculated according to CAD-RADS 2.0 thresholds. Agreement between PCAC and PSIS was assessed using Cohen’s kappa, Wilcoxon signed-rank test, and McNemar’s test. Subgroup analyses evaluated distribution of P-score categories, sex and age in CAC = 0 patients, and the impact of symptom status using Wilcoxon rank-sum, chi-square, Fisher’s exact test, and ordinal logistic regression. The median patient age was 67 years, and 38.7% were female. CAC and SIS produced concordant P-scores in 23% of cases, with discordance in 77%. SIS determined the final P-score in 75.5% and CAC in only 1.5%. Cohen’s kappa indicated modest agreement (κ = 0.40, p < 0.01), while Wilcoxon signed-rank and McNemar’s tests revealed significant discordance, with SIS tending to assign higher P-score categories (p < 0.01). Among patients with CAC = 0 (n = 97; median age 62; 65% female), 95% had non-calcified plaque by AI-QCT, and only 4 had both CAC and SIS of zero. P-score, PCAC, and PSIS distributions did not differ significantly by symptom status (all p > 0.05), although a significant difference was detected for PSIS on chi-square and Fisher’s exact tests but not by ordinal regression or Wilcoxon rank-sum. Further analysis demonstrated that discordance was most prominent in higher P-score categories and was primarily driven by non-calcified plaque. Our study demonstrates frequent discordance between CAC and SIS in CAD-RADS 2.0 plaque burden classification, particularly due to the detection of non-calcified plaque by SIS. Reliance on either CAC or SIS alone may result in risk misclassification, especially for patients with CAC = 0, the majority of whom had non-calcified plaque by AI-QCT. These findings highlight the need for standardized criteria and integration of both measures in automated plaque quantification to improve cardiovascular risk assessment, especially in discordant populations.