Background: Coronary computed tomography angiography (CCTA) provides non-invasive quantitative assessments of plaque burden and composition. The quantitative assessment of plaque components requires the use of analysis software that provides reproducible semi-automated plaque detection and analysis. However, commercially available plaque analysis software can vary widely in the degree of automation, resulting in differences in terms of reproducibility and time spent. Aim: To compare the reproducibility and time spent of two CCTA analysis software tools using different algorithms for the quantitative assessment of coronary plaque volumes and composition in two independent patient cohorts. Methods: The study population included 100 patients from two different cohorts: 50 patients from a single-center (Siemens Healthineers, SOMATOM Force (DSCT)) and another 50 patients from a multi-center study (5 different > 64 slice CT scanner types). Quantitative measurements of total calcified and non-calcified plaque volume of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex coronary artery (LCX) were performed on a total of 300 coronaries by two independent readers, using two different CCTA analysis software tools (Tool #1: Siemens Healthineers, syngo.via Frontier CT Coronary Plaque Analysis and Tool #2: Siemens Healthineers, successor CT Coronary Plaque Analysis prototype). In addition, the total time spent for the analysis was recorded with both programs. Results: The patients in cohorts 1 and 2 were 62.8 ± 10.2 and 70.9 ± 11.7 years old, respectively, 10 (20.0%) and 35 (70.0%) were female and 34 (68.0%) and 20 (40.0%), respectively, had hyperlipidemia. In Cohort #1, the inter- and intra-observer variabilities for the assessment of plaque volumes per patient for Tool #1 versus Tool #2 were 22.8%, 22.0%, and 26.0% versus 2.3%, 3.9%, and 2.5% and 19.7%, 21.4%, and 22.1% versus 0.2%, 0.1%, and 0.3%, respectively, for total, noncalcified, and calcified lesions (p < 0.001 for all between Tools #1 and 2 both for inter- and intra-observer). The inter- and intra-observer variabilities using Tool #2 remained low at 2.9%, 2.7%, and 3.0% and 3.8%, 3.7%, and 4.0%, respectively, for total, non-calcified, and calcified lesions in Cohort #2. For each dataset, the median processing time was higher for Tool #1 versus Tool #2 (459.5 s IQR = 348.0–627.0 versus 208.5 s; IQR = 198.0–216.0) (p < 0.001). Conclusion: The plaque analysis Tool #2 (CT-guided PCI) encompassing a higher degree of automated support required less manual editing, was more time-efficient, and showed a higher intra- and inter-observer reproducibility for the quantitative assessment of plaque volumes both in a representative single-center and in a multi-center validation cohort.

Aim: To evaluate the impact of different lipid-lowering treatment intensities on high-risk plaque features and pericoronary adipose tissue (PCAT) attenuation in patients undergoing serial coronary computed tomography angiography (CCTA). Methods: Individuals with suspected or known coronary artery disease (CAD) from 11 imaging centers who underwent serial CCTA examinations were retrospectively analyzed. Plaque volumes and PCAT were quantified, and the presence of high-risk plaque features was semi-quantitatively assessed using the plaque feature score (PFS). Results: In total, 216 consecutive patients (mean age 63.1 ± 9.7 years, 26.4% female) were included. The mean observation and treatment timespan between the CCTA scans was 824.5 (interquartile range (IQR) = 463.0–1323.0) days (27.5 months). The regression of high-risk features was more common with high-intensity versus low or no lipid-lowering treatment (HR = 4.6, 95%CI = 1.8–12.0, p < 0.001) and was associated with the attenuated increase in non-calcified plaque volume (p < 0.001). PCAT_mean decreased with increasing intensity of lipid-lowering treatment (p = 0.01) but no associations were observed between the changes in PCAT and PFS or plaque volumes. Lipid-lowering drug intensity was predictive of PFS regression (p < 0.001), whereas baseline PCAT_RCA was predictive for PFS progression (p = 0.03), both independent of age, cardiovascular risk factors, and baseline plaque volumes. Conclusions: PCAT predicts the progression of high-risk coronary plaque features. High-intensity lipid-lowering drugs may cause the regression of high-risk plaque features through a plaque ‘delipidization’ process. Future trials are now warranted, studying if this process is potentially associated with improved clinical outcomes.