With growing adoption of coronary computed tomographic angiography (CTA), there is increasing evidence for and interest in the prognostic importance of atherosclerotic plaque volume. Manual tools for plaque segmentation are cumbersome, and their routine implementation in clinical practice is limited. The aim of this study was to develop nomographic quantitative plaque values from a large consecutive multicenter cohort using coronary CTA. Quantitative assessment of total atherosclerotic plaque and plaque subtype volumes was performed in patients undergoing clinically indicated coronary CTA, using an Artificial Intelligence-Enabled Quantitative Coronary Plaque Analysis tool. A total of 11,808 patients were included in the analysis; their mean age was 62.7 ± 12.2 years, and 5,423 (45.9%) were women. The median total plaque volume was 223 mm³ (IQR: 29-614 mm³) and was significantly higher in male participants (360 mm³; IQR: 78-805 mm³) compared with female participants (108 mm³; IQR: 10-388 mm³) (P < 0.0001). Total plaque increased with age in both male and female patients. Younger patients exhibited a higher prevalence of noncalcified plaque. The distribution of total plaque volume and its components was reported in every decile by age group and sex. The authors developed pragmatic age- and sex-stratified percentile nomograms for atherosclerotic plaque measures using findings from coronary CTA. The impact of age and sex on total plaque and its components should be considered in the risk-benefit analysis when treating patients. Artificial Intelligence-Enabled Quantitative Coronary Plaque Analysis work flows could provide context to better interpret coronary computed tomographic angiographic measures and could be integrated into clinical decision making.

BACKGROUND: Luminal stenosis, computed tomography–derived fractional-flow reserve (FFR _CT ), and high-risk plaque features on coronary computed tomography angiography are all known to be associated with adverse clinical outcomes. The interactions between these variables, patient outcomes, and quantitative plaque volumes have not been previously described. METHODS: Patients with coronary computed tomography angiography (n=4430) and one-year outcome data from the international ADVANCE (Assessing Diagnostic Value of Noninvasive FFR _CT in Coronary Care) registry underwent artificial intelligence–enabled quantitative coronary plaque analysis. Optimal cutoffs for coronary total plaque volume and each plaque subtype were derived using receiver-operator characteristic curve analysis. The resulting plaque volumes were adjusted for age, sex, hypertension, smoking status, type 2 diabetes, hyperlipidemia, luminal stenosis, distal FFR _CT , and translesional delta-FFR _CT . Median plaque volumes and optimal cutoffs for these adjusted variables were compared with major adverse cardiac events, late revascularization, a composite of the two, and cardiovascular death and myocardial infarction. RESULTS: At one year, 55 patients (1.2%) had experienced major adverse cardiac events, and 123 (2.8%) had undergone late revascularization (>90 days). Following adjustment for age, sex, risk factors, stenosis, and FFR _CT , total plaque volume above the receiver-operator characteristic curve–derived optimal cutoff (total plaque volume >564 mm ³ ) was associated with the major adverse cardiac event/late revascularization composite (adjusted hazard ratio, 1.515 [95% CI, 1.093–2.099]; P =0.0126), and both components. Total percent atheroma volume greater than the optimal cutoff was associated with both major adverse cardiac event/late revascularization (total percent atheroma volume >24.4%; hazard ratio, 2.046 [95% CI, 1.474–2.839]; P 37.17%, hazard ratio, 4.53 [95% CI, 1.943–10.576]; P =0.0005). Calcified, noncalcified, and low-attenuation percentage atheroma volumes above the optimal cutoff were associated with all adverse outcomes, although this relationship was not maintained for cardiovascular death/myocardial infarction in analyses stratified by median plaque volumes. CONCLUSIONS: Analysis of the ADVANCE registry using artificial intelligence–enabled quantitative plaque analysis shows that total plaque volume is associated with one-year adverse clinical events, with incremental predictive value over luminal stenosis or abnormal physiology by FFR _CT . REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02499679.

Automated artificial intelligence–based quantification of coronary plaque volume from cardiac CT was highly correlated with the invasive reference standard of intravascular ultrasound.

Purpose To evaluate the performance of a new artificial intelligence (AI)-based tool by comparing the quantified stenosis severity at coronary CT angiography (CCTA) with a reference standard derived from invasive quantitative coronary angiography (QCA). Materials and Methods This secondary, post hoc analysis included 120 participants (mean age, 59.7 years ± 10.8 [SD]; 73 [60.8%] men, 47 [39.2%] women) from three large clinical trials (AFFECTS, P3, REFINE) who underwent CCTA and invasive coronary angiography with QCA. Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95; P < .001) and 0.93 (95% CI: 0.89, 0.97; P < .001), respectively. The AUCs to predict DS of 50% or greater and 70% or greater on a per-patient basis were 0.93 (95% CI: 0.88, 0.97; P < .001) and 0.88 (95% CI: 0.81, 0.94; P < .001), respectively. Conclusion AI-CSQ at CCTA demonstrated a high diagnostic performance compared with QCA both on a per-patient and per-vessel basis, with high sensitivity for stenosis detection. Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. Published under a CC BY 4.0 license.