Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study.

BACKGROUND: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. OBJECTIVE: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). METHODS: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. RESULTS: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. CONCLUSION: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.

CT ​Evaluation ​by ​Artificial ​Intelligence ​for ​Atherosclerosis, Stenosis and Vascular ​Morphology ​(CLARIFY): ​A ​Multi-center, international study.

BACKGROUND: Atherosclerosis evaluation by coronary computed tomography angiography (CCTA) is promising for coronary artery disease (CAD) risk stratification, but time consuming and requires high expertise. Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations. We hypothesized AI aided analysis allows for rapid, accurate evaluation of vessel morphology and stenosis. METHODS: This was a multi-site study of 232 patients undergoing CCTA. Studies were analyzed by FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by three L3 readers. CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category. RESULTS: AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively. Bland-Altman plots depict agreement between expert reader and AI determined maximal diameter stenosis for per-vessel (mean difference -0.8%; 95% CI 13.8% to -15.3%) and per-patient (mean difference -2.3%; 95% CI 15.8% to -20.4%). L3 and AI agreed within one CAD-RADS category in 228/232 (98.3%) exams per-patient and 923/924 (99.9%) vessels on a per-vessel basis. There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution. CONCLUSIONS: AI-aided approach to CCTA interpretation determines coronary stenosis and CAD-RADS category in close agreement with consensus of L3 expert readers. There was a wide range of atherosclerosis identified through AI.

The evolving role of coronary CT angiography in Acute Coronary Syndromes.

In the United States, non-obstructive coronary disease has been on the rise, and each year, nearly one million adults suffer myocardial infarction, 70% of which are non-ST-segment elevation myocardial infarction (NSTEMI). In addition, approximately 15% of patients suffering NSTEMI will have subsequent readmission for a recurrent acute coronary syndrome (ACS). While invasive angiography remains the standard of care in the diagnostic and therapeutic approach to these patients, these methods have limitations that include procedural complications, uncertain specificity in diagnosis of the culprit lesion in patients with multi-vessel coronary artery disease (CAD), and challenges in following coronary disease over time. The role of coronary computed tomography angiography (CCTA) for evaluating patients with both stable and acute chest pain has seen a paramount upshift in the last decade. This paper reviews the established role of CCTA for the rapid exclusion of obstructive plaque in troponin negative acute chest pain, while exploring opportunities to address challenges in the current approach to evaluating NSTEMI.