Abstract
BackgroundNon-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown. MethodsWe performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 mm3. ResultsA total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 mm3 (IQR 3.5–13.9 mm3), most of which was non-calcified (median 6.2 mm3; 2.9–12.3 mm3). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 ± 1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 mm3 (IQR 8.3–45.2 mm3) and 13.8 mm3 (IQR 5.7–33.4 mm3), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up. ConclusionIn this retrospective analysis from the PARADIGM study, small plaques (<50 mm3) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA.
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