Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.
Kwiecinski J., Kolossváry M., Tzolos E., Meah MN., Adamson PD., Joshi NV., Williams MC., van Beek EJR., Berman DS., Maurovich-Horvat P., Newby DE., Dweck MR., Dey D., Slomka PJ.
BACKGROUND: Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. METHODS: Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. RESULTS: Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). CONCLUSIONS: In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.