RT Journal Article SR Electronic T1 Machine-learning with 18F-sodium fluoride PET and quantitative plaque analysis on CT angiography for the future risk of myocardial infarction JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP jnumed.121.262283 DO 10.2967/jnumed.121.262283 A1 Jacek Kwiecinski A1 Evangelos Tzolos A1 Mohammed Meah A1 Sebastien Cadet A1 Philip D Adamson A1 Kajetan Grodecki A1 Nikhil V Joshi A1 Alastair J Moss A1 Michelle C Williams A1 Edwin JR van Beek A1 Daniel S. Berman A1 David E Newby A1 Damini Dey A1 Marc R Dweck A1 Piotr J. Slomka YR 2021 UL http://jnm.snmjournals.org/content/early/2021/04/23/jnumed.121.262283.abstract AB Coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) and computed tomography (CT) angiography-based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary 18F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis measures and 18F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65±9 years; 84% male), 22 subjects experienced a myocardial infarction over the 53 [40-59] months of follow-up. On univariable receiver-operator-curve analysis, only 18F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% confidence interval [CI] 0.68-0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53-0.76;) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60-0.84). After inclusion of all available data (clinical, quantitative plaque and 18F-NaF PET), we achieved a substantial improvement (P = 0.008 versus 18F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79-0.91). Conclusion: Both 18F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model.