Noninvasive prediction of coronary artery disease progression using pericoronary adipose tissue radiomics from coronary CTA

OBJECTIVE: Coronary artery disease (CAD) progression is directly associated with major adverse cardiovascular events and death. This study aimed to construct a pericoronary adipose tissue (PCAT) radiomics model to predict subsequent progression in patients with CAD. METHODS: Data from 116 patients who had at least 2 coronary computed tomography angiography (CCTA) exams between March 1, 2020, and August 30, 2022, were collected at our institution. Obstructive stenosis, CAD-RADS classification, segment involvement score (SIS), and segment stenosis score (SSS) were noted. The radiomics features of the proximal to the left anterior descending artery, left circumflex artery, and right coronary artery were extracted on CCTA images using fully automated software. According to CAD-RADS, SIS, and SSS, non-progression was identified in 96, 80, and 72 patients and progression was identified in 20, 36, and 44 patients, respectively. All patients were randomly divided into the training and testing cohorts in a 7:3 ratio. Cox regression models were constructed based on PCAT radiomics signatures, and their predictive abilities were measured using receiver operating characteristic curves. RESULTS: We included 116 patients (age 58.00 [53.25, 64.00] years; 78 [67.20%] were male). After screening, 16 PCAT radiomics features were identified as being significantly related to CAD progression. The Cox regression models had area under the curve values of 0.841, 0.838, and 0.725 in the training cohort and 0.818, 0.817, and 0.851 in the testing cohort, respectively, to predict 2-year CAD-RADS, SIS, and SSS progression. CONCLUSIONS: PCAT-based radiomics models demonstrated promising performance in predicting subsequent CAD progression. ADVANCES IN KNOWLEDGE: PCAT-based radiomics signatures derived from coronary CT angiography provided incremental predictive value for CAD progression beyond conventional imaging markers (CAD-RADS, SIS, SSS), and may serve as noninvasive imaging biomarkers for individualized risk stratification.