Radiol Med. 2026 Oct 30. doi: 10.1007/s11547-026-02302-0. Online ahead of print.
ABSTRACT
OBJECTIVE: To evaluate the predictive efficacy and risk stratification potential of coronary computed tomography angiography (CCTA)-derived pericoronary adipose tissue (PCAT) radiomics for major adverse cardiovascular events (MACEs).
MATERIALS AND METHODS: We retrospectively enrolled patients with suspected coronary artery disease (CAD) who underwent CCTA at our hospital between June 2017 and June 2020, with 5-year follow-up for MACE occurrence. Patients were grouped by MACE status. Machine learning was applied to construct and compare three models: PCAT radiomics model, fat attenuation index (FAI) model, and clinical model. Prospective validation was conducted in 578 patients with suspected CAD undergoing CCTA, who were stratified into high- and low-risk groups by the optimal cutoff of the PCAT radiomics model. The predictive and risk stratification performance of all models was assessed.
RESULTS: Fourteen PCAT radiomics features (5 first-order and 9 texture features) were finally screened. The PCAT radiomics model showed significantly superior MACE predictive performance over the FAI and clinical models (P < 0.001). Only the PCAT radiomics model identified significantly higher MACE risk in the high-risk versus low-risk group (HR=2.611, 95% CI: 1.806-3.775, P < 0.001), with progressively reduced MACE-free survival in the high-risk group during follow-up (P < 0.001). No such significant between-group differences were found in the FAI and clinical models (all P > 0.05).
CONCLUSION: The PCAT radiomics model showed favorable performance for predicting the composite MACE endpoint, which was predominantly driven by worsening stable or unstable angina, and provided better risk stratification than the FAI and clinical models.
PMID:42961710 | DOI:10.1007/s11547-026-02302-0