Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

Jin Shang,Shaowei Ma,Yang Hou,Fuchun Xie,Yue Ma,Ting Liu,Jinzhu Yang,Qian Zhang,Yuxue Dang,Linlin Yang,Quanmei Ma,Ke Zhou,Yan Guo

doi:10.1007/s00330-021-08109-z

Abstract

ObjectivesTo evaluate whether radiomics signature of pericoronary adipose tissue (PCAT) based on coronary computed tomography angiography (CCTA) could improve the prediction of future acute coronary syndrome (ACS) within 3 years.MethodsWe designed a retrospective case-control study that patients with ACS (n = 90) were well matched to patients with no cardiac events (n = 1496) during 3 years follow-up, then which were randomly divided into training and test datasets with a ratio of 3:1. A total of 107 radiomics features were extracted from PCAT surrounding lesions and 14 conventional plaque characteristics were analyzed. Radiomics score, plaque score, and integrated score were respectively calculated via a linear combination of the selected features, and their performance was evaluated with discrimination, calibration, and clinical application.ResultsRadiomics score achieved superior performance in identifying patients with future ACS within 3 years in both training and test datasets (AUC = 0.826, 0.811) compared with plaque score (AUC = 0.699, 0.640), with a significant difference of AUC between two scores in the training dataset (p = 0.009); while the improvement of integrated score discriminating capability (AUC = 0.838, 0.826) was non-significant. The calibration curves of three predictive models demonstrated a good fitness respectively (all p > 0.05). Decision curve analysis suggested that integrated score added more clinical benefit than plaque score. Stratified analysis revealed that the performance of three predictive models was not affected by tube voltage, CT version, different sites of hospital.ConclusionCCTA-based radiomics signature of PCAT could have the potential to predict the occurrence of subsequent ACS. Radiomics-based integrated score significantly outperformed plaque score in identifying future ACS within 3 years.Key Points• Plaque score based on conventional plaque characteristics had certain limitations in the prediction of ACS.• Radiomics signature of PCAT surrounding plaques could have the potential to improve the predictive ability of subsequent ACS.• Radiomics-based integrated score significantly outperformed plaque score in the identification of future ACS within 3 years.

Highlights

Acute coronary syndrome (ACS) can be often the first manifestation of coronary artery disease (CAD) and the main cause of death in the majority of the world’s population [1, 2]
We found that two plaque features (MLD and high-risk plaque (HRP)) were significantly associated with the occurrence of future ACS using univariate and multivariate logistic regression, they were combined to construct a plaque score by multivariate logistic regression analysis
We developed an integrated score that incorporated radiomics features of pericoronary adipose tissue (PCAT) surrounding target lesions and significant plaque predictors based on coronary computed tomography angiography (CCTA) and validated the performance with respect to discrimination, calibration, and clinical application

Summary

Introduction

Acute coronary syndrome (ACS) can be often the first manifestation of coronary artery disease (CAD) and the main cause of death in the majority of the world’s population [1, 2]. As a widely used non-invasive imaging modality, coronary computed tomography angiography (CCTA) has shown its clinical value by enabling robust coronary plaque characterization and quantification [3, 4], especially for the identification of adverse plaque characteristics (APC) [5]. As plaque rupture is a complicated biomechanical process, whether the clinical outcome of vulnerable plaques developed into ACS may be affected by several factors. Vascular inflammation is recognized as a key factor to both plaque formation and rupture, resulting in the occurrence of subsequent ACS [6]. A comprehensive evaluation combining plaque characteristics with vascular inflammation may further enhance the prediction of ACS

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