What is the clinical evidence from this study?

Study design: Observational. Population: Non-calcified coronary plaques (n=431). Intervention: Combined clinical-radiomics models of pericoronary adipose tissue on non-contrast CT. Primary outcome: Diagnostic efficacy for non-calcified plaques (Model 1) and vulnerable plaques (Model 2) (AUC 0.896).

April 22, 2022Open Access

Non-contrast CT-based radiomic signature of pericoronary adipose tissue for screening non-calcified plaque

Key Result

A combined clinical-radiomics model based on non-contrast CT images of pericoronary adipose tissue effectively identified non-calcified plaques with an AUC of 0.896.

Study Design

Type

Observational (n=431)

Structured PICO

Does a combined clinical-radiomics model of pericoronary adipose tissue on non-contrast CT accurately detect and characterize non-calcified plaques in patients undergoing CCTA?

Population

431 patients undergoing Coronary Computed Tomography Angiography, including 173 with non-calcified plaques of the right coronary artery and 258 with no abnormality.

Exposure

Combined clinical-radiomics models of pericoronary adipose tissue (PCAT) on non-contrast CT scan

Outcome

Diagnostic performance (AUC-ROC, sensitivity, specificity, accuracy) for detecting non-calcified plaques and vulnerable plaquessurrogate

A combined clinical-radiomics model using non-contrast CT images of pericoronary adipose tissue shows good diagnostic efficacy for identifying non-calcified and vulnerable coronary plaques.

Main Result

Effect estimate: AUC 0.896

Abstract

Abstract Objective. To develop two combined clinical-radiomics models of pericoronary adipose tissue (PCAT) for the presence and characterization of non-calcified plaques on non-contrast CT scan. Approach. Altogether, 431 patients undergoing Coronary Computed Tomography Angiography from March 2019 to June 2021 who had complete data were enrolled, including 173 patients with non-calcified plaques of the right coronary artery(RCA) and 258 with no abnormality. PCAT was segmented around the proximal RCA on non-contrast CT scan (calcium score acquisition). Two best models were established by screening features and classifiers respectively using FeAture Explorer software. Model 1 distinguished normal coronary arteries from those with non-calcified plaques, and model 2 distinguished vulnerable plaques in non-calcified plaques. Performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). Main results. 4 and 9 features were selected for the establishment of the radiomics model respectively through Model 1 and 2. In the test group, the AUC values, sensitivity, specificity and accuracy were 0.833%, 78.3%, 80.8%, 76.6% and 0.7467%, 75.0%, 77.8%, 73.5%, respectively. The combined model including radiomics features and independent clinical factors yielded an AUC, sensitivity, specificity and accuracy of 0.896%, 81.4%, 86.5%, 77.9% for model 1 and 0.752%, 75.0%, 77.8%, 73.5% for model 2. Significance. The combined clinical-radiomics models based on non-contrast CT images of PCAT had good diagnostic efficacy for non-calcified and vulnerable plaques.