The diagnostic value of a coronary computed tomography angiography scan-based radiomics model for coronary stenosis

Ke Niu; Sigeng Chen; Jingfan Fan; Jian Yang

doi:10.1117/12.3036824

13 July 2024 The diagnostic value of a coronary computed tomography angiography scan-based radiomics model for coronary stenosis

Ke Niu, Sigeng Chen, Jingfan Fan, Jian Yang

Author Affiliations +

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132081G (2024) https://doi.org/10.1117/12.3036824
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

Coronary artery disease (CAD) is a cardiovascular disease characterized by coronary stenosis or occlusion due to atherosclerosis, which may result in a number of symptoms, including myocardial ischemia, angina and heart failure. Coronary computed tomography angiography (CCTA) is a diagnostic assessment for CAD. Radiology encompasses a vast amount of quantitative, high-dimensional features and transform medical images into a rich dataset that can be explored for insights. This study introduces an approach leveraging radiology features for the automated detection of coronary artery stenosis. We extract curved planar reconstruction (CPR) images along with the segmentation of the coronary arteries from three-dimensional CCTA images and extract radiomic features from the segmented regions of interest. Considering the high-dimensional nature of radiology features, we utilize techniques like LASSO regression to reduce the dimensionality of these features. We construct a graph convolutional network (GCN) block to fuse radiomic features and deep features embed this block within an encoder-decoder network. In the visualization analysis of coronary radiology features, there is a qualitative distinction between lipid and calcification regions, demonstrating the diagnostic value of radiology in coronary stenosis detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ke Niu, Sigeng Chen, Jingfan Fan, and Jian Yang "The diagnostic value of a coronary computed tomography angiography scan-based radiomics model for coronary stenosis", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132081G (13 July 2024); https://doi.org/10.1117/12.3036824

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KEYWORDS

Radiology

Radiomics

Feature extraction

Arteries

Cooccurrence matrices

Diagnostics

Computed tomography

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