Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques

The RF model in the combined model group based on non-contrast CT scan PCAT can predict the presence of NCP more accurately and has the potential for preliminary screening of the NCP.

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Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2024-12-18
DOI
https://doi.org/10.1177/08953996241292476
Citations
3
Primary Topic
Cardiovascular Disease and Adiposity
Type
article
Field-Weighted Citation Impact
1.21
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article

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques

Ning Dong, Yaoqiang Ren, Shunxin Hu, Ziyue Wang et al.
3 citations
Journal of X-Ray Science and Technology
Cardiovascular Disease and Adiposity
1.21
article

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques

Ning Dong, Yaoqiang Ren, Shunxin Hu, Ziyue Wang, Li Wang, Yan Ding, Junli Yu, J. Sheng
article en
3 citations

Abstract

The RF model in the combined model group based on non-contrast CT scan PCAT can predict the presence of NCP more accurately and has the potential for preliminary screening of the NCP.

Journal of X-Ray Science and TechnologyVol. 33(1)
Yantaishan Hospital (CN), Qiqihar Medical University (CN)
Good health and well-being
Openalex Percentile: Top 17%
Cardiovascular Disease and Adiposity
1.21
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