A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Abstract Background Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process in...

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Veröffentlicht in:	European heart journal Jg. 40; H. 43; S. 3529 - 3543
Hauptverfasser:	Oikonomou, Evangelos K, Williams, Michelle C, Kotanidis, Christos P, Desai, Milind Y, Marwan, Mohamed, Antonopoulos, Alexios S, Thomas, Katharine E, Thomas, Sheena, Akoumianakis, Ioannis, Fan, Lampson M, Kesavan, Sujatha, Herdman, Laura, Alashi, Alaa, Centeno, Erika Hutt, Lyasheva, Maria, Griffin, Brian P, Flamm, Scott D, Shirodaria, Cheerag, Sabharwal, Nikant, Kelion, Andrew, Dweck, Marc R, Van Beek, Edwin J R, Deanfield, John, Hopewell, Jemma C, Neubauer, Stefan, Channon, Keith M, Achenbach, Stephan, Newby, David E, Antoniades, Charalambos
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 14.11.2019
Schlagworte:	Adipose Tissue - diagnostic imaging Adipose Tissue - pathology Aged Algorithms Case-Control Studies Computed Tomography Angiography Coronary Artery Disease - diagnostic imaging Coronary Artery Disease - genetics Coronary Artery Disease - pathology Female Follow-Up Studies Gene Expression Profiling - methods Genetic Markers Humans Machine Learning Male Middle Aged Phenotype Plaque, Atherosclerotic - diagnostic imaging Plaque, Atherosclerotic - genetics Plaque, Atherosclerotic - pathology Risk Assessment Transcriptome Adipose tissue Computed tomography Coronary artery disease Machine learning Risk stratification Radiomics
ISSN:	0195-668X, 1522-9645, 1522-9645
Online-Zugang:	Volltext
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