1. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
- Author
-
Nikant Sabharwal, Alexios S. Antonopoulos, Sujatha Kesavan, Milind Y. Desai, Keith M. Channon, Charalambos Antoniades, Evangelos Oikonomou, Ioannis Akoumianakis, Cheerag Shirodaria, Erika Hutt Centeno, John E. Deanfield, David E. Newby, Christos P Kotanidis, Marc R. Dweck, Mohamed Marwan, Sheena Thomas, Lampson M. Fan, Brian P. Griffin, Andrew Kelion, Michelle C. Williams, Jemma C. Hopewell, Alaa Alashi, Katharine E Thomas, Scott D. Flamm, Stephan Achenbach, L Herdman, Edwin J R van Beek, Maria Lyasheva, and Stefan Neubauer
- Subjects
Genetic Markers ,Male ,Imaging biomarker ,Computed Tomography Angiography ,Fast Track Clinical Research ,Adipose tissue ,Coronary Artery Disease ,030204 cardiovascular system & hematology ,Coronary Angiography ,Machine learning ,computer.software_genre ,Risk Assessment ,030218 nuclear medicine & medical imaging ,Coronary artery disease ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Vascularity ,Fibrosis ,medicine ,Humans ,Myocardial infarction ,Computed tomography ,Risk stratification ,Aged ,Radiomics ,business.industry ,Gene Expression Profiling ,Middle Aged ,medicine.disease ,Plaque, Atherosclerotic ,3. Good health ,Coronary Calcium Score ,Editor's Choice ,Phenotype ,Adipose Tissue ,Case-Control Studies ,Female ,Artificial intelligence ,medicine.symptom ,Cardiology and Cardiovascular Medicine ,business ,Transcriptome ,computer ,Biomarkers ,Mace ,Algorithms ,Follow-Up Studies - 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 involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P Conclusion The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.
- Published
- 2019
- Full Text
- View/download PDF