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 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 < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. 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.

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

“…At the time of this work, only one study could be found that reports using ML-modelling of CT images to classify individuals according to cardiovascular health outcomes. In this study, coronary CT angiography images were combined with ML-modelling to develop an artificial intelligence-based imaging biomarker to predict myocardial infarction in healthy subjects 41 . However, the use of CT images of skeletal muscle for classifying cardiovascular health outcomes remains unreported.…”

Vulnerable plaques and patients: state-of-the-art

“…15 Advanced computational modelling allows the use of increasing amount of radiographic data (radiomics) 16 in clinical risk estimation, i.e. a new machine learning-based coronary computed tomography angiography (CCTA)-derived profiling of adverse fibrotic and microvascular perivascular adipose tissue remodelling has improved cardiac risk prediction using CCTA over and above the current state-of-the-art 17 (Figure 1). The advances in non-invasive coronary imaging, including CCTA, magnetic resonance angiography, and positron emission tomography ( Figure 2), have been recently reviewed in detail elsewhere.…”

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

“…However, traditional regression approaches do not accommodate complex interaction between patient characteristics, but machine learning methods may be a promising new approach that gains acceptance in cardiovascular medicine. [9][10][11] Among patients with persistent AF, we aimed to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.…”