Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19

Abstract

Background: Direct evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19.

Methods: For this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes.

Findings: Patients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [<6·99] C19-RS; hazard ratio [HR] 3·31 [95% CI 1·49-7·33], p=0·0033; and 2·58 [1·10-6·05], p=0·028), adjusted for clinical factors, biochemical biomarkers of inflammation and myocardial injury, and technical parameters. The adjusted HR for in-hospital mortality was 8·24 (95% CI 2·16-31·36, p=0·0019) in patients who received no dexamethasone treatment, but 2·27 (0·69-7·55, p=0·18) in those who received dexamethasone after the scan, suggesting that vascular inflammation might have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0·61, p=0·00031) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways.

Interpretation: Radiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy.

Funding: Engineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation.

Figure 1

Workflow for building the radiotranscriptomic signature C19-RS Workflow depicting the multiple steps taken to develop the radiomic signature C19-RS. To limit our analysis to radiomic features that could be of value as imaging biomarkers, we did a series of filtering steps, to exclude features that are not stable in test–retest analyses, features that are highly correlated with each other, and features that are significantly correlated with BMI or intrathoracic adipose tissue volume, to retain only features that predict the outcome variable with the same direction within study arm 1 and 20% of the exploratory study arm 3 subpopulation. Finally, recursive feature elimination with a random forest algorithm and repeated five-times cross-validation showed a plateau in the accuracy of the trained model with 33 final features. Those features were next used within the study arm 1 population to train an XGBoost algorithm using decisions trees in order to identify patients with activated inflammatory pathways within their arterial vasculature. The raw product of the algorithm was named C19-RS. PVAT=perivascular adipose tissue.

Figure 2

Unsupervised hierarchical clustering of cytokine production genes expressed in human internal mammary arteries in the study arm 1 population (A) Unsupervised hierarchical clustering of the list of genes relevant to inflammation annotated in the Gene Ontology (GO) terms “Cytokine Production” (GO:0001816) from the GO main domain “Biological Process” and “Cytokine Activity” (GO:005125) from the GO main domain “Molecular Function”. Hierarchical clustering was done with Ward's method and Minkowski distance, with the Minkowski distance metric, p, set to 10. (B) Enriched signalling pathways of differentially expressed genes between the two clusters of vascular inflammation identified through ConsensusPathDB. (C) Feature importance of the top 20 radiomic features comprising C19-RS and their correlation with cpm (count per million) values with key inflammatory genes in the study arm 1 population (the asterisk denotes significance, p<0·05 by the Spearman's ρ correlation coefficient). An index of the radiomic features included in the figure is presented in the appendix (p 41). The full list of the 145 genes selected is also provided in the appendix (p 32). IMA=internal mammary artery.

Figure 3

C19-RS for COVID-19 detection (A) Illustration of PVAT mapping in CCTA in a patient 3 years before and during SARS-CoV-2 infection. (B) C19-RS was significantly higher in patients who developed COVID-19 compared to baseline scans, whereas in matched paired controls C19-RS showed no significant change over time. Data are presented as box plots (medians and IQRs). (C) Comparison of the δ values in C19-RS between baseline and follow-up. Data are presented as means (SEs). (D) C19-RS values were higher in SARS-CoV-2-positive patients, with an area under the curve for COVID-19 detection of 0·66 (95% CI 0·59–0·74, p<0·001). (E) Patients with the B.1.1.7 SARS-CoV-2 variant after viral genome sequencing had significantly higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant, suggesting higher degrees of vascular inflammation (data presented as means [SEs] for visualisation). Comparisons made by Wilcoxon signed-rank test in panel B and by Mann-Whitney U test in all other panels. AU=abstract units. CCTA=coronary CT angiogram. HU=Hounsfield units. IMA=internal mammary artery. PVAT=perivascular adipose tissue.

Figure 4

Prognostic value of C19-RS (A) Univariate receiver operating characteristic (ROC) analysis for the ability of C19-RS to predict death in hospital and a composite endpoint of death in-hospital or intensive care unit (ICU) admission, or both, in the SARS-CoV-2-positive study arm 3 population (n=254). (B) Comparison of ROCs derived from logistic regression models showcasing the additive value of C19-RS in the SARS-CoV-2-positive study arm 3 population (n=254). Model 1 consists of demographic variables (age, sex, hypertension, hyperlipidaemia, diabetes, BMI, presence of coronary artery disease, and history of chronic obstructive pulmonary disease), and tube voltage. Model 2 includes, in addition to the variables in model 1, biochemistry biomarkers (white blood cell count, C-reactive protein, and plasma troponin). Model 3 includes all parameters in model 2 plus C19-RS. (C) Kaplan–Meier curve and adjusted hazard ratio (HR) for in-hospital death for high versus low C19-RS groups in the SARS-CoV-2-positive study arm 3 population (n=254; n=139 from the first wave and n=115 from the second wave) with 39 deaths. HR adjusted for age older than 65 years, sex, cardiovascular risk factors (hypertension, hyperlipidaemia, diabetes, BMI, and presence of coronary artery disease), C-reactive protein plasma concentrations, white blood cell count, plasma troponin, history of chronic obstructive pulmonary disease, tube voltage, and dexamethasone treatment. (D) Kaplan–Meier curve and adjusted HR for in-hospital death for high versus low C19-RS groups in the SARS-CoV-2-positive study arm 3 population that did not receive dexamethasone treatment (n=144 with 19 deaths). (E) Kaplan–Meier curve and adjusted HR for in-hospital death for high versus low C19-RS groups in the SARS-CoV-2-positive study arm 3 population that received dexamethasone treatment (n=110 with 20 deaths). HRs in panels D and E adjusted for age older than 65 years, sex, cardiovascular risk factors (hypertension, hyperlipidaemia, diabetes, and BMI), C-reactive protein plasma concentrations, white blood cell count, history of chronic obstructive pulmonary disease, and tube voltage. (F) Kaplan–Meier curve and adjusted HR for in-hospital death for high versus low C19-RS groups in the external validation study arm 4 population (n=104 with 34 deaths). HR adjusted for age older than 65 years, sex, cardiovascular risk factors (hypertension, hyperlipidaemia, diabetes, BMI, and presence of coronary artery disease), C-reactive protein plasma concentrations, white blood cell count, plasma troponin, history of chronic obstructive pulmonary disease, and tube voltage. *p_DeLong value less than 0·05 for AUC comparisons.