Featured Publications
A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019, 40: 3529-3543. PMID: 31504423, PMCID: PMC6855141, DOI: 10.1093/eurheartj/ehz592.Peer-Reviewed Original ResearchConceptsPerivascular adipose tissueFat attenuation indexCoronary CT angiographyCardiac risk predictionCoronary perivascular adipose tissueMajor adverse cardiac eventsCT angiographyRisk predictionHigh-risk plaque featuresPerivascular fat attenuation indexRadiomic featuresAdverse cardiac eventsConsecutive eligible participantsSCOT-HEART trialTraditional risk stratificationCoronary artery diseaseCoronary calcium scoreStandard coronary CT angiographyAcute myocardial infarctionCoronary inflammationCardiac eventsArtery diseaseCalcium scoreCardiac surgeryMACE prediction
2020
Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease
Oikonomou EK, Siddique M, Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovascular Research 2020, 116: 2040-2054. PMID: 32090243, PMCID: PMC7585409, DOI: 10.1093/cvr/cvaa021.Peer-Reviewed Original ResearchConceptsCardiac computed tomographyNon-invasive imagingCardiovascular imagingCardiovascular diseaseNon-invasive cardiovascular imagingCardiac CTCardiovascular risk stratificationFirst-line optionImportant clinical implicationsRisk stratificationUnstable patientsClinical careComputed tomographyCurrent evidenceClinical implicationsCardiac imagingRadiomics approachPatientsRadiomics methodTissue biologyCardiovascular diagnosticsDiseaseFuture studiesImagingPhenotyping
2017
Detecting human coronary inflammation by imaging perivascular fat
Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L, Margaritis M, Shirodaria C, Kampoli AM, Akoumianakis I, Petrou M, Sayeed R, Krasopoulos G, Psarros C, Ciccone P, Brophy CM, Digby J, Kelion A, Uberoi R, Anthony S, Alexopoulos N, Tousoulis D, Achenbach S, Neubauer S, Channon KM, Antoniades C. Detecting human coronary inflammation by imaging perivascular fat. Science Translational Medicine 2017, 9 PMID: 28701474, DOI: 10.1126/scitranslmed.aal2658.Peer-Reviewed Original ResearchConceptsPerivascular adipose tissueFat attenuation indexVascular inflammationSubclinical coronary artery diseaseHuman adipose tissue explantsHuman vesselsLipid accumulationHuman coronary vasculatureAcute coronary syndromeCoronary artery diseaseVulnerable atherosclerotic plaquesIntracellular lipid accumulationHuman coronary arteriesAdipocyte lipid contentAdipose tissue explantsPositron emission tomographyCT scan informationCoronary inflammationCoronary syndromeArtery diseaseCardiac surgeryValidation cohortCoronary arteryMultiple disease statesTissue inflammation