Guedeney P, Claessen BE, Kalkman DN, Aquino M, Sorrentino S, Giustino G, et al. Residual inflammatory risk in patients with low LDL cholesterol levels undergoing percutaneous coronary intervention. J Am Coll Cardiol. 2019;73(19):2401–9.
PubMed
Google Scholar
Bhatt DL, Lopes RD, Harrington RA. Diagnosis and treatment of acute coronary syndromes: a review. JAMA. 2022;327(7):662–75.
PubMed
Google Scholar
Madhavan MV, Kirtane AJ, Redfors B, Genereux P, Ben-Yehuda O, Palmerini T, et al. Stent-related adverse events >1 year after percutaneous coronary intervention. J Am Coll Cardiol. 2020;75(6):590–604.
CAS
PubMed
Google Scholar
Gao G, Zhang D, Song C, Xu H, Yin D, Guan C, et al. Integrating the residual SYNTAX score to improve the predictive ability of the age, creatinine, and ejection fraction (ACEF) score for cardiac mortality in percutaneous coronary intervention patients. Catheter Cardiovasc Interv. 2020;95(Suppl 1):534–41.
PubMed
Google Scholar
He YM, Shen L, Ge JB. Fallacies and possible remedies of the SYNTAX score. J Interv Cardiol. 2020;2020:8822308.
PubMed
PubMed Central
Google Scholar
Bohula EA, Giugliano RP, Cannon CP, Zhou J, Murphy SA, White JA, et al. Achievement of dual low-density lipoprotein cholesterol and high-sensitivity C-reactive protein targets more frequent with the addition of ezetimibe to simvastatin and associated with better outcomes in IMPROVE-IT. Circulation. 2015;132(13):1224–33.
CAS
PubMed
Google Scholar
Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111–88.
PubMed
Google Scholar
Bhatt DL, Steg PG, Miller M, Brinton EA, Jacobson TA, Ketchum SB, et al. Cardiovascular risk reduction with icosapent ethyl for hypertriglyceridemia. N Engl J Med. 2019;380(1):11–22.
CAS
PubMed
Google Scholar
Antonopoulos AS, Sanna F, Sabharwal N, Thomas S, Oikonomou EK, Herdman L, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017. https://doi.org/10.1126/scitranslmed.aal2658.
Article
PubMed
Google Scholar
Margaritis M, Antonopoulos AS, Digby J, Lee R, Reilly S, Coutinho P, et al. Interactions between vascular wall and perivascular adipose tissue reveal novel roles for adiponectin in the regulation of endothelial nitric oxide synthase function in human vessels. Circulation. 2013;127(22):2209–21.
CAS
PubMed
Google Scholar
Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Hutt Centeno E, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392(10151):929–39.
PubMed
PubMed Central
Google Scholar
van Diemen PA, Bom MJ, Driessen RS, Schumacher SP, Everaars H, de Winter RW, et al. Prognostic value of RCA pericoronary adipose tissue CT-attenuation beyond high-risk plaques, plaque volume, and ischemia. JACC Cardiovasc Imaging. 2021;14(8):1598–610.
PubMed
Google Scholar
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J. 2019;40(43):3529–43.
PubMed
PubMed Central
Google Scholar
Lin A, Kolossvary M, Yuvaraj J, Cadet S, McElhinney PA, Jiang C, et al. Myocardial infarction associates with a distinct pericoronary adipose tissue radiomic phenotype: a prospective case-control study. JACC Cardiovasc Imaging. 2020;13(11):2371–83.
CAS
PubMed
PubMed Central
Google Scholar
Shang J, Ma S, Guo Y, Yang L, Zhang Q, Xie F, et al. Prediction of acute coronary syndrome within 3 years using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography. Eur Radiol. 2022;32(2):1256–66.
PubMed
Google Scholar
Cui K, Liang S, Hua M, Gao Y, Feng Z, Wang W, et al. Diagnostic performance of machine learning-derived radiomics signature of pericoronary adipose tissue in coronary computed tomography angiography for coronary artery in-stent restenosis. Acad Radiol. 2023. https://doi.org/10.1016/j.acra.2023.04.006.
Article
PubMed
Google Scholar
D’Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet. 2021;397(10270):199–207.
PubMed
Google Scholar
Ranucci M, Castelvecchio S, Menicanti L, Frigiola A, Pelissero G. Risk of assessing mortality risk in elective cardiac operations: age, creatinine, ejection fraction, and the law of parsimony. Circulation. 2009;119(24):3053–61.
PubMed
Google Scholar
Farooq V, Serruys PW, Bourantas CV, Zhang Y, Muramatsu T, Feldman T, et al. Quantification of incomplete revascularization and its association with five-year mortality in the synergy between percutaneous coronary intervention with taxus and cardiac surgery (SYNTAX) trial validation of the residual SYNTAX score. Circulation. 2013;128(2):141–51.
CAS
PubMed
Google Scholar
Sianos G, Morel MA, Kappetein AP, Morice MC, Colombo A, Dawkins K, et al. The SYNTAX Score: an angiographic tool grading the complexity of coronary artery disease. EuroIntervention. 2005;1(2):219–27.
PubMed
Google Scholar
Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al. CAD-RADS 2.0—2022 coronary artery disease-reporting and data system: an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr. 2022;16(6):536–57.
PubMed
Google Scholar
Ferencik M, Mayrhofer T, Lu MT, Bittner DO, Emami H, Puchner SB, et al. Coronary atherosclerosis, cardiac troponin, and interleukin-6 in patients with chest pain: the PROMISE trial results. JACC Cardiovasc Imaging. 2022;15(8):1427–38.
PubMed
PubMed Central
Google Scholar
Kolossvary M, Szilveszter B, Merkely B, Maurovich-Horvat P. Plaque imaging with CT-a comprehensive review on coronary CT angiography based risk assessment. Cardiovasc Diagn Ther. 2017;7(5):489–506.
PubMed
PubMed Central
Google Scholar
Lee JM, Choi G, Koo BK, Hwang D, Park J, Zhang J, et al. Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging. 2019;12(6):1032–43.
PubMed
Google Scholar
Rao SV, O’Donoghue ML, Ruel M, Rab T, Tamis-Holland JE, Alexander JH, et al. 2025 ACC/AHA/ACEP/NAEMSP/SCAI guideline for the management of patients with acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2025;85(22):2135–237.
PubMed
Google Scholar
Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology. 2019;291(1):53–9.
PubMed
Google Scholar
Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F, et al. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol. 2021;31(4):2272–80.
PubMed
Google Scholar
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.
PubMed
PubMed Central
Google Scholar
Mangiacapra F, Del Buono MG, Abbate A, Gori T, Barbato E, Montone RA, et al. Role of endothelial dysfunction in determining angina after percutaneous coronary intervention: learning from pathophysiology to optimize treatment. Prog Cardiovasc Dis. 2020;63(3):233–42.
PubMed
Google Scholar
Goeller M, Achenbach S, Duncker H, Dey D, Marwan M. Imaging of the pericoronary adipose tissue (PCAT) using cardiac computed tomography: modern clinical implications. J Thorac Imaging. 2021;36(3):149–61.
PubMed
Google Scholar
Ridker PM, Everett BM, Thuren T, MacFadyen JG, Chang WH, Ballantyne C, et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377(12):1119–31.
CAS
PubMed
Google Scholar
Nidorf SM, Fiolet ATL, Mosterd A, Eikelboom JW, Schut A, Opstal TSJ, et al. Colchicine in patients with chronic coronary disease. N Engl J Med. 2020;383(19):1838–47.
CAS
PubMed
Google Scholar
Ridker PM. How common is residual inflammatory risk? Circ Res. 2017;120(4):617–9.
CAS
PubMed
Google Scholar
Lawler PR, Bhatt DL, Godoy LC, Luscher TF, Bonow RO, Verma S, et al. Targeting cardiovascular inflammation: next steps in clinical translation. Eur Heart J. 2021;42(1):113–31.
CAS
PubMed
Google Scholar
Kinoshita D, Suzuki K, Yuki H, Niida T, Fujimoto D, Minami Y, et al. Coronary artery disease reporting and data system (CAD-RADS), vascular inflammation and plaque vulnerability. J Cardiovasc Comput Tomogr. 2023. https://doi.org/10.1016/j.jcct.2023.09.008.
Article
PubMed
Google Scholar
Lee JW, Kim JY, Han K, Im DJ, Lee KH, Kim TH, et al. Coronary CT angiography CAD-RADS versus coronary artery calcium score in patients with acute chest pain. Radiology. 2021;301(1):81–90.
PubMed
Google Scholar
Qin B, Li Z, Zhou H, Liu Y, Wu H, Wang Z. The predictive value of the perivascular adipose tissue CT fat attenuation index for coronary in-stent restenosis. Front Cardiovasc Med. 2022;9: 822308.
CAS
PubMed
PubMed Central
Google Scholar
Nogic J, Kim J, Layland J, Cheng K, Dey D, Wong DT, et al. Peri-coronary adipose tissue is a predictor of stent failure in patients undergoing percutaneous coronary intervention. Cardiovasc Revasc Med. 2023;53:61–6.
PubMed
Google Scholar
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.
PubMed
Google Scholar
Jolly SS, d’Entremont MA, Lee SF. Colchicine and spironolactone in acute myocardial infarction. Reply. N Engl J Med. 2025;392(20):2074–5.
PubMed
Google Scholar