Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography

  • Su Nam Lee (Department of Imaging and Medicine, Cedars-Sinai Medical Center) ;
  • Andrew Lin (Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University and MonashHeart, Monash Health) ;
  • Damini Dey (Department of Imaging and Medicine, Cedars-Sinai Medical Center) ;
  • Daniel S. Berman (Department of Imaging and Medicine, Cedars-Sinai Medical Center) ;
  • Donghee Han (Department of Imaging and Medicine, Cedars-Sinai Medical Center)
  • Received : 2023.12.06
  • Accepted : 2024.03.23
  • Published : 2024.06.01
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Abstract

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

Keywords

Acknowledgement

The work was supported in part by a grant from the Miriam and Sheldon G. Adelson Medical Research Foundation. Outside of the current work, Dr. Dey is funded by the National Institute of Health/National Heart, Lung, and Blood Institute grants (1R01HL148787-01A1 and 1R01HL151266) and the Winnick Family Foundation as well as a grant from the Miriam and Sheldon G. Adelson Medical Research Foundation.

References

  1. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020;41:407-477 
  2. Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. Circulation 2021;144:e368-e454 
  3. Arbab-Zadeh A, Hoe J. Quantification of coronary arterial stenoses by multidetector CT angiography in comparison with conventional angiography methods, caveats, and implications. JACC Cardiovasc Imaging 2011;4:191-202 
  4. Tonino PA, Fearon WF, De Bruyne B, Oldroyd KG, Leesar MA, Ver Lee PN, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol 2010;55:2816-2821 
  5. Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al. CAD-RADSTM 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;16:536-557 
  6. Boogers MJ, Schuijf JD, Kitslaar PH, van Werkhoven JM, de Graaf FR, Boersma E, et al. Automated quantification of stenosis severity on 64-slice CT: a comparison with quantitative coronary angiography. JACC Cardiovasc Imaging 2010;3:699-709 
  7. Yang DH. Application of artificial intelligence to cardiovascular computed tomography. Korean J Radiol 2021;22:1597-1608 
  8. Hong Y, Commandeur F, Cadet S, Goeller M, Doris MK, Chen X, et al. Deep learning-based stenosis quantification from coronary CT angiography. Proc SPIE Int Soc Opt Eng 2019;10949:109492I 
  9. Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health 2022;4:e256-e265 
  10. Griffin WF, Choi AD, Riess JS, Marques H, Chang HJ, Choi JH, et al. AI evaluation of stenosis on coronary CTA, comparison with quantitative coronary angiography and fractional flow reserve: a CREDENCE trial substudy. JACC Cardiovasc Imaging 2023;16:193-205 
  11. Dundas J, Leipsic JA, Sellers S, Blanke P, Miranda P, Ng N, et al. Artificial intelligence-based coronary stenosis quantification at coronary CT angiography versus quantitative coronary angiography. Radiol Cardiothorac Imaging 2023;5:e230124 
  12. Madsen KT, Norgaard BL, Ovrehus KA, Jensen JM, Parner E, Grove EL, et al. Prognostic value of coronary CT angiography-derived fractional flow reserve on 3-year outcomes in patients with stable angina. Radiology 2023;308:e230524 
  13. Patel MR, Norgaard BL, Fairbairn TA, Nieman K, Akasaka T, Berman DS, et al. 1-year impact on medical practice and clinical outcomes of FFRCT: the ADVANCE registry. JACC Cardiovasc Imaging 2020;13(1 Pt 1):97-105 
  14. Mittal TK, Hothi SS, Venugopal V, Taleyratne J, O'Brien D, Adnan K, et al. The use and efficacy of FFR-CT: real-world multicenter audit of clinical data with cost analysis. JACC Cardiovasc Imaging 2023;16:1056-1065 
  15. Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 2018;28:2655-2664 
  16. Nous FMA, Budde RPJ, Lubbers MM, Yamasaki Y, Kardys I, Bruning TA, et al. Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials. Eur Radiol 2020;30:3692-3701 
  17. Mushtaq S, Conte E, Pontone G, Baggiano A, Annoni A, Formenti A, et al. State-of-the-art-myocardial perfusion stress testing: static CT perfusion. J Cardiovasc Comput Tomogr 2020;14:294-302 
  18. Choi JH, Koo BK, Yoon YE, Min JK, Song YB, Hahn JY, et al. Diagnostic performance of intracoronary gradient-based methods by coronary computed tomography angiography for the evaluation of physiologically significant coronary artery stenoses: a validation study with fractional flow reserve. Eur Heart J Cardiovasc Imaging 2012;13:1001-1007 
  19. Choi JH, Min JK, Labounty TM, Lin FY, Mendoza DD, Shin DH, et al. Intracoronary transluminal attenuation gradient in coronary CT angiography for determining coronary artery stenosis. JACC Cardiovasc Imaging 2011;4:1149-1157 
  20. Yoon YE, Choi JH, Kim JH, Park KW, Doh JH, Kim YJ, et al. Noninvasive diagnosis of ischemia-causing coronary stenosis using CT angiography: diagnostic value of transluminal attenuation gradient and fractional flow reserve computed from coronary CT angiography compared to invasively measured fractional flow reserve. JACC Cardiovasc Imaging 2012;5:1088-1096 
  21. Wong DT, Ko BS, Cameron JD, Nerlekar N, Leung MC, Malaiapan Y, et al. Transluminal attenuation gradient in coronary computed tomography angiography is a novel noninvasive approach to the identification of functionally significant coronary artery stenosis: a comparison with fractional flow reserve. J Am Coll Cardiol 2013;61:1271-1279 
  22. Park EA, Lee W, Park SJ, Kim YK, Hwang HY. Influence of coronary artery diameter on intracoronary transluminal attenuation gradient during CT angiography. JACC Cardiovasc Imaging 2016;9:1074-1083 
  23. Bom MJ, Driessen RS, Stuijfzand WJ, Raijmakers PG, Van Kuijk CC, Lammertsma AA, et al. Diagnostic value of transluminal attenuation gradient for the presence of ischemia as defined by fractional flow reserve and quantitative positron emission tomography. JACC Cardiovasc Imaging 2019;12:323-333 
  24. Dey D, Achenbach S, Schuhbaeck A, Pflederer T, Nakazato R, Slomka PJ, et al. Comparison of quantitative atherosclerotic plaque burden from coronary CT angiography in patients with first acute coronary syndrome and stable coronary artery disease. J Cardiovasc Comput Tomogr 2014;8:368-374 
  25. Diaz-Zamudio M, Fuchs TA, Slomka P, Otaki Y, Arsanjani R, Gransar H, et al. Quantitative plaque features from coronary computed tomography angiography to identify regional ischemia by myocardial perfusion imaging. Eur Heart J Cardiovasc Imaging 2017;18:499-507 
  26. Hell MM, Dey D, Marwan M, Achenbach S, Schmid J, Schuhbaeck A. Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography. Eur J Radiol 2015;84:1502-1508 
  27. Lin A, van Diemen PA, Motwani M, McElhinney P, Otaki Y, Han D, et al. Machine learning from quantitative coronary computed tomography angiography predicts fractional flow reserve-defined ischemia and impaired myocardial blood flow. Circ Cardiovasc Imaging 2022;15:e014369 
  28. Hecht HS. Coronary artery calcium: utilization for primary prevention of CHD. Curr Cardiol Rep 2011;13:465-474 
  29. Kim SY, Suh YJ, Kim NY, Lee S, Nam K, Kim J, et al. A modified length-based grading method for assessing coronary artery calcium severity on non-electrocardiogram-gated chest computed tomography: a multiple-observer study. Korean J Radiol 2023;24:284-293 
  30. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990;15:827-832 
  31. Budoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. J Am Coll Cardiol 2007;49:1860-1870 
  32. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med 2008;358:1336-1345 
  33. Lee JG, Kim H, Kang H, Koo HJ, Kang JW, Kim YH, et al. Fully automatic coronary calcium score software empowered by artificial intelligence technology: validation study using three CT cohorts. Korean J Radiol 2021;22:1764-1776 
  34. Ayoub C, Erthal F, Abdelsalam MA, Murad MH, Wang Z, Erwin PJ, et al. Prognostic value of segment involvement score compared to other measures of coronary atherosclerosis by computed tomography: a systematic review and meta-analysis. J Cardiovasc Comput Tomogr 2017;11:258-267 
  35. Min JK, Shaw LJ, Devereux RB, Okin PM, Weinsaft JW, Russo DJ, et al. Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol 2007;50:1161-1170 
  36. Boogers MJ, Broersen A, van Velzen JE, de Graaf FR, ElNaggar HM, Kitslaar PH, et al. Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification. Eur Heart J 2012;33:1007-1016 
  37. de Graaf MA, Broersen A, Kitslaar PH, Roos CJ, Dijkstra J, Lelieveldt BP, et al. Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. Int J Cardiovasc Imaging 2013;29:1177-1190 
  38. Fujimoto S, Kondo T, Kodama T, Fujisawa Y, Groarke J, Kumamaru KK, et al. A novel method for non-invasive plaque morphology analysis by coronary computed tomography angiography. Int J Cardiovasc Imaging 2014;30:1373-1382 
  39. Voros S, Rinehart S, Qian Z, Vazquez G, Anderson H, Murrieta L, et al. Prospective validation of standardized, 3-dimensional, quantitative coronary computed tomographic plaque measurements using radiofrequency backscatter intravascular ultrasound as reference standard in intermediate coronary arterial lesions: results from the ATLANTA (assessment of tissue characteristics, lesion morphology, and hemodynamics by angiography with fractional flow reserve, intravascular ultrasound and virtual histology, and noninvasive computed tomography in atherosclerotic plaques) I study. JACC Cardiovasc Interv 2011;4:198-208 
  40. Choi AD, Marques H, Kumar V, Griffin WF, Rahban H, Karlsberg RP, et al. CT evaluation by artificial intelligence for atherosclerosis, stenosis and vascular morphology (CLARIFY): a multi-center, international study. J Cardiovasc Comput Tomogr 2021;15:470-476 
  41. Sheahan M, Ma X, Paik D, Obuchowski NA, St Pierre S, Newman WP 3rd, et al. Atherosclerotic plaque tissue: noninvasive quantitative assessment of characteristics with software-aided measurements from conventional CT angiography. Radiology 2018;286:622-631 
  42. Dey D, Schepis T, Marwan M, Slomka PJ, Berman DS, Achenbach S. Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US. Radiology 2010;257:516-522 
  43. Tzimas G, Gulsin GS, Everett RJ, Akodad M, Meier D, Sewnarain K, et al. Age- and sex-specific nomographic CT quantitative plaque data from a large international cohort. JACC Cardiovasc Imaging 2024;17:165-175 
  44. Park HB, Lee BK, Shin S, Heo R, Arsanjani R, Kitslaar PH, et al. Clinical feasibility of 3D automated coronary atherosclerotic plaque quantification algorithm on coronary computed tomography angiography: comparison with intravascular ultrasound. Eur Radiol 2015;25:3073-3083 
  45. Matsumoto H, Watanabe S, Kyo E, Tsuji T, Ando Y, Otaki Y, et al. Standardized volumetric plaque quantification and characterization from coronary CT angiography: a head-to-head comparison with invasive intravascular ultrasound. Eur Radiol 2019;29:6129-6139 
  46. Puchner SB, Ferencik M, Maehara A, Stolzmann P, Ma S, Do S, et al. Iterative image reconstruction improves the accuracy of automated plaque burden assessment in coronary CT angiography: a comparison with intravascular ultrasound. AJR Am J Roentgenol 2017;208:777-784 
  47. Stolzmann P, Schlett CL, Maurovich-Horvat P, Maehara A, Ma S, Scheffel H, et al. Variability and accuracy of coronary CT angiography including use of iterative reconstruction algorithms for plaque burden assessment as compared with intravascular ultrasound-an ex vivo study. Eur Radiol 2012;22:2067-2075 
  48. van Rosendael AR, van den Hoogen IJ, Gianni U, Ma X, Tantawy SW, Bax AM, et al. Association of statin treatment with progression of coronary atherosclerotic plaque composition. JAMA Cardiol 2021;6:1257-1266 
  49. Lee SE, Chang HJ, Sung JM, Park HB, Heo R, Rizvi A, et al. Effects of statins on coronary atherosclerotic plaques: the PARADIGM study. JACC Cardiovasc Imaging 2018;11:1475-1484 
  50. Madder RD, Chinnaiyan KM, Marandici AM, Goldstein JA. Features of disrupted plaques by coronary computed tomographic angiography: correlates with invasively proven complex lesions. Circ Cardiovasc Imaging 2011;4:105-113 
  51. Cardoso R, Choi AD, Shiyovich A, Besser SA, Min JK, Earls J, et al. How early can atherosclerosis be detected by coronary CT angiography? Insights from quantitative CT analysis of serial scans in the PARADIGM trial. J Cardiovasc Comput Tomogr 2023;17:407-412 
  52. Buckler AJ, Doros G, Kinninger A, Lakshmanan S, Le VT, Libby P, et al. Quantitative imaging biomarkers of coronary plaque morphology: insights from EVAPORATE. Front Cardiovasc Med 2023;10:1204071 
  53. Kwan AC, McElhinney PA, Tamarappoo BK, Cadet S, Hurtado C, Miller RJH, et al. Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score. Eur Radiol 2021;31:1227-1235 
  54. Chang HJ, Lin FY, Lee SE, Andreini D, Bax J, Cademartiri F, et al. Coronary atherosclerotic precursors of acute coronary syndromes. J Am Coll Cardiol 2018;71:2511-2522 
  55. Williams MC, Kwiecinski J, Doris M, McElhinney P, D'Souza MS, Cadet S, et al. Low-attenuation noncalcified plaque on coronary computed tomography angiography predicts myocardial infarction: results from the multicenter SCOT-HEART trial (Scottish computed tomography of the heart). Circulation 2020;141:1452-1462 
  56. Lee SE, Sung JM, Rizvi A, Lin FY, Kumar A, Hadamitzky M, et al. Quantification of coronary atherosclerosis in the assessment of coronary artery disease. Circ Cardiovasc Imaging 2018;11:e007562 
  57. Meah MN, Tzolos E, Wang KL, Bularga A, Dweck MR, Curzen N, et al. Plaque burden and 1-year outcomes in acute chest pain: results from the multicenter RAPID-CTCA trial. JACC Cardiovasc Imaging 2022;15:1916-1925 
  58. Criqui MH, Denenberg JO, Ix JH, McClelland RL, Wassel CL, Rifkin DE, et al. Calcium density of coronary artery plaque and risk of incident cardiovascular events. JAMA 2014;311:271-278 
  59. van Rosendael AR, Narula J, Lin FY, van den Hoogen IJ, Gianni U, Al Hussein Alawamlh O, et al. Association of high-density calcified 1K plaque with risk of acute coronary syndrome. JAMA Cardiol 2020;5:282-290 
  60. Bax AM, Yoon YE, Gianni U, Ma X, Lu Y, Lee BC, et al. Plaque character and progression according to the location of coronary atherosclerotic plaque. Am J Cardiol 2021;158:15-22 
  61. Han D, Lin A, Kuronuma K, Tzolos E, Kwan AC, Klein E, et al. Association of plaque location and vessel geometry determined by coronary computed tomographic angiography with future acute coronary syndrome-causing culprit lesions. JAMA Cardiol 2022;7:309-319 
  62. Kolossvary M, Karady J, Kikuchi Y, Ivanov A, Schlett CL, Lu MT, et al. Radiomics versus visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: an ex vivo study. Radiology 2019;293:89-96 
  63. Lin A, Kolossvary M, Cadet S, McElhinney P, Goeller M, Han D, et al. Radiomics-based precision phenotyping identifies unstable coronary plaques from computed tomography angiography. JACC Cardiovasc Imaging 2022;15:859-871 
  64. Chen Q, Xie G, Tang CX, Yang L, Xu P, Gao X, et al. Development and validation of CCTA-based radiomics signature for predicting coronary plaques with rapid progression. Circ Cardiovasc Imaging 2023;16:e015340 
  65. Chen Q, Pan T, Wang YN, Schoepf UJ, Bidwell SL, Qiao H, et al. A coronary CT angiography radiomics model to identify vulnerable plaque and predict cardiovascular events. Radiology 2023;307:e221693 
  66. Shin S, Park HB, Chang HJ, Arsanjani R, Min JK, Kim YJ, et al. Impact of intensive LDL cholesterol lowering on coronary artery atherosclerosis progression: a serial CT angiography study. JACC Cardiovasc Imaging 2017;10:437-446 
  67. Svanteson M, Rollefstad S, Klow NE, Hisdal J, Ikdahl E, Sexton J, et al. Effects of long-term statin-treatment on coronary atherosclerosis in patients with inflammatory joint diseases. PLoS One 2019;14:e0226479 
  68. Otaki Y, Tamarappoo B, Cadet SJ, Doris M, Arnson Y, Huynh PT, et al. Decrease in LDL-C is associated with decrease in all components of noncalcified plaque on coronary CTA. Atherosclerosis 2019;285:128-134 
  69. Shi R, Gao Y, Shen LL, Shi K, Wang J, Jiang L, et al. The effect of LDL-C status on the association between increased coronary artery calcium score and compositional plaque volume progression in statins-treated diabetic patients: evaluated using serial coronary CTAs. Cardiovasc Diabetol 2022;21:121 
  70. Hirai K, Kawasaki T, Soejima T, Kajiyama K, Fukami Y, Asada S, et al. Impact of intensive low-density lipoprotein cholesterol-lowering therapy on coronary artery plaques in acute coronary syndrome. Am J Cardiol 2023;204:84-91 
  71. Sun T, Wang Y, Wang X, Hu W, Li A, Li S, et al. Effect of long-term intensive cholesterol control on the plaque progression in elderly based on CTA cohort study. Eur Radiol 2022;32:4374-4383 
  72. Kim U, Leipsic JA, Sellers SL, Shao M, Blanke P, Hadamitzky M, et al. Natural history of diabetic coronary atherosclerosis by quantitative measurement of serial coronary computed tomographic angiography: results of the PARADIGM study. JACC Cardiovasc Imaging 2018;11:1461-1471 
  73. Nakanishi R, Ceponiene I, Osawa K, Luo Y, Kanisawa M, Megowan N, et al. Plaque progression assessed by a novel semi-automated quantitative plaque software on coronary computed tomography angiography between diabetes and non-diabetes patients: a propensity-score matching study. Atherosclerosis 2016;255:73-79 
  74. Won KB, Lee SE, Lee BK, Park HB, Heo R, Rizvi A, et al. Longitudinal assessment of coronary plaque volume change related to glycemic status using serial coronary computed tomography angiography: a PARADIGM (progression of atherosclerotic plaque determined by computed tomographic angiography imaging) substudy. J Cardiovasc Comput Tomogr 2019;13:142-147 
  75. El Mahdiui M, Smit JM, van Rosendael AR, Neglia D, Knuuti J, Saraste A, et al. Sex differences in coronary plaque changes assessed by serial computed tomography angiography. Int J Cardiovasc Imaging 2021;37:2311-2321 
  76. Lee SE, Sung JM, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, et al. Sex differences in compositional plaque volume progression in patients with coronary artery disease. JACC Cardiovasc Imaging 2020;13:2386-2396 
  77. Han D, Berman DS, Miller RJH, Andreini D, Budoff MJ, Cademartiri F, et al. Association of cardiovascular disease risk factor burden with progression of coronary atherosclerosis assessed by serial coronary computed tomographic angiography. JAMA Netw Open 2020;3:e2011444 
  78. Weber C, Deseive S, Brim G, Stocker TJ, Broersen A, Kitslaar P, et al. Coronary plaque volume and predictors for fast plaque progression assessed by serial coronary CT angiography-A single-center observational study. Eur J Radiol 2020;123:108805 
  79. Won KB, Lee BK, Heo R, Park HB, Lin FY, Hadamitzky M, et al. Longitudinal quantitative assessment of coronary atherosclerotic plaque burden related to serum hemoglobin levels. JACC Asia 2022;2:311-319 
  80. Won KB, Lee BK, Park HB, Heo R, Lee SE, Rizvi A, et al. Quantitative assessment of coronary plaque volume change related to triglyceride glucose index: the progression of atherosclerotic plaque determined by computed tomographic angiography imaging (PARADIGM) registry. Cardiovasc Diabetol 2020;19:113 
  81. Won KB, Park HB, Heo R, Lee BK, Lin FY, Hadamitzky M, et al. Longitudinal quantitative assessment of coronary atherosclerosis related to normal systolic blood pressure maintenance in the absence of established cardiovascular disease. Clin Cardiol 2022;45:873-881 
  82. Li S, Tang X, Luo Y, Wu B, Huang Z, Li Z, et al. Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study. Cardiovasc Diabetol 2020;19:146 
  83. Wang X, Shan DK, Dou GH, Ding YP, Jing J, Che HB, et al. Lipoprotein(a) is associated with coronary atheroma progression: analysis from a serial coronary computed tomography angiography study. J Geriatr Cardiol 2021;18:996-1007 
  84. Ben Zekry S, Sreedharan S, Han D, Sellers S, Ahmadi AA, Blanke P, et al. Comparison of coronary atherosclerotic plaque progression in East Asians and Caucasians by serial coronary computed tomographic angiography: a PARADIGM substudy. J Cardiovasc Comput Tomogr 2022;16:222-229 
  85. Kaiser Y, Daghem M, Tzolos E, Meah MN, Doris MK, Moss AJ, et al. Association of lipoprotein(a) with atherosclerotic plaque progression. J Am Coll Cardiol 2022;79:223-233 
  86. Hoffmann H, Frieler K, Schlattmann P, Hamm B, Dewey M. Influence of statin treatment on coronary atherosclerosis visualised using multidetector computed tomography. Eur Radiol 2010;20:2824-2833 
  87. Inoue K, Motoyama S, Sarai M, Sato T, Harigaya H, Hara T, et al. Serial coronary CT angiography-verified changes in plaque characteristics as an end point: evaluation of effect of statin intervention. JACC Cardiovasc Imaging 2010;3:691-698 
  88. Zeb I, Li D, Nasir K, Malpeso J, Batool A, Flores F, et al. Effect of statin treatment on coronary plaque progression - a serial coronary CT angiography study. Atherosclerosis 2013;231:198-204 
  89. Lo J, Lu MT, Ihenachor EJ, Wei J, Looby SE, Fitch KV, et al. Effects of statin therapy on coronary artery plaque volume and high-risk plaque morphology in HIV-infected patients with subclinical atherosclerosis: a randomised, double-blind, placebo-controlled trial. Lancet HIV 2015;2:e52-e63 
  90. Auscher S, Heinsen L, Nieman K, Vinther KH, Logstrup B, Moller JE, et al. Effects of intensive lipid-lowering therapy on coronary plaques composition in patients with acute myocardial infarction: assessment with serial coronary CT angiography. Atherosclerosis 2015;241:579-587 
  91. Li Z, Hou Z, Yin W, Liu K, Gao Y, Xu H, et al. Effects of statin therapy on progression of mild noncalcified coronary plaque assessed by serial coronary computed tomography angiography: a multicenter prospective study. Am Heart J 2016;180:29-38 
  92. Nou E, Lu MT, Looby SE, Fitch KV, Kim EA, Lee H, et al. Serum oxidized low-density lipoprotein decreases in response to statin therapy and relates independently to reductions in coronary plaque in patients with HIV. AIDS 2016;30:583-590 
  93. Smit JM, van Rosendael AR, El Mahdiui M, Neglia D, Knuuti J, Saraste A, et al. Impact of clinical characteristics and statins on coronary plaque progression by serial computed tomography angiography. Circ Cardiovasc Imaging 2020;13:e009750 
  94. Foldyna B, Lo J, Mayrhofer T, Grinspoon SK, Hoffmann U, Lu MT. Individual coronary plaque changes on serial CT angiography: within-patient heterogeneity, natural history, and statin effects in HIV. J Cardiovasc Comput Tomogr 2020;14:144-148 
  95. Motoyama S, Nagahara Y, Sarai M, Kawai H, Miyajima K, Sato Y, et al. Effect of omega-3 fatty acids on coronary plaque morphology-a serial computed tomography angiography study. Circ J 2022;86:831-842 
  96. Alfaddagh A, Elajami TK, Ashfaque H, Saleh M, Bistrian BR, Welty FK. Effect of eicosapentaenoic and docosahexaenoic acids added to statin therapy on coronary artery plaque in patients with coronary artery disease: a randomized clinical trial. J Am Heart Assoc 2017;6:e006981 
  97. Baumann S, Kettel L, Stach K, Ozdemir GH, Renker M, Tesche C, et al. Serial changes in coronary plaque formation using CT angiography in patients undergoing PCSK9-inhibitor therapy with 1-year follow-up. J Thorac Imaging 2022;37:285-291 
  98. Budoff MJ, Bhatt DL, Kinninger A, Lakshmanan S, Muhlestein JB, Le VT, et al. Effect of icosapent ethyl on progression of coronary atherosclerosis in patients with elevated triglycerides on statin therapy: final results of the EVAPORATE trial. Eur Heart J 2020;41:3925-3932 
  99. Perez de Isla L, Diaz-Diaz JL, Romero MJ, Muniz-Grijalvo O, Mediavilla JD, Argueso R, et al. Alirocumab and coronary atherosclerosis in asymptomatic patients with familial hypercholesterolemia: the ARCHITECT study. Circulation 2023;147:1436-1443 
  100. Elnabawi YA, Dey AK, Goyal A, Groenendyk JW, Chung JH, Belur AD, et al. Coronary artery plaque characteristics and treatment with biologic therapy in severe psoriasis: results from a prospective observational study. Cardiovasc Res 2019;115:721-728 
  101. Choi H, Uceda DE, Dey AK, Abdelrahman KM, Aksentijevich M, Rodante JA, et al. Treatment of psoriasis with biologic therapy is associated with improvement of coronary artery plaque lipid-rich necrotic core: results from a prospective, observational study. Circ Cardiovasc Imaging 2020;13:e011199 
  102. Budoff MJ, Ellenberg SS, Lewis CE, Mohler ER 3rd, Wenger NK, Bhasin S, et al. Testosterone treatment and coronary artery plaque volume in older men with low testosterone. JAMA 2017;317:708-716 
  103. Lee DH, Chun EJ, Hur JH, Min SH, Lee JE, Oh TJ, et al. Effect of sarpogrelate, a selective 5-HT2A receptor antagonist, on characteristics of coronary artery disease in patients with type 2 diabetes. Atherosclerosis 2017;257:47-54 
  104. Matsumoto S, Ibrahim R, Gregoire JC, L'Allier PL, Pressacco J, Tardif JC, et al. Effect of treatment with 5-lipoxygenase inhibitor VIA-2291 (atreleuton) on coronary plaque progression: a serial CT angiography study. Clin Cardiol 2017;40:210-215 
  105. Vaidya K, Arnott C, Martinez GJ, Ng B, McCormack S, Sullivan DR, et al. Colchicine therapy and plaque stabilization in patients with acute coronary syndrome: a CT coronary angiography study. JACC Cardiovasc Imaging 2018;11(2 Pt 2):305-316 
  106. Shaikh K, Kinninger A, Cherukuri L, Birudaraju D, Nakanishi R, Almeida S, et al. Aged garlic extract reduces low attenuation plaque in coronary arteries of patients with diabetes: a randomized, double-blind, placebo-controlled study. Exp Ther Med 2020;19:1457-1461 
  107. Aldana-Bitar J, Moore J, Manubolu VS, Dahal S, Verghese D, Lakshmanan S, et al. Plaque progression differences between apixaban and rivaroxaban in patients with atrial fibrillation measured with cardiac computed tomography and plaque quantification. Am J Ther 2023;30:e313-e320 
  108. Heinsen LJ, Pararajasingam G, Andersen TR, Auscher S, Sheta HM, Precht H, et al. Liraglutide treatment is associated with progression of coronary artery fibrous plaque: a prospective 1-year follow-up study in asymptomatic patients with type 2 diabetes. BMC Cardiovasc Disord 2023;23:214 
  109. Takx RA, Willemink MJ, Nathoe HM, Schilham AM, Budde RP, de Jong PA, et al. The effect of iterative reconstruction on quantitative computed tomography assessment of coronary plaque composition. Int J Cardiovasc Imaging 2014;30:155-163 
  110. Williams MC, Earls JP, Hecht H. Quantitative assessment of atherosclerotic plaque, recent progress and current limitations. J Cardiovasc Comput Tomogr 2022;16:124-137 
  111. Achenbach S, Boehmer K, Pflederer T, Ropers D, Seltmann M, Lell M, et al. Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque. J Cardiovasc Comput Tomogr 2010;4:110-115 
  112. Min JK, Chang HJ, Andreini D, Pontone G, Guglielmo M, Bax JJ, et al. Coronary CTA plaque volume severity stages according to invasive coronary angiography and FFR. J Cardiovasc Comput Tomogr 2022;16:415-422