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The International Journal of Cardiovascular Imaging

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04.07.2020 | Review Paper

Ischemia and outcome prediction by cardiac CT based machine learning

verfasst von: Verena Brandt, Tilman Emrich, U. Joseph Schoepf, Danielle M. Dargis, Richard R. Bayer, Carlo N. De Cecco, Christian Tesche

Erschienen in: The International Journal of Cardiovascular Imaging | Ausgabe 12/2020

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Abstract

Cardiac CT using non-enhanced coronary artery calcium scoring (CACS) and coronary CT angiography (cCTA) has been proven to provide excellent evaluation of coronary artery disease (CAD) combining anatomical and morphological assessment of CAD for cardiovascular risk stratification and therapeutic decision-making, in addition to providing prognostic value for the occurrence of adverse cardiac outcome. In recent years, artificial intelligence (AI) and, in particular, the application of machine learning (ML) algorithms, have been promoted in cardiovascular CT imaging for improved decision pathways, risk stratification, and outcome prediction in a more objective, reproducible, and rational manner. AI is based on computer science and mathematics that are based on big data, high performance computational infrastructure, and applied algorithms. The application of ML in daily routine clinical practice may hold potential to improve imaging workflow and to promote better outcome prediction and more effective decision-making in patient management. Moreover, CT represents a field wherein ML may be particularly useful, such as CACS and cCTA. Thus, the purpose of this review is to give a short overview about the contemporary state of ML based algorithms in cardiac CT, as well as to provide clinicians with currently available scientific data on clinical validation and implementation of these algorithms for the prediction of ischemia-specific CAD and cardiovascular outcome.

Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C et al (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477PubMed

Chow BJ, Small G, Yam Y, Chen L, Achenbach S, Al-Mallah M et al (2011) Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRM: COroNary computed tomography angiography evaluation for clinical outcomes: an InteRnational Multicenter registry. Circ Cardiovasc Imaging 4:463–472PubMed

Cho I, Al’Aref SJ, Berger A, Hartaigh OB, Gransar H, Valenti V et al (2018) Prognostic value of coronary computed tomographic angiography findings in asymptomatic individuals: a 6-year follow-up from the prospective multicentre international CONFIRM study. Eur Heart J. 39:934–941PubMedPubMedCentral

Investigators S-H, Newby DE, Adamson PD, Berry C, Boon NA, Dweck MR et al (2018) Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med 379:924–933

Patel MR, Norgaard BL, Fairbairn TA, Nieman K, Akasaka T, Berman DS et al (2019) 1-Year impact on medical practice and clinical outcomes of FFRCT: the ADVANCE Registry. JACC Cardiovasc Imaging. 13:97–105PubMed

Tesche C, De Cecco CN, Albrecht MH, Duguay TM, Bayer RR 2nd, Litwin SE et al (2017) Coronary CT angiography-derived fractional flow reserve. Radiology 285:17–33PubMed

Pontone G, Baggiano A, Andreini D, Guaricci AI, Guglielmo M, Muscogiuri G et al (2019) Dynamic stress computed tomography perfusion with a whole-heart coverage scanner in addition to coronary computed tomography angiography and fractional flow reserve computed tomography derived. JACC Cardiovasc Imaging. 12:2460–2471PubMed

van Assen M, De Cecco CN, Eid M, von Knebel Doeberitz P, Scarabello M, Lavra F et al (2019) Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease. J Cardiovasc Comput Tomogr. 13:26–33PubMed

Kolossvary M, De Cecco CN, Feuchtner G, Maurovich-Horvat P (2019) Advanced atherosclerosis imaging by CT: radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr. 13:274–280PubMed

10.

van Assen M, Banerjee I, De Cecco CN (2020) Beyond the artificial intelligence hype: what lies behind the algorithms and what we can achieve. J Thorac Imaging 35:S3–S10PubMed

11.

Monti CB, Codari M, van Assen M, De Cecco CN, Vliegenthart R (2020) Machine learning and deep neural networks applications in computed tomography for coronary artery disease and myocardial perfusion. J Thorac Imaging 35:S58–S65PubMed

12.

Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP et al (2019) Artificial iintelligence in cardiovascular Imaging: JACC state-of-the-art review. J Am Coll Cardiol 73:1317–1335PubMedPubMedCentral

13.

Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A et al (2019) Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J 40:1975–1986PubMed

14.

Breiman L (2001) Random forests. Mach Learn. 45:5–32

15.

Hubel DH (1959) Single unit activity in striate cortex of unrestrained cats. J Physiol 147:226–238PubMedPubMedCentral

16.

Singh G, Al’Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK et al (2018) Machine learning in cardiac CT: basic concepts and contemporary data. J Cardiovasc Comput Tomogr. 12:192–201PubMed

17.

McClelland RL, Chung H, Detrano R, Post W, Kronmal RA (2006) Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 113:30–37PubMed

18.

Rozanski A, Gransar H, Shaw LJ, Kim J, Miranda-Peats L, Wong ND et al (2011) Impact of coronary artery calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) prospective randomized trial. J Am Coll Cardiol 57:1622–1632PubMedPubMedCentral

19.

Fischer AM, Eid M, De Cecco CN, Gulsun MA, van Assen M, Nance JW et al (2020) Accuracy of an artificial intelligence deep learning algorithm implementing a recurrent neural network with long short-term memory for the automated detection of calcified plaques from coronary computed tomography angiography. J Thorac Imaging 35:S49–S57PubMed

20.

Wolterink JM, Leiner T, Takx RAP, Viergever MA, Išgum I (2015) Automatic coronary calcium scoring in non-contrast-enhanced ECG-triggered cardiac CT with ambiguity detection. IEEE Trans Med Imaging 34:1867–1878PubMed

21.

Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Isgum I (2016) Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal 34:123–136PubMed

22.

Martin SS, van Assen M, Rapaka S, Hudson HT Jr, Fischer AM, Varga-Szemes A et al (2020) Evaluation of a deep learning-based automated CT coronary artery calcium scoring algorithm. JACC Cardiovasc Imaging. 13:524–526PubMed

23.

van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard D, Leiner T et al (2020) Deep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT Protocols. Radiology 295:66–79PubMed

24.

Yang G, Chen Y, Ning X, Sun Q, Shu H, Coatrieux JL (2016) Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys 43:2174PubMed

25.

Shahzad R, van Walsum T, Schaap M, Rossi A, Klein S, Weustink AC et al (2013) Vessel specific coronary artery calcium scoring: an automatic system. Acad Radiol. 20:1–9PubMed

26.

Hecht HS, Cronin P, Blaha MJ, Budoff MJ, Kazerooni EA, Narula J et al (2017) 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of Cardiovascular Computed Tomography and Society of Thoracic Radiology. J Thorac Imaging 32:W54–W66PubMed

27.

Cano-Espinosa C, Gonzalez G, Washko GR, Cazorla M, Estepar RSJ (2018) Automated Agatston score computation in non-ECG gated CT scans using deep learning. Proc SPIE Int Soc Opt Eng 10574:105742KPubMedPubMedCentral

28.

Chang HJ, Lin FY, Gebow D, An HY, Andreini D, Bathina R et al (2019) Selective referral using CCTA versus direct referral for individuals referred to invasive coronary angiography for suspected CAD: a randomized, controlled, open-label Trial. JACC Cardiovasc Imaging. 12:1303–1312PubMed

29.

Maroules CD, Rajiah P, Bhasin M, Abbara S (2019) Current evidence in cardiothoracic imaging: growing evidence for coronary computed tomography angiography as a first-line test in stable chest pain. J Thorac Imaging 34:4–11PubMed

30.

Hoffmann H, Frieler K, Hamm B, Dewey M (2008) Intra- and interobserver variability in detection and assessment of calcified and noncalcified coronary artery plaques using 64-slice computed tomography: variability in coronary plaque measurement using MSCT. Int J Cardiovasc Imaging 24:735–742PubMed

31.

Hell MM, Achenbach S, Shah PK, Berman DS, Dey D (2015) Noncalcified plaque in cardiac CT: quantification and clinical implications. Curr Cardiovasc Imaging Rep. 8:27

32.

Kolossvary M, Kellermayer M, Merkely B, Maurovich-Horvat P (2018) Cardiac computed tomography radiomics: a comprehensive review on radiomic techniques. J Thorac Imaging 33:26–34PubMed

33.

Tejero-de-Pablos A, Huang K, Yamane H, Kurose Y, Mukuta Y, Iho J et al (2019) Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S et al (eds) Medical image computing and computer assisted intervention—MICCAI 2019. Springer, Cham, pp 732–740

34.

Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H et al (2015) Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging 2:014003

35.

Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28:2655–2664PubMedPubMedCentral

36.

Zreik M, van Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, Isgum I (2019) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38:1588–1598PubMed

37.

Denzinger F et al (2020) Deep learning algorithms for coronary artery plaque characterisation from CCTA scans. In: Tolxdorff T, Deserno T, Handels H, Maier A, Maier-Hein K, Palm C (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell, Springer, Wiesbaden

38.

Jawaid MM, Riaz A, Rajani R, Reyes-Aldasoro CC, Slabaugh G (2017) Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles. Comput Biol Med 89:84–95PubMed

39.

Wei J, Zhou C, Chan HP, Chughtai A, Agarwal P, Kuriakose J et al (2014) Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis. Med Phys 41:081901PubMedPubMedCentral

40.

Kolossvary M, Karady J, Szilveszter B, Kitslaar P, Hoffmann U, Merkely B et al (2017) Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circ Cardiovasc Imaging 10:e006843PubMedPubMedCentral

41.

Benton SM Jr, Tesche C, De Cecco CN, Duguay TM, Schoepf UJ, Bayer RR 2nd (2018) Noninvasive derivation of fractional flow reserve from coronary computed tomographic angiography: a review. J Thorac Imaging 33:88–96PubMed

42.

Schwartz FR, Koweek LM, Norgaard BL (2019) Current evidence in cardiothoracic imaging: computed tomography-derived fractional flow reserve in stable chest pain. J Thorac Imaging 34:12–17PubMed

43.

Tang CX, Liu CY, Lu MJ, Schoepf UJ, Tesche C, Bayer RR 2nd et al (2019) CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging. 3(4):980–990

44.

Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M et al (1985) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol 2016(121):42–52

45.

Tesche C, Gray HN (2020) Machine learning and deep neural networks applications in coronary flow assessment: the case of computed tomography fractional flow reserve. J Thorac Imaging 35(Suppl 1):S66–S71PubMed

46.

Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE Consortium. Circ Cardiovasc Imaging 11:e007217PubMed

47.

Tesche C, De Cecco CN, Baumann S, Renker M, McLaurin TW, Duguay TM et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64PubMed

48.

von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Duguay TM, Albrecht MH, van Assen M et al (2018) Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol 29(5):2378–2387PubMed

49.

Tang CX, Wang YN, Zhou F, Schoepf UJ, Assen MV, Stroud RE et al (2019) Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol 116:90–97PubMed

50.

Tesche C, Otani K, De Cecco CN, Coenen A, De Geer J, Kruk M et al (2019) Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE Registry. JACC Cardiovasc Imaging. 13(3):760–770PubMed

51.

Baumann S, Renker M, Schoepf UJ, De Cecco CN, Coenen A, De Geer J et al (2019) Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve—results from the MACHINE registry. Eur J Radiol 119:108657PubMed

52.

Tesche C, Vliegenthart R, Duguay TM, De Cecco CN, Albrecht MH, De Santis D et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol 120:2121–2127PubMed

53.

von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, Albrecht MH, van Assen M, De Santis D et al (2019) Impact of coronary computerized tomography angiography-derived plaque quantification and machine-learning computerized tomography fractional flow reserve on adverse cardiac outcome. Am J Cardiol 124:1340–1348PubMed

54.

Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK (2015) Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal 24:77–89PubMedPubMedCentral

55.

Han D, Lee JH, Rizvi A, Gransar H, Baskaran L, Schulman-Marcus J et al (2018) Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach. J Nucl Cardiol. 25:223–233PubMed

56.

Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH et al (2016) Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J 38(7):500–507PubMedCentral

57.

van Rosendael AR, Maliakal G, Kolli KK, Beecy A, Al’Aref SJ, Dwivedi A et al (2018) Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J Cardiovasc Comput Tomogr. 12:204–209PubMed

58.

van Assen M, Varga-Szemes A, Schoepf UJ, Duguay TM, Hudson HT, Egorova S et al (2019) Automated plaque analysis for the prognostication of major adverse cardiac events. Eur J Radiol 116:76–83PubMed

59.

Johnson KM, Johnson HE, Zhao Y, Dowe DA, Staib LH (2019) Scoring of coronary artery disease characteristics on coronary CT angiograms by using machine learning. Radiology 292:354–362PubMed

60.

Nous FMA, Coenen A, Boersma E, Kim YH, Kruk MBP, Tesche C et al (2019) Comparison of the diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve in patients with versus without diabetes mellitus (from the MACHINE Consortium). Am J Cardiol 123:537–543PubMed

Titel: Ischemia and outcome prediction by cardiac CT based machine learning
verfasst von: Verena Brandt
Tilman Emrich
U. Joseph Schoepf
Danielle M. Dargis
Richard R. Bayer
Carlo N. De Cecco
Christian Tesche
Publikationsdatum: 04.07.2020
Verlag: Springer Netherlands
Erschienen in: The International Journal of Cardiovascular Imaging / Ausgabe 12/2020
Print ISSN: 1569-5794
Elektronische ISSN: 1875-8312
DOI: https://doi.org/10.1007/s10554-020-01929-y

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Ischemia and outcome prediction by cardiac CT based machine learning

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