nach oben

Erschienen in:

19.01.2018 | Cardiac

Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study

verfasst von: Damini Dey, Sara Gaur, Kristian A. Ovrehus, Piotr J. Slomka, Julian Betancur, Markus Goeller, Michaela M. Hell, Heidi Gransar, Daniel S. Berman, Stephan Achenbach, Hans Erik Botker, Jesper Moller Jensen, Jens Flensted Lassen, Bjarne Linde Norgaard

Erschienen in: European Radiology | Ausgabe 6/2018

Einloggen, um Zugang zu erhalten

Abstract

Objectives

We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA).

Methods

In a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation.

Results

Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006.

Conclusions

Integrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD.

Key Points

• Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures

• Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach

• Contrast density difference had the highest information gain to identify lesion-specific ischaemia

Budoff MJ, Dowe D, Jollis JG et al (2008) Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 52:1724–1732CrossRefPubMed

Hausleiter J, Meyer T, Hadamitzky M et al (2007) Non-invasive coronary computed tomographic angiography for patients with suspected coronary artery disease: the Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial. Eur Heart J 28:3034–3041CrossRefPubMed

Achenbach S, Ropers U, Kuettner A et al (2008) Randomized comparison of 64-slice single- and dual-source computed tomography coronary angiography for the detection of coronary artery disease. J Am Coll Cardiol Cardiovascular Imaging 1:177–186CrossRef

Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359:2324–2336CrossRefPubMed

Meijboom WB, van Mieghem CAG, Mollet NR et al (2007) 64-slice computed tomography coronary angiography in patients with high, intermediate, or low pretest probability of significant coronary artery disease. J Am Coll Cardiol 50:1469–1475CrossRefPubMed

Achenbach S, Moselewski F, Ropers D et al (2004) Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound. Circulation 109:14–17CrossRefPubMed

Achenbach S, Ropers D, Hoffmann U et al (2004) Assessment of coronary remodeling in stenotic and nonstenotic coronary atherosclerotic lesions by multidetector spiral computed tomography. J Am Coll Cardiol 43:842–847CrossRefPubMed

Leber AW, Becker A, Knez A et al (2006) Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound. J Am Coll Cardiol 47:672–677CrossRefPubMed

Leber AW, Knez A, Becker A et al (2004) Accuracy of multidetector spiral computed tomography in identifying and differentiating the composition of coronary atherosclerotic plaques: a comparative study with intracoronary ultrasound. J Am Coll Cardiol 43:1241–1247CrossRefPubMed

10.

Pundziute G, Schuijf JD, Jukema JW et al (2008) Evaluation of plaque characteristics in acute coronary syndromes: non-invasive assessment with multi-slice computed tomography and invasive evaluation with intravascular ultrasound radiofrequency data analysis. Eur Heart J 29:2373–2381CrossRefPubMed

11.

Tonino PA, De Bruyne B, Pijls NH et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224CrossRefPubMed

12.

Pijls NH, Fearon WF, Tonino PA et al (2010) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention in patients with multivessel coronary artery disease: 2-year follow-up of the FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) study. J Am Coll Cardiol 56:177–184CrossRefPubMed

13.

Park HB, Heo R, Ó Hartaigh B et al (2015) Atherosclerotic plaque characteristics by CT angiography identify coronary lesions that cause ischemia: a direct comparison to fractional flow reserve. JACC Cardiovasc Imaging 8:1–10CrossRefPubMedPubMedCentral

14.

Gaur S, Øvrehus KA, Dey D et al (2016) Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions. Eur Heart J. https://doi.org/10.1093/eurheartj/ehv690

15.

Deo RC (2015) Machine learning in medicine. Circulation 132:1920–1930CrossRefPubMedPubMedCentral

16.

Waljee AK, Higgins PD (2010) Machine learning in medicine: a primer for physicians. Am J Gastroenterol 105:1224–1226CrossRefPubMed

17.

Arsanjani R, Xu Y, Dey D et al (2013) Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol 20:553–562CrossRefPubMedPubMedCentral

18.

Kang D, Slomka PJ, Nakazato R et al (2013) Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography. Med Phys 40:041912CrossRefPubMed

19.

Motwani M, Dey D, Berman DS et al (2017) 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 38:500–507PubMed

20.

Ambale-Venkatesh B, Yang X, Wu CO et al (2017) Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res 9:311312

21.

Norgaard BL, Leipsic J, Gaur S et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63:1145–1155CrossRefPubMed

22.

Montalescot G, Sechtem U, Achenbach S et al (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34:2949–3003CrossRefPubMed

23.

Leipsic J, Abbara S, Achenbach S et al (2014) SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr 8:342–358CrossRefPubMed

24.

Dey D, Cheng VY, Slomka PJ et al (2009) Automated three-dimensional quantification of non-calcified and calcified coronary plaque from coronary CT angiography. J Cardiovasc Comput Tomogr 3:372–382

25.

Dey D, Schepis T, Marwan M, Slomka PJ, Berman DS, Achenbach S (2010) Automated three-dimensional quantification of non-calcified coronary plaque from coronary CT angiography: comparison with intravascular ultrasound. Radiology 257:516–522CrossRefPubMed

26.

Motoyama S, Sarai M, Harigaya H et al (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54:49–57CrossRefPubMed

27.

Hell MM, Dey D, Marwan M, Achenbach S, Schmid J, Schuhbaeck A (2015) Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography. Eur J Radiol 84:1502–1508CrossRefPubMed

28.

Schuhbaeck A, Dey D, Otaki Y et al (2013) Interscan reproducibility of quantitative coronary plaque volume and composition from CT coronary angiography using an automated method. J Am Coll Cardiol. https://doi.org/10.1016/S0735-1097(1013)61040-61042

29.

Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11:10–18CrossRef

30.

Arsanjani R, Dey D, Khachatryan T et al (2015) Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol 22:877–884CrossRefPubMed

31.

Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15:1437–1447CrossRef

32.

Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous valued attributes for classification learning. Thirteenth International Joint Conference on Artificial Intelligence, 1022-1027

33.

Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28:337–407CrossRef

34.

Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington

35.

Molinaro AM, Simon R, Pfeiffer RM (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics 21:3301–3307CrossRefPubMed

36.

DeLong ER, DeLong DM, Clarke-Pearson DL (2007) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRef

37.

Pencina MJ, D'Agostino RB, Pencina KM, Janssens ACJW, Greenland P (2012) Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 176:473–481CrossRefPubMedPubMedCentral

38.

Park H-B, Heo R, ó Hartaigh B et al (2015) Atherosclerotic plaque characteristics by CT angiography identify coronary lesions that cause ischemia: a direct comparison to fractional flow reserve. JACC Cardiovasc Imaging 8:1–10CrossRefPubMedPubMedCentral

39.

Diaz Zamudio M, Dey D, Schuhbaeck A et al (2015) Automated quantitative plaque burden from coronary CT angiography noninvasively predicts hemodynamic significance by fractional flow reserve in intermediate coronary lesions. Radiology 276:408–415

40.

Ko BS, Wong DT, Cameron JD et al (2015) The ASLA score: a CT angiographic index to predict functionally significant coronary stenoses in lesions with intermediate severity-diagnostic accuracy. Radiology 276:91–101CrossRefPubMed

41.

Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245

42.

Koo BK, Erglis A, Doh JH et al (2011) Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol 58:1989–1997CrossRefPubMed

43.

Coenen A, Lubbers MM, Kurata A et al (2015) Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology 274:674–683CrossRefPubMed

44.

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

45.

Ko BS, Cameron JD, Leung M et al (2012) Combined CT coronary angiography and stress myocardial perfusion imaging for hemodynamically significant stenoses in patients with suspected coronary artery disease: a comparison with fractional flow reserve. JACC Cardiovasc Imaging 5:1097–1111CrossRefPubMed

46.

Obermeyer Z, Emanuel EJ (2016) Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 375:1216–1219CrossRefPubMedPubMedCentral

Titel: Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study
verfasst von: Damini Dey
Sara Gaur
Kristian A. Ovrehus
Piotr J. Slomka
Julian Betancur
Markus Goeller
Michaela M. Hell
Heidi Gransar
Daniel S. Berman
Stephan Achenbach
Hans Erik Botker
Jesper Moller Jensen
Jens Flensted Lassen
Bjarne Linde Norgaard
Publikationsdatum: 19.01.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2018
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5223-z

Neu im Fachgebiet Radiologie

18.04.2024 | Pädiatrische Notfallmedizin | Nachrichten

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Update Radiologie

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 6/2018

A simple method for bone age assessment: the capitohamate planimetry

Computed tomography findings in ABO-incompatible living donor liver transplantation recipients with biliary strictures

Myocardial perfusion in patients with suspected coronary artery disease: comparison between 320-MDCT and rubidium-82 PET

Effect of intra-articular injection of intermediate-weight hyaluronic acid on hip and knee cartilage: in-vivo evaluation using T2 mapping

Impact of noncardiac findings in patients undergoing CT coronary angiography: a substudy of the Scottish computed tomography of the heart (SCOT-HEART) trial

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

Neu im Fachgebiet Radiologie

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Studie bestätigt Potenzial von KI in der Radiologie

Update Radiologie