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01-02-2022 | ORIGINAL ARTICLE

A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT

Authors: Valeria Cantoni, PhD, Roberta Green, PhD, Carlo Ricciardi, MSc, Roberta Assante, MD, PhD, Emilia Zampella, MD, PhD, Carmela Nappi, MD, PhD, Valeria Gaudieri, MD, PhD, Teresa Mannarino, MD, Andrea Genova, MD, Giovanni De Simini, MD, Alessia Giordano, MD, Adriana D’Antonio, MD, Wanda Acampa, MD, PhD, Mario Petretta, MD, FAHA, Alberto Cuocolo, MD

Published in: Journal of Nuclear Cardiology | Issue 1/2022

Abstract

Background

We evaluated the performance of conventional (C) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride (CZT)-SPECT in a large cohort of patients with suspected or known coronary artery disease (CAD) and compared the diagnostic accuracy of the two systems using machine learning (ML) algorithms.

Methods and Results

A total of 517 consecutive patients underwent stress myocardial perfusion imaging (MPI) by both C-SPECT and CZT-SPECT. In the overall population, an excellent correlation between stress MPI data and left ventricular (LV) functional parameters measured by C-SPECT and by CZT-SPECT was observed (all P < .001). ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (NN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for k-NN) was greater than that of C-SPECT (88% for RF and 53% for k-NN).

Conclusions

MPI data and LV functional parameters obtained by CZT-SPECT are highly reproducible and provide good correlation with those obtained by C-SPECT. ML approach showed that the accuracy and sensitivity of CZT-SPECT is greater than C-SPECT in detecting CAD.

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Hendel RC, Berman DS, Di Carli MF, Heidenreich PA, Henkin RE, Pellikka PA, et al. ACCF/ASNC/ACR/AHA/ASE/SCCT/ SCMR/SNM 2009 appropriate use criteria for cardiac radionuclide imaging. J Am Coll Cardiol 2009;53:2201-29.PubMed

Anger HO. Scintillation camera with multichannel collimators. J Nucl Med 1964;5:515-31.PubMed

Sharir T, Ben-Haim S, Merzon K, Prochorov V, Dickman D, Ben-Haim S, et al. High-speed myocardial perfusion imaging initial clinical comparison with conventional dual detector anger camera imaging. JACC Cardiovasc Imaging 2008;1:156-63.PubMed

Berman DS, Kang X, Tamarappoo B, Wolak A, Hayes SW, Nakazato R, et al. Stress thallium-201/rest technetium-99 m sequential dual isotope high-speed myocardial perfusion imaging. JACC Cardiovasc Imaging 2009;2:273-82.PubMed

Esteves FP, Raggi P, Folks RD, Keidar Z, Askew JW, Rispler S, et al. Novel solid-state-detector dedicated cardiac camera for fast myocardial perfusion imaging: Multicenter comparison with standard dual detector cameras. J Nucl Cardiol 2009;16:927-34.PubMedPubMedCentral

Slomka PJ, Patton JA, Berman DS, Germano G. Advances in technical aspects of myocardial perfusion SPECT imaging. J Nucl Cardiol 2009;16:255-76.PubMed

Lima R, Peclat T, Soares T, Ferreira C, Souza AC, Camargo G. Comparison of the prognostic value of myocardial perfusion imaging using a CZT-SPECT camera with a conventional anger camera. J Nucl Cardiol 2017;24:245-51.PubMed

Koh HC, Tan G. Data mining applications in healthcare. J Healthc Inf Manag 2005;19:64-72.PubMed

Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine learning in oncology: A clinical appraisal. Cancer Lett 2020;481:55-62.PubMed

10.

Tanaka H, Chikamori T, Hida S, Uchida K, Igarashi Y, Yokoyama T, et al. Comparison of myocardial perfusion imaging between the new high-speed gamma camera and the standard anger camera. Circ J 2013;77:1009-17.PubMed

11.

Hesse B, Tägil K, Cuocolo A, Anagnostopoulos C, Bardiés M, Bax J, et al. EANM/ESC procedural guidelines for myocardial perfusion imaging in nuclear cardiology. Eur J Nucl Med Mol Imaging 2005;32:855-97.PubMed

12.

Acampa W, Petretta M, Daniele S, Del Prete G, Assante R, Zampella E, et al. Incremental prognostic value of stress myocardial perfusion imaging in asymptomatic diabetic patients. Atherosclerosis 2013;227:307-12.PubMed

13.

Gambhir SS, Berman DS, Ziffer J, Nagler M, Sandler M, Patton J, et al. A novel high sensitivity rapid acquisition single photon cardiac imaging camera. J Nucl Med 2009;50:635-43.PubMed

14.

Sharir T, Slomka PJ, Hayes SW, Di Carli MF, Ziffer JA, Martin WH, et al. Multicenter trial of high-speed versus conventional single-photon emission computed tomography imaging: quantitative results of myocardial perfusion and left ventricular function. J Am Coll Cardiol 2010;55:1965-74.PubMed

15.

Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging 1982;1:113-22.PubMed

16.

Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr 1984;8:306-16.PubMed

17.

Germano G, Kavanagh PB, Waechter P, Areeda J, Van Kriekinge S, Sharir T, et al. A new algorithm for the quantitation of myocardial perfusion SPECT. I: Technical principles and reproducibility. J Nucl Med 2000;41:712-9.PubMed

18.

Slomka PJ, Nishina H, Berman DS, Kang X, Friedman JD, Hayes SW, et al. Automatic quantification of myocardial perfusion stress-rest change: A new measure of ischemia. J Nucl Med 2004;45:183-91.PubMed

19.

Acampa W, Petretta M, Evangelista L, Daniele S, Xhoxhi E, De Rimini ML, et al. Myocardial perfusion imaging and risk classification for coronary heart disease in diabetic patients. The IDIS study: A prospective, multicenter trial. Eur J Nucl Med Mol Imaging 2012;39:387-95.PubMed

20.

Acampa W, Petretta M, Florimonte L, di Santolo MS, Cuocolo A. Sestamibi SPECT in the detection of myocardial viability in patients with chronic ischemic left ventricular dysfunction: Comparison between visual and quantitative analysis. J Nucl Cardiol 2000;7:406-13.PubMed

21.

de Vet HC, Terwee CB, Knol DL, Bouter LM. When to use agreement versus reliability measures. J Clin Epidemiol 2006;59:1033-9.PubMed

22.

Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.PubMed

23.

Ricciardi C, Valente AS, Edmund K, Cantoni V, Green R, Fiorillo A, et al. Linear discriminant analysis and principal component analysis to predict coronary artery disease [published online ahead of print, 2020 Jan 23]. Health Informatics J 2020;1460458219899210.

24.

Breiman L. Random forests. Mach Learn 2001;45:5-32.

25.

Aha DW, Kibler D, Albert MK. Instance-based learning algorithms. Mach Learn 1991;6:37-66.

26.

Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc Fourteenth Int Joint Conf Artif Intell 1995;14:1137-45.

27.

Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373-9.PubMed

28.

Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2014 update: A report from the American Heart Association. Circulation 2014;129:e28-292.PubMed

29.

Duvall WL, Rai M, Ahlberg AW, O’Sullivan DM, Henzlova MJ. A multi-center assessment of the temporal trends in myocardial perfusion imaging. J Nucl Cardiol 2015;22:539-51.PubMed

30.

Imbert L, Poussier S, Franken PR, Songy B, Verger A, Morel O, et al. Compared performance of high-sensitivity cameras dedicated to myocardial perfusion SPECT: A comprehensive analysis of phantom and human images. J Nucl Med 2012;53:1897-903.PubMed

31.

Verger A, Djaballah W, Fourquet N, Rouzet F, Koehl G, Imbertet L, et al. Comparison between stress myocardial perfusion SPECT recorded with cadmium-zinc-telluride and Anger cameras in various study protocols. Eur J Nucl Med Mol Imaging 2013;40:331-40.PubMed

32.

Mouden M, Timmer JR, Ottervanger JP, Reiffers S, Oostdijk AH, Knollema S, et al. Impact of a new ultrafast CZT SPECT camera for myocardial perfusion imaging: fewer equivocal results and lower radiation dose. Eur J Nucl Med Mol Imaging 2012;39:1048-55.PubMed

33.

Duvall WL, Sweeny JM, Croft LB, Barghash MH, Kulkarni NK, Guma KA, et al. Comparison of high efficiency CZT SPECT MPI to coronary angiography. J Nucl Cardiol 2011;18:595-604.PubMed

34.

Zoccarato O, Lizio D, Savi A, Indovina L, Scabbio C, Leva L, et al. Comparative analysis of cadmium-zincum-telluride cameras dedicated to myocardial perfusion SPECT: A phantom study. J Nucl Cardiol 2016;23:885-93.PubMed

35.

Slomka PJ, Berman DS, Germano G. Quantification of serial changes in myocardial perfusion. J Nucl Med 2004;45:1978-80.PubMed

36.

Berman DS, Kang X, Gransar H, Gerlach J, Friedman JD, Hayeset SW, et al. Quantitative assessment of myocardial perfusion abnormality on SPECT myocardial perfusion imaging is more reproducible than expert visual analysis. J Nucl Cardiol 2009;16:45-53.PubMedPubMedCentral

37.

Shaw LJ, Berman DS, Maron DJ, Mancini GBJ, Hayes SW, Hartigan PM, et al. Optimal medical therapy with or without percutaneous coronary intervention to reduce ischemic burden: Results from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial nuclear substudy. Circulation 2008;117:1283-91.PubMed

38.

Neill J, Prvulovich EM, Fish MB, Berman DS, Slomka PJ, Sharir T, et al. Initial multicenter experience of high-speed myocardial perfusion imaging: comparison between high-speed and conventional single-photon emission computed tomography with angiographic validation. Eur J Nucl Med Mol Imaging 2013;40:1084-94.PubMed

39.

Arsanjani R, Xu Y, Dey D, Vahistha V, Shalev A, Nakanishi R, et al. Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population. J Nucl Cardiol 2013;20:553-62.PubMedPubMedCentral

40.

Mannarino T, Assante R, Ricciardi C, Zampella E, Nappi C, Gaudieri V, et al. Head-to-head comparison of diagnostic accuracy of stress-only myocardial perfusion imaging with conventional and cadmium-zinc telluride single-photon emission computed tomography in women with suspected coronary artery disease. J Nucl Cardiol 2019. https://doi.org/10.1007/s12350-019-01789-7.CrossRefPubMed

41.

Ricciardi C, Cantoni V, Improta G, Iuppariello L, Latessa I, Cesarelli M, et al. Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center. Comput Methods Programs Biomed 2020;189:105343. https://doi.org/10.1016/j.cmpb.2020.105343.CrossRefPubMed

42.

Ricciardi C, Edmunds KJ, Recenti M, Sigurdsson S, Gudnason V, Carraro U, et al. Assessing cardiovascular risks from a mid-thigh CT image: A tree-based machine learning approach using radiodensitometric distributions. Sci Rep 2020;10:2863.PubMedPubMedCentral

43.

Acampa W, Buechel RR, Gimelli A. Low dose in nuclear cardiology: State of the art in the era of new cadmium-zinc-telluride cameras. Eur Heart J Cardiovasc Imaging 2016;17:591-5.PubMed

44.

Kottner J, Audigé L, Brorson S, Donner A, Gajewski BJ, Hróbjartsson A, et al. Guidelines for reporting reliability and agreement studies (GRRAS) were proposed. J Clin Epidemiol 2011;64:96-106.PubMed

45.

Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 2003;22:85-93.PubMed

Title: A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT
Authors: Valeria Cantoni, PhD
Roberta Green, PhD
Carlo Ricciardi, MSc
Roberta Assante, MD, PhD
Emilia Zampella, MD, PhD
Carmela Nappi, MD, PhD
Valeria Gaudieri, MD, PhD
Teresa Mannarino, MD
Andrea Genova, MD
Giovanni De Simini, MD
Alessia Giordano, MD
Adriana D’Antonio, MD
Wanda Acampa, MD, PhD
Mario Petretta, MD, FAHA
Alberto Cuocolo, MD
Publication date: 01-02-2022
Publisher: Springer International Publishing
Published in: Journal of Nuclear Cardiology / Issue 1/2022
Print ISSN: 1071-3581
Electronic ISSN: 1532-6551
DOI: https://doi.org/10.1007/s12350-020-02187-0

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A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT

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Conclusions

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