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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

  • ORIGINAL ARTICLE
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Journal of Nuclear Cardiology Aims and scope

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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Abbreviations

SPECT:

Single-photon emission computed tomography

MPI:

Myocardial perfusion imaging

CAD:

Coronary artery disease

C:

Conventional

CZT:

Cadmium-zinc-telluride

ML:

Machine learning

ECG:

Electrocardiography

LV:

Left ventricular

EF:

Ejection fraction

TPD:

Total perfusion defect

SSS:

Summed stress score

ICC:

Intraclass correlation coefficient

RO:

Receiver operating characteristic

RF:

Random forest

NN:

Nearest neighbor

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Disclosure

V Cantoni, R. Green, C. Ricciardi, R. Assante, E. Zampella, C. Nappi, V. Gaudieri, T. Mannarino, A. Genova, G. De Simini, A. Giordano, A. D’Antonio, W. Acampa, M. Petretta, and A. Cuocolo declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy

    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 & Alberto Cuocolo MD

  2. Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy

    Wanda Acampa MD, PhD

  3. Department of Translational Medical Sciences, University Federico II, Naples, Italy

    Mario Petretta MD, FAHA

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  1. Valeria Cantoni PhD
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Correspondence to Alberto Cuocolo MD.

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Cantoni, V., Green, R., Ricciardi, C. et al. A machine learning-based approach to directly compare the diagnostic accuracy of myocardial perfusion imaging by conventional and cadmium-zinc telluride SPECT. J. Nucl. Cardiol. 29, 46–55 (2022). https://doi.org/10.1007/s12350-020-02187-0

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