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Research ArticleCardiology

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Julian Betancur, Lien-Hsin Hu, Frederic Commandeur, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Joanna X. Liang, Balaji K. Tamarappoo, Damini Dey, Daniel S. Berman and Piotr J. Slomka
Journal of Nuclear Medicine May 2019, 60 (5) 664-670; DOI: https://doi.org/10.2967/jnumed.118.213538
Julian Betancur
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Lien-Hsin Hu
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Frederic Commandeur
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Tali Sharir
2Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
3Ben Gurion University of the Negev, Beer Sheba, Israel
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Andrew J. Einstein
4Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York–Presbyterian Hospital, New York, New York
5Department of Radiology, Columbia University Irving Medical Center and New York–Presbyterian Hospital, New York, New York
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Mathews B. Fish
6Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon
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Terrence D. Ruddy
7Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
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Philipp A. Kaufmann
8Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
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Albert J. Sinusas
9Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
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Edward J. Miller
9Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
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Timothy M. Bateman
10Cardiovascular Imaging Technologies LLC, Kansas City, Missouri; and
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Sharmila Dorbala
11Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
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Marcelo Di Carli
11Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts
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Guido Germano
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Yuka Otaki
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Joanna X. Liang
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Balaji K. Tamarappoo
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Damini Dey
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Daniel S. Berman
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Piotr J. Slomka
1Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
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Abstract

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.

  • obstructive coronary artery disease
  • SPECT myocardial perfusion imaging
  • deep learning
  • convolutional neural network
  • total perfusion deficit

Footnotes

  • Published online Sep. 27, 2018.

  • © 2019 by the Society of Nuclear Medicine and Molecular Imaging.
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Journal of Nuclear Medicine: 60 (5)
Journal of Nuclear Medicine
Vol. 60, Issue 5
May 1, 2019
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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
Julian Betancur, Lien-Hsin Hu, Frederic Commandeur, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Joanna X. Liang, Balaji K. Tamarappoo, Damini Dey, Daniel S. Berman, Piotr J. Slomka
Journal of Nuclear Medicine May 2019, 60 (5) 664-670; DOI: 10.2967/jnumed.118.213538

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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
Julian Betancur, Lien-Hsin Hu, Frederic Commandeur, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Joanna X. Liang, Balaji K. Tamarappoo, Damini Dey, Daniel S. Berman, Piotr J. Slomka
Journal of Nuclear Medicine May 2019, 60 (5) 664-670; DOI: 10.2967/jnumed.118.213538
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Keywords

  • obstructive coronary artery disease
  • SPECT myocardial perfusion imaging
  • deep learning
  • convolutional neural network
  • total perfusion deficit
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