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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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  • FIGURE 1.
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    FIGURE 1.

    DL prediction of obstructive CAD from upright and supine MPI. A deep convolutional neural network trained from obstructive stenosis correlations by ICA was used to simultaneously estimate probability of obstructive CAD for LAD, LCx, and RCA territories from upright and supine polar MPI maps. Maximum probability was retained as probability of patient disease. FC = fully connected layer; Max-pooling = function that returns maximum value for image patch; ReLU = rectified linear unit.

  • FIGURE 2.
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    FIGURE 2.

    Leave-one-center-out cross-validation. Input stress MPI datasets are divided by center (4 in total). Four folds are built, each containing training sample made up of images from 3 centers and validation sample with images from remaining center. This procedure allows external validation of 4 DL models trained separately in each fold.

  • FIGURE 3.
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    FIGURE 3.

    DL prediction of obstructive CAD from 4 externally validated models with merged data for 4 centers. Per-patient (A) and per-vessel (B) DL predictions of obstructive CAD from upright and supine images (DL, red) are compared with prediction of obstructive CAD by combined upright-supine TPD (cTPD, blue). AUC per center was externally validated using CAD scores from 4 different DL models (1 per center) with each model trained with data from other 3 centers. Red dotted line (bottom) shows overall multicenter AUC. CI = confidence interval; ROC = receiver operating characteristic.

  • FIGURE 4.
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    FIGURE 4.

    Sensitivity for prediction of obstructive CAD. Per-patient DL prediction of obstructive CAD by DL computed from upright and supine MPI (DL, red) had higher sensitivity than prediction by cTPD (blue) and same sensitivity as on-site clinical readers (green). DL cutoff was set to 0.29, and cTPD cutoff was set to 0.62% to exhibit same specificity as normal or probably-normal clinical read. CI = confidence interval.

  • FIGURE 5.
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    FIGURE 5.

    Prediction of obstructive CAD per subpopulation. REFINE-SPECT subpopulations were defined by sex (F, M), obesity (nonobese: body mass index < 30 kg/m2, obese: body mass index ≥ 30 kg/m2), stress imaging activity (low: patients undergoing stress-first/stress-only MPI; standard: patients undergoing rest-first/2-d MPI), and stress test type (exercise, pharmacologic). Red dotted line shows overall multicenter AUC as reported in Figure 3. BMI = body mass index; CI = confidence interval.

  • FIGURE 6.
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    FIGURE 6.

    Prediction of obstructive CAD from upright and supine stress MPI. Short/long axis views, polar maps depicting normalized radiotracer count distribution and perfusion defects (top), and predictions by cTPD and DL (bottom) are shown for 2 patients with obstructive CAD. (A) In 79-y-old man (85% proximal LAD stenosis) quantified with normal cTPD (per-patient cTPD < 3% and per-vessel cTPD < 1%), DL correctly identified LAD disease. Patient had body mass index of 30 kg/m2 and diabetes and underwent exercise stress MPI. (B) In 62-y-old woman (70% mid LAD stenosis, 95% proximal LCX stenosis, and 80% proximal RCA stenosis) with cTPD abnormal for 1 vessel only, DL correctly identified triple-vessel disease. Patient had body mass index of 25 kg/m2, dyslipidemia, and family history of cardiac disease and underwent exercise stress MPI. BMI = body mass index.

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    TABLE 1

    Baseline Characteristics of Studied Population

    CharacteristicOverall, n = 1,160Nonobstructive CAD, n = 442 (38.1%)Obstructive CAD, n = 718 (61.9%)P
    Age (y)64.3 ± 11.562.2 ± 1265.6 ± 11.1<0.0001
    Sex
     Male745 (64.2)232 (52.5)513 (71.5)<0.0001
     Female415 (35.8)210 (47.5)205 (28.5)<0.0001
    Weight (kg)88.5 ± 21.989.4 ± 23.687.9 ± 20.80.278
    Body mass index (kg/m2)30 ± 6.530.8 ± 7.529.5 ± 5.7<0.01
    Diabetes mellitus351 (30.3)121 (27.4)230 (32)0.09
    Hypertension841 (72.5)304 (68.8)537 (74.8)<0.05
    Dyslipidemia780 (67.2)281 (63.6)499 (69.5)<0.05
    Smoking221 (19.1)83 (18.8)138 (19.2)0.85
    Stress test type
     Exercise522 (45)196 (44.3)326 (45.4)0.73
     Exercise + pharmacologic164 (14.1)48 (10.9)116 (16.2)<0.05
     Pharmacologic474 (40.9)198 (44.8)276 (38.4)<0.05
    Imaging protocol
    Stress only48 (4.1)19 (4.3)29 (4)0.83
    Same day stress and rest1,073 (92.5)403 (91.2)670 (93.3)0.18
     Stress-first261 (22.5)84 (19.0)177 (24.7)<0.05
     Rest-first812 (70.0)319 (72.2)493 (68.7)<0.05
    Two-day stress and rest39 (3.4)20 (4.5)19 (2.7)0.09
    • Qualitative data are expressed as numbers followed by percentages in parentheses; continuous data are expressed as mean ± SD.

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    TABLE 2

    Prevalence of Obstructive CAD

    Prevalence
    Obstructive diseaseOverall multicenter, n = 1,160Center 1, n = 362Center 2, n = 191Center 3, n = 275Center 4, n = 332P
    No disease442 (38.1)139 (38.4)72 (37.8)96 (34.9)135 (40.7)0.54
    One-vessel disease321 (27.7)100 (27.6)62 (32.5)79 (28.7)80 (24.1)0.22
    Double-vessel disease240 (20.7)74 (20.4)33 (17.3)64 (23.3)69 (20.8)0.48
    Triple-vessel disease157 (13.5)49 (13.6)24 (11.0)36 (13.1)48 (14.5)0.93
    Per-patient718 (61.9)223 (61.6)119 (62.3)179 (65.1)197 (59.3)0.54
    LAD509 (43.9)163 (45.0)84 (44.0)124 (45.1)138 (41.6)0.78
    LCx384 (33.1)121 (33.4)60 (31.4)92 (33.5)111 (33.4)0.96
    RCA379 (32.7)111 (30.7)56 (29.3)99 (36)113 (34.0)0.35
    Per-vessel (LAD + LCx + RCA)1,272/3,480 (36.6)395/1,086 (36.4)200/573 (34.9)315/825 (38.2)362/996 (36.4)0.65
    • Data are numbers followed by percentages in parentheses.

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    TABLE 3

    Radiotracer Activity for Stress Image Acquisition

    Image protocolInjected activity (MBq)
    Same-day protocols, n = 1,073 (92.5%)
    Stress-first protocol, n = 261 (24.3%)213.1 ± 87.3
    Rest-first protocol, n = 812 (75.7%)103 ± 384
    Two-day protocol, n = 39 (3.4%)682.3 ± 481.44
    Stress-only protocol, n = 48 (4.1%)260.2 ± 486.5
    Overall, n = 1,160804 ± 494
    • Data are mean ± SD.

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Journal of Nuclear Medicine: 60 (5)
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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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