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OtherAI/Advanced Image Analysis

Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events

Robert JH Miller, Konrad Pieszko, Aakash Shanbhag, Attila Feher, Mark Lemley, Aditya Killekar, Paul B. Kavanagh, Serge D. Van Kriekinge, Joanna X. Liang, Cathleen Huang, Edward J. Miller, Timothy Bateman, Daniel S. Berman, Damini Dey and Piotr J. Slomka
Journal of Nuclear Medicine October 2022, jnumed.122.264423; DOI: https://doi.org/10.2967/jnumed.122.264423
Robert JH Miller
1 Cedars-Sinai Medical Center, United States;
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Konrad Pieszko
1 Cedars-Sinai Medical Center, United States;
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Aakash Shanbhag
1 Cedars-Sinai Medical Center, United States;
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Attila Feher
2 Yale University School of Medicine, United States;
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Mark Lemley
1 Cedars-Sinai Medical Center, United States;
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Aditya Killekar
1 Cedars-Sinai Medical Center, United States;
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Paul B. Kavanagh
1 Cedars-Sinai Medical Center, United States;
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Serge D. Van Kriekinge
1 Cedars-Sinai Medical Center, United States;
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Joanna X. Liang
1 Cedars-Sinai Medical Center, United States;
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Cathleen Huang
1 Cedars-Sinai Medical Center, United States;
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Edward J. Miller
3 Yale University;
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Timothy Bateman
4 Cardiovascular Imaging Technologies LLC
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Daniel S. Berman
1 Cedars-Sinai Medical Center, United States;
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Damini Dey
1 Cedars-Sinai Medical Center, United States;
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Piotr J. Slomka
1 Cedars-Sinai Medical Center, United States;
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Abstract

Background: Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease (n = 2271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0; 1-100; 101-400; >400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index (NRI). Results: In the external testing population, DL CAC was 0 in 908(40.0%), 1-100 in 596(26.2%), 100-400 in 354(15.6%), and >400 in 413(18.2%) patients. Agreement in CAC category by DL CTAC and expert annotation was excellent (linear weighted Kappa 0.80), but DL CAC was obtained automatically in <2 seconds compared to ~2.5-minutes for expert CAC. DL CAC category was an independent risk for MACE with hazard ratios in comparison to CAC of zero: CAC 1-100 (2.20, 95% CI 1.54 – 3.14, p<0.001), CAC 101-400 (4.58, 95% CI 3.23 – 6.48, p<0.001), and CAC > 400 (5.92, 95% CI 4.27 – 8.22, p<0.001). Overall NRI was 0.494 for DL CAC, which was similar to expert annotated CAC (0.503). Conclusion: DL CAC from SPECT/CT attenuation maps has good agreement with expert CAC annotations and provides similar risk stratification but can be obtained automatically. DL CAC scores improved classification of a significant proportion of patients as compared to myocardial perfusion SPECT alone.

  • Cardiology (basic/technical)
  • Cardiology (clinical)
  • artificial intelligence
  • coronary artery calcification
  • deep learning
  • myocardial perfusion imaging
  • risk stratification
  • Copyright © 2022 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

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Journal of Nuclear Medicine: 64 (3)
Journal of Nuclear Medicine
Vol. 64, Issue 3
March 1, 2023
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Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events
Robert JH Miller, Konrad Pieszko, Aakash Shanbhag, Attila Feher, Mark Lemley, Aditya Killekar, Paul B. Kavanagh, Serge D. Van Kriekinge, Joanna X. Liang, Cathleen Huang, Edward J. Miller, Timothy Bateman, Daniel S. Berman, Damini Dey, Piotr J. Slomka
Journal of Nuclear Medicine Oct 2022, jnumed.122.264423; DOI: 10.2967/jnumed.122.264423

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Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events
Robert JH Miller, Konrad Pieszko, Aakash Shanbhag, Attila Feher, Mark Lemley, Aditya Killekar, Paul B. Kavanagh, Serge D. Van Kriekinge, Joanna X. Liang, Cathleen Huang, Edward J. Miller, Timothy Bateman, Daniel S. Berman, Damini Dey, Piotr J. Slomka
Journal of Nuclear Medicine Oct 2022, jnumed.122.264423; DOI: 10.2967/jnumed.122.264423
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Keywords

  • Cardiology (basic/technical)
  • Cardiology (clinical)
  • artificial intelligence
  • coronary artery calcification
  • deep learning
  • myocardial perfusion imaging
  • risk stratification
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