In Journal of nuclear medicine : official publication, Society of Nuclear Medicine
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.
Miller Robert Jh, Pieszko Konrad, Shanbhag Aakash, Feher Attila, Lemley Mark, Killekar Aditya, Kavanagh Paul B, Van Kriekinge Serge D, Liang Joanna X, Huang Cathleen, Miller Edward J, Bateman Timothy, Berman Daniel S, Dey Damini, Slomka Piotr J
Cardiology (basic/technical), Cardiology (clinical), artificial intelligence, coronary artery calcification, deep learning, myocardial perfusion imaging, risk stratification